diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index e0de92c..af1f6b6 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -27,6 +27,7 @@ repos:
     rev: "v1.10.0"
     hooks:
       - id: mypy
+        additional_dependencies: ["types-toml"]
   - repo: https://github.com/igorshubovych/markdownlint-cli
     rev: v0.40.0
     hooks:
diff --git a/bubble_analyser/__main__.py b/bubble_analyser/__main__.py
index 495fd02..fd29cb7 100644
--- a/bubble_analyser/__main__.py
+++ b/bubble_analyser/__main__.py
@@ -1 +1,6 @@
 """The entry point for the Bubble Analyser program."""
+
+from .default import default
+
+if __name__ == "__main__":
+    default()
diff --git a/bubble_analyser/background_subtraction_threshold.py b/bubble_analyser/background_subtraction_threshold.py
new file mode 100644
index 0000000..12b84ac
--- /dev/null
+++ b/bubble_analyser/background_subtraction_threshold.py
@@ -0,0 +1,107 @@
+"""Background Subtraction and Thresholding: Isolate objects from backgrounds.
+
+This module provides tools for image preprocessing including grayscale conversion,
+background subtraction, and thresholding. It is designed to handle images where objects
+of interest need to be isolated from their backgrounds for further analysis.
+
+Functions:
+    convert_grayscale(image): Converts a color image to grayscale.
+
+    background_subtraction(target_img, background_img): Subtracts the background image
+    from the target image to highlight differences.
+
+    threshold(difference_img, threshold_value): Applies a binary threshold to an image
+    to create a binary mask.
+
+    background_subtraction_threshold(target_img, background_img, threshold_value):
+    Combines background subtraction and thresholding to isolate objects of interest in
+    an image.
+
+These functions are used to preprocess images for applications such as object detection,
+where isolating the changes between images or from a background is necessary. Each
+function is designed to be modular, allowing them to be used independently or in
+sequence depending on the requirements of the task.
+"""
+
+from typing import cast
+
+import cv2
+import numpy as np
+from numpy import typing as npt
+
+
+def convert_grayscale(image: npt.NDArray[np.int_]) -> npt.NDArray[np.int_]:
+    """Converts an image to grayscale.
+
+    Args:
+        image (npt.NDArray): The input image to be converted.
+
+    Returns:
+        npt.NDArray: The converted image in grayscale.
+    """
+    if len(image.shape) == 3:
+        image = cast(npt.NDArray[np.int_], cv2.cvtColor(image, cv2.COLOR_BGR2GRAY))
+    return image
+
+
+def background_subtraction(
+    target_img: npt.NDArray[np.int_], background_img: npt.NDArray[np.int_]
+) -> npt.NDArray[np.int_]:
+    """Performs background subtraction on two images.
+
+    Args:
+        target_img (npt.NDArray): The target image where the objects of interest are
+        located.
+        background_img (npt.NDArray): The background image (without object of interest).
+
+    Returns:
+        npt.NDArray: The difference image after background subtraction.
+    """
+    difference_img = cv2.absdiff(target_img, background_img)
+    return cast(npt.NDArray[np.int_], difference_img)
+
+
+def threshold(
+    difference_img: npt.NDArray[np.int_], threshold_value: float
+) -> npt.NDArray[np.bool_]:
+    """Applies a binary threshold to the given difference image.
+
+    Args:
+        difference_img (npt.NDArray): The input difference image to be thresholded.
+        threshold_value (int): The threshold value to apply to the difference image.
+
+    Returns:
+        npt.NDArray: The thresholded image.
+    """
+    _, thresholded_img = cv2.threshold(
+        difference_img, threshold_value, 255, cv2.THRESH_BINARY
+    )
+    return cast(npt.NDArray[np.bool_], thresholded_img)
+
+
+def background_subtraction_threshold(
+    target_img: npt.NDArray[np.int_],
+    background_img: npt.NDArray[np.int_],
+    threshold_value: float,
+) -> npt.NDArray[np.bool_]:
+    """Perform background subtraction and apply thresholding.
+
+    Args:
+        target_img: The target image where the objects of interest are located.
+        background_img: The background image (without objects of interest).
+        threshold_value: The threshold value to apply after background subtraction.
+
+    Returns:
+        A binary image with the objects of interest isolated.
+    """
+    # Ensure both images are in grayscale
+    target_img = convert_grayscale(target_img)
+    background_img = convert_grayscale(background_img)
+
+    # Subtract the background image from the target image
+    difference_img = background_subtraction(target_img, background_img)
+
+    # Apply a threshold to the difference image
+    thresholded_img = threshold(difference_img, threshold_value)
+
+    return thresholded_img
diff --git a/bubble_analyser/calculate_circle_properties.py b/bubble_analyser/calculate_circle_properties.py
new file mode 100644
index 0000000..9272c0a
--- /dev/null
+++ b/bubble_analyser/calculate_circle_properties.py
@@ -0,0 +1,83 @@
+"""Calculate Circle Properties.
+
+This module contains functions for calculating geometric properties of regions
+identified in an image. It is particularly focused on regions that are labeled in terms
+of their circularity attributes.
+
+The `calculate_circle_properties` function evaluates the geometric features of labeled
+regions within an image, which have been identified as separate entities, often through
+a segmentation process. It measures various properties related to the shape and size of
+the regions, adjusted to real-world dimensions using a provided pixel-to-centimeter
+conversion ratio.
+
+Function:
+    calculate_circle_properties(labels, px2cm): Computes area, equivalent diameter,
+    eccentricity, solidity, and circularity for each labeled region.
+
+Each computed property is defined as follows:
+- Area: Total area of the region converted from pixels to square centimeters.
+- Equivalent diameter: Diameter of a circle with the equivalent area as the region,
+  provided in centimeters.
+- Eccentricity: Measure of the deviation of the region from a perfect circle, where
+  0 indicates a perfect circle and values closer to 1 indicate elongated shapes.
+- Solidity: Ratio of the region's area to the area of its convex hull, indicating
+  the compactness of the shape.
+- Circularity: A value that describes how closely the shape of the region approaches
+  that of a perfect circle, calculated from the area and the perimeter.
+
+The function returns a list of dictionaries, with each dictionary holding the properties
+for a specific region, facilitating easy access and manipulation of these metrics in
+subsequent analysis or reporting stages.
+"""
+
+import numpy as np
+from numpy import typing as npt
+from skimage import measure
+
+
+def calculate_circle_properties(
+    labels: npt.NDArray[np.int_], px2cm: float
+) -> list[dict[str, float]]:
+    """Calculate geometric properties of labeled regions in an image.
+
+    This function computes various properties that describe the "circularity" of regions
+    within the labeled image, such as area, equivalent diameter, eccentricity, solidity,
+    and circularity. These properties are calculated in centimeters based on the
+    provided pixel-to-centimeter ratio.
+
+    Args:
+        labels: A labeled image where each distinct region (or "circle") is represented
+        by unique labels.
+        px2cm: The ratio of centimeters per pixel, used to convert measurements from
+        pixels to centimeters.
+
+    Returns:
+        A list of dictionaries, each containing the following properties for a region:
+        - area: The area of the region in square centimeters.
+        - equivalent_diameter: The diameter of a circle with the same area as the region
+        , in centimeters.
+        - eccentricity: The eccentricity of the ellipse that has the same second-moments
+        as the region.
+        - solidity: The proportion of the pixels in the convex hull that are also in the
+        region.
+        - circularity: A measure of how close the shape is to a perfect circle,
+        calculated using the perimeter and area.
+    """
+    properties = measure.regionprops(labels)
+    circle_properties = []
+    for prop in properties:
+        area = prop.area * (px2cm**2)
+        equivalent_diameter = prop.equivalent_diameter * px2cm
+        eccentricity = prop.eccentricity
+        solidity = prop.solidity
+        circularity = (4 * np.pi * area) / (prop.perimeter * px2cm) ** 2
+        circle_properties.append(
+            {
+                "area": area,
+                "equivalent_diameter": equivalent_diameter,
+                "eccentricity": eccentricity,
+                "solidity": solidity,
+                "circularity": circularity,
+            }
+        )
+    return circle_properties
diff --git a/bubble_analyser/calculate_px2cm.py b/bubble_analyser/calculate_px2cm.py
new file mode 100644
index 0000000..f0ad7fa
--- /dev/null
+++ b/bubble_analyser/calculate_px2cm.py
@@ -0,0 +1,203 @@
+"""Bubble Analyser: Image Processing for Circular Feature Detection.
+
+This module provides a suite of tools for image manipulation and measurement calibration
+using computer vision techniques. It includes functions to resize images, draw on images
+interactively, and calculate real-world measurements from pixels.
+
+The functions in this module utilize OpenCV and NumPy to perform tasks such as image
+resizing, interactive line drawing for measurement marking, pixel distance calculations,
+and conversionfrom pixel measurements to real-world units (e.g., centimeters). These
+capabilities are particularly useful in applications where precision in spatial
+measurements is required, such as in quality control, materials science, and medical
+imaging.
+
+Key Functions:
+- resize_to_target_width(image, target_width): Resizes an image to a specified target
+  width while maintaining the aspect ratio.
+- draw_line(event, x, y, flags, param): A callback function that allows interactive line
+  drawing on an image displayed in an OpenCV window.
+- get_pixel_distance(img): Displays an image and allows the user to draw a line, then
+  calculates the pixel distance between the endpoints of the line.
+- get_cm_per_pixel(pixel_distance, scale_percent, img_resample): Calculates the
+  conversion ratio from pixels to centimeters, taking into account any image resizing
+  that has been applied.
+- calculate_px2cm(image_path, img_resample): Orchestrates the process of loading an
+  image, resizing it, allowing the user to mark a measurement, and calculating a
+  pixel-to-centimeter conversion factor.
+
+Each function is designed to be modular, allowing for flexible integration into broader
+image processing and analysis workflows. The module facilitates the extraction of
+quantitative data from images, which can be critical for applications requiring detailed
+spatial analysis.
+"""
+
+from pathlib import Path
+from typing import cast
+
+import cv2
+import numpy as np
+import numpy.typing as npt
+
+from .image_preprocess import load_image
+
+
+def resize_to_target_width(
+    image: npt.NDArray[np.int_], target_width: int = 1000
+) -> tuple[npt.NDArray[np.int_], float]:
+    """Resizes an image to a specified target width while maintaining the aspect ratio.
+
+    Args:
+        image (npt.npt.NDArray[np.int_]): The input image to be resized.
+        target_width (int, optional): The desired width of the resized image. Defaults
+        to 1000.
+
+    Returns:
+        npt.npt.NDArray[np.int_]: The resized image.
+        scale_percent: The scaling percentage applied to the image during resizing.
+    """
+    # Scale down the image to a width of 1000 pixels, keeping the aspect ratio the same
+    scale_percent: float = (
+        target_width / image.shape[1]
+    )  # Calculate the scale percent to make width 1000
+    width: int = target_width  # Set the new width to 1000 pixels
+    height: int = int(
+        image.shape[0] * scale_percent
+    )  # Adjust the height to maintain the aspect ratio
+    dim: tuple[int, int] = (width, height)  # Define the new dimensions
+    image_resized = cv2.resize(
+        image, dim, interpolation=cv2.INTER_AREA
+    )  # Resize the image
+
+    return cast(npt.NDArray[np.int_], image_resized), scale_percent
+
+
+def draw_line(event: int, x: int, y: int, flags: int, param: dict[str, object]) -> None:
+    """Callback function to draw a line on the image.
+
+    This function is used as a mouse callback to allow the user to draw a line on the
+    image.
+
+    Args:
+        event: The type of mouse event (e.g., left button down, mouse move, left button
+        up).
+        x: The x-coordinate of the mouse event.
+        y: The y-coordinate of the mouse event.
+        flags: Any relevant flags passed by OpenCV.
+        param: A dictionary containing reference points and drawing state.
+    """
+    refPt = cast(list[tuple[int, int]], param["refPt"])
+    img = cast(npt.NDArray[np.uint8], param["img"])
+    img_copy = cast(npt.NDArray[np.uint8], param["img_copy"])
+
+    if event == cv2.EVENT_LBUTTONDOWN:
+        refPt.append((x, y))
+        param["drawing"] = True
+
+    elif event == cv2.EVENT_MOUSEMOVE:
+        if param["drawing"]:
+            img_copy[:] = img[:]  # Reset to the original image before drawing the line
+            cv2.line(img_copy, refPt[0], (x, y), (0, 255, 0), 2)
+            cv2.imshow("image", img_copy)
+
+    elif event == cv2.EVENT_LBUTTONUP:
+        refPt.append((x, y))
+        param["drawing"] = False
+        cv2.line(img, refPt[0], refPt[1], (0, 255, 0), 2)
+        cv2.imshow("image", img)
+
+
+def get_pixel_distance(img: npt.NDArray[np.int_]) -> float:
+    """Display the image and allow the user to draw a line representing 1 cm.
+
+    This function uses OpenCV to display the image and capture the user input
+    for drawing a line that represents 1 cm on the ruler. It calculates the
+    Euclidean distance between the two points of the line in pixels.
+
+    Args:
+        img: The image on which the user will draw a line.
+
+    Returns:
+        The distance in pixels between the two drawn points. Returns 0 if the
+        line was not drawn correctly.
+    """
+    refPt: list[tuple[int, int]] = []
+    drawing: bool = False
+    img_copy: npt.NDArray[np.int_] = img.copy()
+
+    cv2.namedWindow("image")
+    cv2.setMouseCallback(
+        "image",
+        draw_line,  # type: ignore
+        {"refPt": refPt, "drawing": drawing, "img": img, "img_copy": img_copy},
+    )
+
+    print(
+        "Draw a line representing 1 cm according to the ruler's scaling in the image."
+    )
+
+    while True:
+        cv2.imshow("image", img_copy)
+        key: int = cv2.waitKey(1) & 0xFF
+        if key == ord("q"):
+            break
+
+    cv2.destroyAllWindows()
+
+    if len(refPt) == 2:
+        pixel_distance: float = np.sqrt(
+            (refPt[1][0] - refPt[0][0]) ** 2 + (refPt[1][1] - refPt[0][1]) ** 2
+        )
+        return pixel_distance
+    else:
+        print("Line was not drawn correctly.")
+        return 0.0
+
+
+def get_cm_per_pixel(
+    pixel_distance: float, scale_percent: float, img_resample: float
+) -> float:
+    """Calculate the conversion ratio from pixels to centimeters.
+
+    Args:
+        pixel_distance: The distance in pixels between the two drawn points.
+        scale_percent: The scaling percentage applied to the image during resizing.
+        img_resample: The resampling factor applied to the original target and
+        background image.
+
+    Returns:
+        The conversion factor in centimeters per pixel, corrected for the resampling
+        applied to the original image.
+    """
+    original_pixel_distance: float = pixel_distance / scale_percent
+    cm_per_pixel: float = 1.0 / original_pixel_distance
+    cm_per_pixel = cm_per_pixel / img_resample
+    return cm_per_pixel
+
+
+def calculate_px2cm(image_path: Path, img_resample: float) -> float:
+    """Calculates the conversion factor from pixels to centimeters.
+
+    This function reads an image of a ruler, allows the user to draw a line
+    correspondingnto 1 cm on the ruler, and calculates the pixel-to-centimeter
+    conversion factor. The image is scaled down for easier interaction, but the final
+    calculation accounts forthis scaling as well as the resample factor for target and
+    background images to ensure accuracy relative to the original image size.
+
+    Args:
+        image_path (str): The path to the image file.
+        img_resample (float): The resampling factor applied to the original target and
+        background image.
+
+    Returns:
+        float: The conversion factor in centimeters per pixel, corrected for the
+        resampling applied to the original image.
+    """
+    image = load_image(image_path)
+    image, scale_percent = resize_to_target_width(image)
+    pixel_distance: float = get_pixel_distance(image)
+    if pixel_distance > 0:
+        cm_per_pixel: float = get_cm_per_pixel(
+            pixel_distance, scale_percent, img_resample
+        )
+        print(f"Conversion factor: {cm_per_pixel} cm per pixel")
+    return cm_per_pixel
diff --git a/bubble_analyser/config.py b/bubble_analyser/config.py
new file mode 100644
index 0000000..9f6dbee
--- /dev/null
+++ b/bubble_analyser/config.py
@@ -0,0 +1,273 @@
+"""This module defines the configuration parameters for the Bubble Analyser project.
+
+The `Config` class is a Pydantic model that validates and manages the configuration
+parameters used in the image processing and analysis routines. These parameters
+include morphological element sizes, connectivity, marker size, image resampling
+factors, and more. The class also includes methods to validate the ranges of these
+parameters, ensuring that they are logically consistent before being used in the
+processing algorithms.
+
+Classes:
+    Config: A Pydantic model for storing and validating configuration parameters.
+
+Methods:
+    check_morphological_element_size_range: Validates the morphological element size
+    range.
+    check_connectivity_range: Validates the connectivity range.
+    check_marker_size_range: Validates the marker size range.
+    check_resample_range: Validates the resample range.
+    check_max_eccentricity_range: Validates the maximum eccentricity range.
+    check_min_solidity_range: Validates the minimum solidity range.
+    check_min_size_range: Validates the minimum size range.
+"""
+
+from pathlib import Path
+
+import typing_extensions
+from pydantic import (
+    BaseModel,
+    PositiveFloat,
+    PositiveInt,
+    StrictBool,
+    StrictFloat,
+    model_validator,
+)
+
+
+class Config(BaseModel):  # type: ignore
+    """A Pydantic model for storing and validating configuration parameters.
+
+    The class contains parameters for image processing and analysis, such as
+    morphological element sizes, connectivity, marker size, image resampling
+    factors, maximum eccentricity, and more. The class also includes methods to
+    validate the ranges of these parameters, ensuring that they are logically
+    consistent before being used in the processing algorithms.
+    # Morphological element used for binary operations, e.g. opening, closing, etc.
+    Morphological_element_size: PositiveInt
+    Morphological_element_size_range: tuple[PositiveInt, PositiveInt]
+
+    Attributes:
+        Morphological_element_size: PositiveInt
+        Morphological_element_size_range: tuple[PositiveInt, PositiveInt]
+        Connectivity: PositiveInt
+        Connectivity_range: tuple[PositiveInt, PositiveInt]
+        Marker_size: PositiveInt
+        Marker_size_range: tuple[PositiveInt, PositiveInt]
+        resample: PositiveFloat
+        resample_range: tuple[PositiveFloat, PositiveFloat]
+        Max_Eccentricity: PositiveFloat
+        Max_Eccentricity_range: tuple[PositiveFloat, PositiveFloat]
+        Min_Solidity: PositiveFloat
+        Min_Solidity_range: tuple[PositiveFloat, PositiveFloat]
+        min_size: StrictFloat
+        min_size_range: tuple[StrictFloat, StrictFloat]
+        px2mm: PositiveFloat
+        target_img_path: Path
+        background_img_path: Path
+        threshold_value: PositiveFloat
+        ruler_img_path: Path
+        do_batch: StrictBool
+    """
+
+    # Default PARAMETERS
+
+    # Morphological element used for binary operations, e.g. opening, closing, etc.
+    Morphological_element_size: PositiveInt
+    Morphological_element_size_range: tuple[PositiveInt, PositiveInt]
+
+    # Connectivity used, use 4 or 8
+    Connectivity: PositiveInt
+    Connectivity_range: tuple[PositiveInt, PositiveInt]
+
+    # Marker size for watershed segmentation
+    Marker_size: PositiveInt
+    Marker_size_range: tuple[PositiveInt, PositiveInt]
+
+    # Images can be resampled to make processing faster
+    resample: PositiveFloat
+    resample_range: tuple[PositiveFloat, PositiveFloat]
+
+    # Reject abnormal bubbles from quantification. E>0.85 or S<0.9
+    Max_Eccentricity: PositiveFloat
+    Max_Eccentricity_range: tuple[PositiveFloat, PositiveFloat]
+    Min_Solidity: PositiveFloat
+    Min_Solidity_range: tuple[PositiveFloat, PositiveFloat]
+
+    # Also ignore too small bubbles (equivalent diameter in mm)
+    min_size: StrictFloat
+    min_size_range: tuple[StrictFloat, StrictFloat]
+
+    # User input Image resolution
+    px2mm: PositiveFloat
+
+    # Path for Target image
+    target_img_path: Path
+
+    # Path for Background image
+    background_img_path: Path
+
+    # Threshold value for background subtraction
+    threshold_value: PositiveFloat
+
+    # Path for Ruler image
+    ruler_img_path: Path
+
+    # Batch processing flag
+    do_batch: StrictBool
+
+    @model_validator(mode="after")
+    def check_morphological_element_size_range(self) -> typing_extensions.Self:
+        """Validates the morphological element size range.
+
+        Ensures that the lower bound of the range is less than the upper bound.
+        If the bounds are in the wrong order, a ValueError is raised.
+
+        Returns:
+            Self: The instance itself, for method chaining.
+        """
+        low, high = self.Morphological_element_size_range
+        # Check if the lower bound is less than the upper bound
+        if low >= high:
+            # Raise a ValueError if the bounds are in the wrong order
+            raise ValueError(
+                "Limits for the Morphological_element_size_range are in the wrong order"
+            )
+        return self
+
+    @model_validator(mode="after")
+    def check_connectivity_range(self) -> typing_extensions.Self:
+        """Validates the connectivity range.
+
+        Ensures that the lower bound of the range is less than the upper bound.
+        If the bounds are in the wrong order, a ValueError is raised.
+
+        Returns:
+            Self: The instance itself, for method chaining.
+        """
+        low, high = self.Connectivity_range
+        # Check if the lower bound is less than the upper bound
+        if low >= high:
+            # Raise a ValueError if the bounds are in the wrong order
+            raise ValueError("Limits for the Connectivity_range are in the wrong order")
+        return self
+
+    @model_validator(mode="after")
+    def check_marker_size_range(self) -> typing_extensions.Self:
+        """Validates the marker size range.
+
+        Ensures that the lower bound of the range is less than the upper bound.
+        If the bounds are in the wrong order, a ValueError is raised.
+
+        Returns:
+            Self: The instance itself, for method chaining.
+        """
+        # Get the lower and upper bounds of the marker size range
+        low, high = self.Marker_size_range
+
+        # Check if the lower bound is less than the upper bound
+        if low >= high:
+            # Raise a ValueError if the bounds are in the wrong order
+            raise ValueError("Limits for the Marker_size_range are in the wrong order")
+
+        # Return the instance itself for method chaining
+        return self
+
+    @model_validator(mode="after")
+    def check_resample_range(self) -> typing_extensions.Self:
+        """Validates the resample range.
+
+        Ensures that the lower bound of the range is less than the upper bound.
+        If the bounds are in the wrong order (lower bound >= upper bound), a ValueError
+        is raised.
+
+        Returns:
+            Self: The instance itself, for method chaining.
+
+        Raises:
+            ValueError: If the lower bound is greater than or equal to the upper bound.
+        """
+        # Get the lower and upper bounds of the resample range
+        low, high = self.resample_range
+
+        # Check if the lower bound is less than the upper bound
+        if low >= high:
+            # Raise a ValueError if the bounds are in the wrong order
+            raise ValueError("Limits for the resample_range are in the wrong order")
+
+        # Return the instance itself for method chaining
+        return self
+
+    @model_validator(mode="after")
+    def check_max_eccentricity_range(self) -> typing_extensions.Self:
+        """Validates the maximum eccentricity range.
+
+        Ensures that the lower bound of the range is less than the upper bound.
+        If the bounds are in the wrong order (lower bound >= upper bound), a ValueError
+        is raised.
+
+        Returns:
+            Self: The instance itself, for method chaining.
+
+        Raises:
+            ValueError: If the lower bound is greater than or equal to the upper bound.
+        """
+        # Get the lower and upper bounds of the maximum eccentricity range
+        low, high = self.Max_Eccentricity_range
+
+        # Check if the lower bound is less than the upper bound
+        if low >= high:
+            # Raise a ValueError if the bounds are in the wrong order
+            raise ValueError(
+                "Limits for the Max_Eccentricity_range are in the wrong order"
+            )
+
+        # Return the instance itself for method chaining
+        return self
+
+    @model_validator(mode="after")
+    def check_min_solidity_range(self) -> typing_extensions.Self:
+        """Validates the minimum solidity range.
+
+        Ensures that the lower bound of the range is less than the upper bound.
+        If the bounds are in the wrong order (lower bound >= upper bound), a ValueError
+        is raised.
+
+        Returns:
+            Self: The instance itself, for method chaining.
+
+        Raises:
+            ValueError: If the lower bound is greater than or equal to the upper bound.
+        """
+        # Get the lower and upper bounds of the minimum solidity range
+        low, high = self.Min_Solidity_range
+
+        # Check if the lower bound is less than the upper bound
+        if low >= high:
+            # Raise a ValueError if the bounds are in the wrong order
+            raise ValueError("Limits for the Min_Solidity_range are in the wrong order")
+
+        return self
+
+    @model_validator(mode="after")
+    def check_min_size_range(self) -> typing_extensions.Self:
+        """Validates the minimum size range.
+
+        Ensures that the lower bound of the range is less than the upper bound.
+        If the bounds are in the wrong order (lower bound >= upper bound), a ValueError
+        is raised.
+
+        Returns:
+            Self: The instance itself, for method chaining.
+
+        Raises:
+            ValueError: If the lower bound is greater than or equal to the upper bound.
+        """
+        # Get the lower and upper bounds of the minimum size range
+        low, high = self.min_size_range
+
+        # Check if the lower bound is less than the upper bound
+        if low >= high:
+            # Raise a ValueError if the bounds are in the wrong order
+            raise ValueError("Limits for the min_size_range are in the wrong order")
+        # Return the instance itself for method chaining
+        return self
diff --git a/bubble_analyser/config.toml b/bubble_analyser/config.toml
new file mode 100644
index 0000000..3b48e61
--- /dev/null
+++ b/bubble_analyser/config.toml
@@ -0,0 +1,48 @@
+# Parameters are described by its name, value, and range.
+# All three items separated by commas
+
+# Default PARAMETERS
+
+# Morphological element used for binary operations, e.g. opening, closing, etc.
+Morphological_element_size = 5
+Morphological_element_size_range = [3, 10]
+
+# Connectivity used, use 4 or 8
+Connectivity = 8
+Connectivity_range = [4, 8]
+
+# Marker size for watershed segmentation
+Marker_size = 10
+Marker_size_range = [2, 30]
+
+# Images can be resampled to make processing faster
+resample = 0.5
+resample_range = [0.1, 1.0]
+
+# Reject abnormal bubbles from quantification. E>0.85 or S<0.9
+Max_Eccentricity = 0.85
+Max_Eccentricity_range = [0.1, 1.0]
+Min_Solidity = 0.9
+Min_Solidity_range = [0.1, 1.0]
+
+# Also ignore too small bubbles (equivalent diameter in mm)
+min_size = 0.1
+min_size_range = [0, 50]
+
+# Image resolution
+px2mm = 1.0
+
+# Target image
+target_img_path = "./tests/sample_images/03.jpg"
+
+# Background image
+background_img_path = "./tests/calibration_files/Background.png"
+
+# Threshold value for background subtraction
+threshold_value = 0.3
+
+# Ruler image
+ruler_img_path = "./tests/calibration_files/Ruler.png"
+
+# Batch processing flag
+do_batch = false
diff --git a/bubble_analyser/default.py b/bubble_analyser/default.py
new file mode 100644
index 0000000..6fba838
--- /dev/null
+++ b/bubble_analyser/default.py
@@ -0,0 +1,246 @@
+"""Bubble Analyser: Image Processing for Circular Feature Detection.
+
+This script is designed to process and analyze images to detect and evaluate circular
+features, such as bubbles, using various image processing techniques. The script
+integrates several modular functions for loading images, processing them, and
+calculating properties of detected features in terms of real-world measurements.
+
+The key functionalities include:
+
+1. Loading configuration parameters from a TOML file, which govern the image processing
+   steps and parameters.
+2. Loading and preprocessing images, including conversion to grayscale and resizing
+   based on a given resampling factor.
+3. Calculating the conversion factor from pixels to centimeters using a reference ruler
+   image, ensuring that measurements of detected features are accurate and scalable.
+4. Executing the image processing algorithm, which involves thresholding, morphological
+   processing, distance transformation, connected component labeling, and watershed
+   segmentation to isolate and analyze circular features.
+5. Displaying and saving intermediary and final images, along with calculating and
+   printing properties such as equivalent diameter and area of the detected features.
+
+The script is structured to allow easy customization and extension, making it suitable
+for a wide range of image analysis tasks that involve circular feature detection and
+measurement.
+
+To run the script, simply execute the `default()` function, which orchestrates the
+entire process from loading configurations and images to running the analysis and
+displaying results.
+"""
+
+from pprint import pprint
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import toml as tomllib
+from numpy import typing as npt
+from skimage import (
+    color,
+    io,
+    morphology,
+    transform,
+)
+
+from .calculate_circle_properties import calculate_circle_properties
+from .calculate_px2cm import calculate_px2cm
+from .config import Config
+from .image_preprocess import image_preprocess
+from .morphological_process import morphological_process
+from .threshold import threshold
+
+
+def load_image(
+    image_path: str, img_resample: float
+) -> tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]]:
+    """Read and preprocess the input image.
+
+    This function loads an image from the specified path, resizes it according to the
+    given resampling factor, and converts it to grayscale if the image is in RGB format.
+
+    Args:
+        image_path: The file path of the image to load.
+        img_resample: The factor by which the image will be resampled (e.g., 0.5 for
+        reducing the size by half).
+
+    Returns:
+        A tuple containing:
+        - The preprocessed grayscale image (if the original was in RGB) or the original
+        grayscale image.
+        - The resized image in RGB format.
+    """
+    # Read the input image
+    img = io.imread(image_path)
+
+    imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+    scale_percent = img_resample * 100  # percent of original size
+    width = int(imgRGB.shape[1] * scale_percent / 100)
+    height = int(imgRGB.shape[0] * scale_percent / 100)
+    dim = (width, height)
+
+    # resize image
+    imgRGB = cv2.resize(imgRGB, dim, interpolation=cv2.INTER_AREA)
+
+    img = transform.resize(
+        img,
+        (int(img.shape[0] * img_resample), int(img.shape[1] * img_resample)),
+        anti_aliasing=True,
+    )
+    if img.ndim > 2:
+        img = color.rgb2gray(img)  # Convert to grayscale if the image is in RGB
+
+    return img, imgRGB
+
+
+def load_toml(file_path: str) -> Config:
+    """Load configuration parameters from a TOML file.
+
+    This function reads the TOML configuration file from the specified path and loads
+    its contents into a dictionary.
+
+    Args:
+        file_path: The file path of the TOML configuration file.
+
+    Returns:
+        A dictionary containing the configuration parameters from the TOML file.
+    """
+    toml_data = tomllib.load(file_path)
+
+    return Config(**toml_data)
+
+
+def run_algorithm(
+    target_img: npt.NDArray[np.int_],
+    bknd_img: npt.NDArray[np.int_],
+    imgRGB: npt.NDArray[np.int_],
+    params: Config,
+    px2cm: float,
+    threshold_value: float,
+) -> None:
+    """Execute the image processing algorithm on the target image.
+
+    This function performs a series of image processing steps on the target image,
+    including thresholding, morphological processing, and watershed segmentation.
+    It then calculates properties of the detected circular features, such as equivalent
+    diameter and area, in centimeters using the provided pixel-to-centimeter ratio.
+
+    Args:
+        target_img: The preprocessed target image.
+        bknd_img: The background image used for thresholding.
+        imgRGB: The resized target image in RGB format.
+        params: A dictionary of parameters loaded from the TOML file.
+        px2cm: The conversion factor between pixels and centimeters.
+        threshold_value: Threshold value for background subtraction
+
+    Returns:
+        None. The function displays and saves various intermediary and final images, and
+        prints the properties of the detected circular features.
+    """
+    # Extract parameters from the dictionary
+    element_size = morphology.disk(
+        params.Morphological_element_size
+    )  # Structuring element for morphological operations
+
+    # Below are variables that might be used in the future coding
+    # connectivity = params.Connectivity  # Neighborhood connectivity (4 or 8)
+    # marker_size = params.Marker_size  # Marker size for watershed segmentation
+    # max_eccentricity = params.Max_Eccentricity  # Maximum eccentricity threshold
+    # min_solidity = params.Min_Solidity  # Minimum solidity threshold
+    # min_bubble_size = params.min_size  # Minimum bubble size (in mm)
+    # do_batch = params.do_batch  # Flag for batch processing
+
+    # Display the original image
+    plt.figure()
+    plt.subplot(231)
+    plt.title("1. Original image")
+    plt.imshow(target_img, cmap="gray")
+
+    # Apply thresholding and morphological processing
+    plt.subplot(232)
+    imgThreshold_ = threshold(target_img, bknd_img, threshold_value)
+    imgThreshold = morphological_process(imgThreshold_, element_size)
+    plt.title("2. Thresh&morph process")
+    plt.imshow(imgThreshold * 255, cmap="gray")
+
+    # Apply distance transform
+    plt.subplot(233)
+    distTrans = cv2.distanceTransform(imgThreshold, cv2.DIST_L2, 5)
+    plt.title("3. Distance Transform")
+    plt.imshow(distTrans)
+
+    # Apply thresholding to the distance transform
+    plt.subplot(234)
+    _, distThresh = cv2.threshold(
+        distTrans, 0.3 * distTrans.max(), 255, cv2.THRESH_BINARY
+    )
+    plt.title("4. Threshold of distTrans")
+    plt.imshow(distThresh)
+
+    # Apply connected component labeling
+    plt.subplot(235)
+    distThresh = distThresh.astype(np.uint8)
+    _, labels = cv2.connectedComponents(distThresh)
+    plt.title("5. Labels")
+    plt.imshow(labels)
+
+    # Apply watershed segmentation
+    plt.figure()
+    plt.subplot(121)
+    labels = labels.astype(np.int32)
+    labels = cv2.watershed(imgRGB, labels).astype(np.int_)
+    plt.title("6. Final graph after watershed")
+    plt.imshow(labels)
+
+    # Display the images
+    plt.show()
+
+    # Calculate and print the circle properties
+    circle_properties = calculate_circle_properties(labels, px2cm)
+    pprint(circle_properties)
+
+
+def default() -> None:
+    """Run the default image processing routine.
+
+    This function loads the configuration parameters from the TOML file, calculates the
+    pixel-to-centimeter ratio using a reference ruler image, and then runs the image
+    processing algorithm on the target image to detect and analyze circular features.
+
+    Args:
+        None.
+
+    Returns:
+        None. The function orchestrates the loading of images, execution of the
+        algorithm, and display of results.
+    """
+    # Load parameters from the TOML configuration file
+    params = load_toml("./bubble_analyser/config.toml")
+
+    # Read path and image resample factor
+    ruler_img_path = params.ruler_img_path
+    target_img_path = params.target_img_path
+    bknd_img_path = params.background_img_path
+    img_resample_factor = params.resample
+    threshold_value = params.threshold_value
+
+    # Calculate the pixel to cm ratio
+    px2cm = calculate_px2cm(ruler_img_path, img_resample_factor)
+    print(f"Pixel to cm ratio: {px2cm} cm/pixel")
+
+    # Read the background and target image, resize and process into gray scale
+    bknd_img, _ = image_preprocess(bknd_img_path, img_resample_factor)
+    target_img, imgRGB = image_preprocess(target_img_path, img_resample_factor)
+
+    # Run the default image processing algorithm
+    run_algorithm(target_img, bknd_img, imgRGB, params, px2cm, threshold_value)
+
+
+if __name__ == "__main__":
+    default()
+
+# First background subtraction (optional) then otsu thresholding
+# Let user define limitations based on the properties of the bubbles for filtering them
+# Output the image that eliminate the bubbles being filtered out
+# Table and Histogram
+# Let user modify the parameters in UI
+# Merge default branch
diff --git a/bubble_analyser/image_preprocess.py b/bubble_analyser/image_preprocess.py
new file mode 100644
index 0000000..005fb6c
--- /dev/null
+++ b/bubble_analyser/image_preprocess.py
@@ -0,0 +1,163 @@
+"""Image Preprocessing Functions.
+
+This module provides a collection of functions for image loading, color space conversion
+, and resizing. It supports the preprocessing steps required for image analysis tasks,
+especially in contexts where images need to be adapted for algorithmic processing and
+visualization.
+
+Key Functions:
+- load_image(image_path): Loads an image from a specified path into a NumPy array.
+- get_greyscale(image): Converts an RGB image to grayscale, facilitating algorithms
+  that require single-channel input.
+- get_RGB(image): Converts an image from BGR (common in OpenCV) to RGB format, suitable
+  for consistent image display and processing.
+- resize_for_RGB(image, img_resample_factor): Resizes an RGB image according to a
+  specified resampling factor, typically used to reduce the image size for faster
+  processing without losing significant detail.
+- resize_for_original_image(image, img_resample_factor): Similar to resize_for_RGB but
+  uses skimage's transform for resizing, providing a high-quality downsampling suitable
+  for analytical purposes.
+- image_preprocess(img_path, img_resample): Orchestrates the loading, converting, and
+  resizing of an image. It outputs both a grayscale version for processing and an RGB
+  version for visualization.
+
+These functions are designed to be modular and can be combined in different ways
+depending on the specific requirements of the image processing task at hand. For example
+, in a typical workflow for image analysis, an image might be loaded, converted to
+grayscale for analysis, and also kept in RGB for result visualization.
+
+Usage:
+These utilities are particularly useful in applications like computer vision and digital
+image processing where preprocessing steps are crucial for subsequent analysis, such as
+object detection, pattern recognition, and more.
+"""
+
+from pathlib import Path
+from typing import cast
+
+import cv2
+import numpy as np
+from numpy import typing as npt
+from skimage import (
+    color,
+    io,
+    transform,
+)
+
+
+def load_image(image_path: Path) -> npt.NDArray[np.int_]:
+    """Read and preprocess the input image.
+
+    Args:
+    image_path (str): The file path of the image to load.
+
+    Returns:
+    npt.NDArray: The image read in ndarray format.
+    """
+    # Read the input image
+
+    img = io.imread(image_path)
+
+    return img
+
+
+def get_greyscale(image: npt.NDArray[np.int_]) -> npt.NDArray[np.int_]:
+    """Converts an image to grayscale if it is in RGB format.
+
+    Args:
+        image (npt.NDArray): The input image to be converted.
+
+    Returns:
+        npt.NDArray: The grayscale image.
+    """
+    if image.ndim > 2:
+        image = color.rgb2gray(image)  # Convert to grayscale if the image is in RGB
+    return image
+
+
+def get_RGB(image: npt.NDArray[np.int_]) -> npt.NDArray[np.int_]:
+    """Converts an image from BGR color space to RGB color space.
+
+    Args:
+    image (npt.NDArray): The input image in BGR format.
+
+    Returns:
+    npt.NDArray: The converted image in RGB format.
+    """
+    imgRGB = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    return cast(npt.NDArray[np.int_], imgRGB)
+
+
+def resize_for_RGB(
+    image: npt.NDArray[np.int_], img_resample_factor: float
+) -> npt.NDArray[np.int_]:
+    """Resizes an image in RGB format based on the provided resampling factor.
+
+    Args:
+        image (npt.NDArray): The input image in RGB format.
+        img_resample_factor (float): The factor by which the image will be resampled.
+
+    Returns:
+        npt.NDArray: The resized image in RGB format.
+    """
+    scale_percent = img_resample_factor * 100  # percent of original size
+    width = int(image.shape[1] * scale_percent / 100)
+    height = int(image.shape[0] * scale_percent / 100)
+    img_resample_dimension = (width, height)
+
+    image_resized = cv2.resize(
+        image, img_resample_dimension, interpolation=cv2.INTER_AREA
+    )
+    return cast(npt.NDArray[np.int_], image_resized)
+
+
+def resize_for_original_image(
+    image: npt.NDArray[np.int_], img_resample_factor: float
+) -> npt.NDArray[np.int_]:
+    """Resizes an image based on the provided resampling factor for original image.
+
+    Args:
+        image (npt.NDArray): The input image to be resized.
+        img_resample_factor (float): The factor by which the image will be resampled.
+
+    Returns:
+        npt.NDArray: The resized image.
+    """
+    image = transform.resize(
+        image,
+        (
+            int(image.shape[0] * img_resample_factor),
+            int(image.shape[1] * img_resample_factor),
+        ),
+        anti_aliasing=True,
+    )
+    return image
+
+
+def image_preprocess(
+    img_path: Path, img_resample: float
+) -> tuple[npt.NDArray[np.int_], npt.NDArray[np.int_]]:
+    """Load an image, resizing it based on a provided resampling factor.
+
+    The resized grayscale image (img) is for use in the following "default" watershed
+    algorithm as a target image. And the RGB image (imgRGB) is for the visualization of
+    the results. They are resized in different ways for different use cases (the output
+    format of the methods are differnt).
+
+    Args:
+        img_path (str): The file path of the image to preprocess.
+        img_resample (float): The resampling factor to apply to the image.
+
+    Returns:
+        tuple[npt.NDArray, npt.NDArray]: A tuple containing the resized grayscale image
+        and the resized RGB image.
+    """
+    image = load_image(img_path)
+    image_RGB = get_RGB(image)
+
+    image = resize_for_original_image(image, img_resample)
+    image = get_greyscale(image)
+
+    image_RGB = resize_for_RGB(image_RGB, img_resample)
+
+    return image, image_RGB
diff --git a/bubble_analyser/morphological_process.py b/bubble_analyser/morphological_process.py
new file mode 100644
index 0000000..e726869
--- /dev/null
+++ b/bubble_analyser/morphological_process.py
@@ -0,0 +1,57 @@
+"""Morphological Processing Function for filling holes and clear borders.
+
+This module includes functions for advanced image processing using morphological
+operations tailored to for filling holes and clear borders for further analysis. It
+specifically focuses on refining the binary masks generated during image segmentation
+processes.
+
+Function:
+- morphological_process(target_img, element_size): Enhances a binary image by
+  applying morphological operations such as closing, hole filling, and border clearing.
+
+This function is particularly useful in contexts where binary images derived from
+thresholding or other segmentation methods contain noise, small holes, or artifacts
+that can interfere with further analysis. By using operations like closing to connect
+nearby regions, filling holes to ensure that objects are solid, and clearing borders
+to remove partial objects, this function prepares images for more reliable and robust
+analysis.
+"""
+
+import numpy as np
+from numpy import typing as npt
+from scipy import ndimage
+from skimage import (
+    morphology,
+    segmentation,
+)
+
+
+def morphological_process(
+    target_img: npt.NDArray[np.bool_], element_size: int
+) -> npt.NDArray[np.int_]:
+    """Apply morphological operations to process the target image.
+
+    This function performs a series of morphological operations on the input image,
+    including closing, filling holes, and clearing borders. These operations help in
+    refining the binary image by removing noise and filling gaps.
+
+    Args:
+        target_img: A binary image (numpy array) where the regions of interest are
+        typically in white (True) and the background in black (False).
+        element_size: A structuring element used for morphological closing, typically a
+        disk-shaped array.
+
+
+    Returns:
+        A processed binary image (numpy array) where the regions of interest are more
+        defined, with filled holes and cleared borders.
+    """
+    # Perform morphological closing and fill holes
+    image_processed = morphology.closing(target_img, element_size)
+    image_processed = ndimage.binary_fill_holes(image_processed)
+    image_processed = segmentation.clear_border(image_processed)
+
+    image_processed = image_processed.astype(np.uint8)
+    # opening = cv2.morphologyEx(B,cv2.MORPH_OPEN,kernel, iterations = 2)
+
+    return image_processed
diff --git a/bubble_analyser/threshold.py b/bubble_analyser/threshold.py
new file mode 100644
index 0000000..40ee358
--- /dev/null
+++ b/bubble_analyser/threshold.py
@@ -0,0 +1,101 @@
+"""Main Thresholding Functions.
+
+This module provides functions for applying various thresholding techniques to images,
+aimed at segmenting objects from their backgrounds. It includes implementations of
+Otsu's method and a customizable thresholding approach that involves background
+subtraction.
+
+Functions:
+- otsu_threshold(target_img): Applies Otsu's method to a grayscale image to create
+  a binary mask, which is then inverted for consistency with other processing steps.
+- select_threshold_method(): Provides an interactive prompt for the user to select
+  a thresholding method from available options, enhancing flexibility in choosing
+  the appropriate method for different scenarios.
+- threshold(target_img, bknd_img, threshold_value): Applies the chosen threshold method
+  to the target image, supporting either Otsu's method or a custom threshold based on
+  background subtraction, determined by user input.
+
+These thresholding functions are adaptable to various use cases, from basic academic
+projects to complex industrial applications requiring robust foreground-background
+segmentation. They are particularly useful in workflows that require pre-processing
+before detailed image analysis, such as feature detection or object classification.
+
+Usage:
+The functions can be directly called with appropriate parameters, with `threshold`
+function allowing for dynamic method selection based on runtime decisions. This
+design ensures that users can select the most suitable thresholding technique
+based on the specific characteristics of the images they are working with.
+"""
+
+import numpy as np
+from numpy import typing as npt
+from skimage import (
+    filters,
+)
+
+from .background_subtraction_threshold import background_subtraction_threshold
+
+
+def otsu_threshold(target_img: npt.NDArray[np.int_]) -> npt.NDArray[np.bool_]:
+    """Apply Otsu's thresholding to the target image and return an inverted binary mask.
+
+    This function takes a target image and a background image. It applies Otsu's
+    thresholding to the target image to create a binary mask where the foreground
+    objects are separatedfrom the background. The binary mask is then inverted, so
+    the foreground becomes the background and vice versa, the inversion is for easier
+    processing in following morphologcal process.
+
+    Args:
+        target_img: A grayscale image (2D array) representing the target image.
+        bknd_img: A grayscale image (2D array) representing the background image (not
+        used in this function).
+
+    Returns:
+        A binary (inverted) image where the foreground and background are swapped.
+    """
+    binary_image = target_img > filters.threshold_otsu(
+        target_img
+    )  # Binary image using Otsu's thresholding
+    return ~binary_image  # Invert the binary image
+
+
+def select_threshold_method() -> str:
+    """Selects a threshold method from a list of available options.
+
+    Prompts the user to choose a threshold method from the list of options.
+    The options are displayed with their corresponding numbers, and the user
+    is asked to input the number of their chosen method.
+
+    Returns:
+        str: The chosen threshold method.
+    """
+    options = ["OTSU's method", "Background subtraction"]
+    print("Select a threshold method:")
+    for i, option in enumerate(options):
+        print(f"{i+1}. {option}")
+    choice = input("Enter the number of your choice: ")
+    return options[int(choice) - 1]
+
+
+def threshold(
+    target_img: npt.NDArray[np.int_],
+    bknd_img: npt.NDArray[np.int_],
+    threshold_value: float,
+) -> npt.NDArray[np.bool]:
+    """Applies a threshold to the target image based on the selected method.
+
+    Args:
+        target_img (npt.NDArray): The target image to apply the threshold to.
+        bknd_img (npt.NDArray): The background image used for background subtraction.
+        threshold_value (float): The threshold value used for background subtraction.
+
+    Returns:
+        npt.NDArray: The thresholded image.
+    """
+    method = select_threshold_method()
+    if method == "OTSU's method":
+        return otsu_threshold(target_img)
+    elif method == "Background subtraction":
+        return background_subtraction_threshold(target_img, bknd_img, threshold_value)
+    else:
+        raise ValueError(f"Unsupported threshold method: {method}")
diff --git a/poetry.lock b/poetry.lock
index 1a3f2a1..f0811bc 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,5 +1,16 @@
 # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
 
+[[package]]
+name = "annotated-types"
+version = "0.7.0"
+description = "Reusable constraint types to use with typing.Annotated"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"},
+    {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"},
+]
+
 [[package]]
 name = "attrs"
 version = "23.2.0"
@@ -179,6 +190,69 @@ files = [
     {file = "colorama-0.4.6.tar.gz", hash = "sha256:08695f5cb7ed6e0531a20572697297273c47b8cae5a63ffc6d6ed5c201be6e44"},
 ]
 
+[[package]]
+name = "contourpy"
+version = "1.2.1"
+description = "Python library for calculating contours of 2D quadrilateral grids"
+optional = false
+python-versions = ">=3.9"
+files = [
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+    {file = "contourpy-1.2.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5b9eb0ca724a241683c9685a484da9d35c872fd42756574a7cfbf58af26677fd"},
+    {file = "contourpy-1.2.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4c75507d0a55378240f781599c30e7776674dbaf883a46d1c90f37e563453480"},
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+    {file = "contourpy-1.2.1-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:a31f94983fecbac95e58388210427d68cd30fe8a36927980fab9c20062645609"},
+    {file = "contourpy-1.2.1-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ef2b055471c0eb466033760a521efb9d8a32b99ab907fc8358481a1dd29e3bd3"},
+    {file = "contourpy-1.2.1-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:b33d2bc4f69caedcd0a275329eb2198f560b325605810895627be5d4b876bf7f"},
+    {file = "contourpy-1.2.1.tar.gz", hash = "sha256:4d8908b3bee1c889e547867ca4cdc54e5ab6be6d3e078556814a22457f49423c"},
+]
+
+[package.dependencies]
+numpy = ">=1.20"
+
+[package.extras]
+bokeh = ["bokeh", "selenium"]
+docs = ["furo", "sphinx (>=7.2)", "sphinx-copybutton"]
+mypy = ["contourpy[bokeh,docs]", "docutils-stubs", "mypy (==1.8.0)", "types-Pillow"]
+test = ["Pillow", "contourpy[test-no-images]", "matplotlib"]
+test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
+
 [[package]]
 name = "coverage"
 version = "7.5.4"
@@ -246,6 +320,21 @@ tomli = {version = "*", optional = true, markers = "python_full_version <= \"3.1
 [package.extras]
 toml = ["tomli"]
 
+[[package]]
+name = "cycler"
+version = "0.12.1"
+description = "Composable style cycles"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "cycler-0.12.1-py3-none-any.whl", hash = "sha256:85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30"},
+    {file = "cycler-0.12.1.tar.gz", hash = "sha256:88bb128f02ba341da8ef447245a9e138fae777f6a23943da4540077d3601eb1c"},
+]
+
+[package.extras]
+docs = ["ipython", "matplotlib", "numpydoc", "sphinx"]
+tests = ["pytest", "pytest-cov", "pytest-xdist"]
+
 [[package]]
 name = "distlib"
 version = "0.3.8"
@@ -287,6 +376,71 @@ docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1
 testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8.0.1)", "pytest (>=7.4.3)", "pytest-asyncio (>=0.21)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)", "virtualenv (>=20.26.2)"]
 typing = ["typing-extensions (>=4.8)"]
 
+[[package]]
+name = "fonttools"
+version = "4.53.1"
+description = "Tools to manipulate font files"
+optional = false
+python-versions = ">=3.8"
+files = [
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 version = "2.1.0"
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+docs = ["numpydoc", "pydata-sphinx-theme", "sphinx (<6)"]
+ffmpeg = ["imageio-ffmpeg", "psutil"]
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+pyav = ["av"]
+test = ["fsspec[github]", "pytest", "pytest-cov"]
+tifffile = ["tifffile"]
+
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 version = "2.0.0"
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 i18n = ["Babel (>=2.7)"]
 
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+    {file = "scipy-1.14.0-cp311-cp311-macosx_14_0_arm64.whl", hash = "sha256:94c164a9e2498e68308e6e148646e486d979f7fcdb8b4cf34b5441894bdb9caf"},
+    {file = "scipy-1.14.0-cp311-cp311-macosx_14_0_x86_64.whl", hash = "sha256:a7d46c3e0aea5c064e734c3eac5cf9eb1f8c4ceee756262f2c7327c4c2691c86"},
+    {file = "scipy-1.14.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9eee2989868e274aae26125345584254d97c56194c072ed96cb433f32f692ed8"},
+    {file = "scipy-1.14.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9e3154691b9f7ed73778d746da2df67a19d046a6c8087c8b385bc4cdb2cfca74"},
+    {file = "scipy-1.14.0-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:c40003d880f39c11c1edbae8144e3813904b10514cd3d3d00c277ae996488cdb"},
+    {file = "scipy-1.14.0-cp311-cp311-win_amd64.whl", hash = "sha256:5b083c8940028bb7e0b4172acafda6df762da1927b9091f9611b0bcd8676f2bc"},
+    {file = "scipy-1.14.0-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:bff2438ea1330e06e53c424893ec0072640dac00f29c6a43a575cbae4c99b2b9"},
+    {file = "scipy-1.14.0-cp312-cp312-macosx_12_0_arm64.whl", hash = "sha256:bbc0471b5f22c11c389075d091d3885693fd3f5e9a54ce051b46308bc787e5d4"},
+    {file = "scipy-1.14.0-cp312-cp312-macosx_14_0_arm64.whl", hash = "sha256:64b2ff514a98cf2bb734a9f90d32dc89dc6ad4a4a36a312cd0d6327170339eb0"},
+    {file = "scipy-1.14.0-cp312-cp312-macosx_14_0_x86_64.whl", hash = "sha256:7d3da42fbbbb860211a811782504f38ae7aaec9de8764a9bef6b262de7a2b50f"},
+    {file = "scipy-1.14.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d91db2c41dd6c20646af280355d41dfa1ec7eead235642178bd57635a3f82209"},
+    {file = "scipy-1.14.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:a01cc03bcdc777c9da3cfdcc74b5a75caffb48a6c39c8450a9a05f82c4250a14"},
+    {file = "scipy-1.14.0-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:65df4da3c12a2bb9ad52b86b4dcf46813e869afb006e58be0f516bc370165159"},
+    {file = "scipy-1.14.0-cp312-cp312-win_amd64.whl", hash = "sha256:4c4161597c75043f7154238ef419c29a64ac4a7c889d588ea77690ac4d0d9b20"},
+    {file = "scipy-1.14.0.tar.gz", hash = "sha256:b5923f48cb840380f9854339176ef21763118a7300a88203ccd0bdd26e58527b"},
+]
+
+[package.dependencies]
+numpy = ">=1.23.5,<2.3"
+
+[package.extras]
+dev = ["cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy (==1.10.0)", "pycodestyle", "pydevtool", "rich-click", "ruff (>=0.0.292)", "types-psutil", "typing_extensions"]
+doc = ["jupyterlite-pyodide-kernel", "jupyterlite-sphinx (>=0.13.1)", "jupytext", "matplotlib (>=3.5)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (>=0.15.2)", "sphinx (>=5.0.0)", "sphinx-design (>=0.4.0)"]
+test = ["Cython", "array-api-strict", "asv", "gmpy2", "hypothesis (>=6.30)", "meson", "mpmath", "ninja", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+
 [[package]]
 name = "six"
 version = "1.16.0"
@@ -1032,6 +1833,39 @@ files = [
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
 ]
 
+[[package]]
+name = "tifffile"
+version = "2024.8.10"
+description = "Read and write TIFF files"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "tifffile-2024.8.10-py3-none-any.whl", hash = "sha256:1c224564fa92e7e9f9a0ed65880b2ece97c3f0d10029ffbebfa5e62b3f6b343d"},
+    {file = "tifffile-2024.8.10.tar.gz", hash = "sha256:fdc12124f1478a07b1524641dc6b50cf6bde0483011a63fd2a773094090c3dcf"},
+]
+
+[package.dependencies]
+numpy = "*"
+
+[package.extras]
+all = ["defusedxml", "fsspec", "imagecodecs (>=2023.8.12)", "lxml", "matplotlib", "zarr"]
+codecs = ["imagecodecs (>=2023.8.12)"]
+plot = ["matplotlib"]
+test = ["cmapfile", "czifile", "dask", "defusedxml", "fsspec", "imagecodecs", "lfdfiles", "lxml", "ndtiff", "oiffile", "psdtags", "pytest", "roifile", "xarray", "zarr"]
+xml = ["defusedxml", "lxml"]
+zarr = ["fsspec", "zarr"]
+
+[[package]]
+name = "toml"
+version = "0.10.2"
+description = "Python Library for Tom's Obvious, Minimal Language"
+optional = false
+python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
+files = [
+    {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
+    {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},
+]
+
 [[package]]
 name = "tomli"
 version = "2.0.1"
@@ -1043,6 +1877,17 @@ files = [
     {file = "tomli-2.0.1.tar.gz", hash = "sha256:de526c12914f0c550d15924c62d72abc48d6fe7364aa87328337a31007fe8a4f"},
 ]
 
+[[package]]
+name = "types-toml"
+version = "0.10.8.20240310"
+description = "Typing stubs for toml"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "types-toml-0.10.8.20240310.tar.gz", hash = "sha256:3d41501302972436a6b8b239c850b26689657e25281b48ff0ec06345b8830331"},
+    {file = "types_toml-0.10.8.20240310-py3-none-any.whl", hash = "sha256:627b47775d25fa29977d9c70dc0cbab3f314f32c8d8d0c012f2ef5de7aaec05d"},
+]
+
 [[package]]
 name = "typing-extensions"
 version = "4.12.2"
@@ -1138,4 +1983,4 @@ watchmedo = ["PyYAML (>=3.10)"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "e8abd3bdd438c75c7770f41870a8c050f2824284d2bb0bf9180552b35a59950d"
+content-hash = "0f151e30f9dff59463c7635ecb20c77817fdca510ad5fd7a9db13f05a23e79ae"
diff --git a/pyproject.toml b/pyproject.toml
index 7f80c4a..a35802e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,28 +9,39 @@ authors = [
 
 [tool.poetry.dependencies]
 python = "^3.10"
+matplotlib = "^3.9.1.post1"
+toml = "^0.10.2"
+numpy = "^2.0.1"
+scikit-image = "^0.24.0"
+scipy = "^1.14.0"
+pydantic = "^2.8.2"
+pathlib = "^1.0.1"
+typing-extensions = "^4.12.2"
+opencv-python = "^4.10.0.84"
 
-[tool.poetry.dev-dependencies]
+[tool.poetry.group.docs.dependencies]
+mkdocs = "^1.6.0"
+mkdocs-material = "^9.5.29"
+
+[tool.poetry.group.dev.dependencies]
 pytest = "^8.2"
 pytest-cov = "^5.0.0"
 pytest-mypy = "^0.10.0"
 pytest-mock = "^3.7.0"
 pre-commit = "^3.0.4"
 ruff = "^0.5.2"
-
-[tool.poetry.group.docs.dependencies]
-mkdocs = "^1.6.0"
-mkdocs-material = "^9.5.29"
+types-toml = "^0.10.8.20240310"
 
 [build-system]
 requires = ["poetry-core>=1.0.0"]
 build-backend = "poetry.core.masonry.api"
 
 [tool.mypy]
+ignore_missing_imports = true
 disallow_any_explicit = true
 disallow_any_generics = true
 warn_unreachable = true
-warn_unused_ignores = true
+warn_unused_ignores = false
 disallow_untyped_defs = true
 exclude = [".venv/"]
 
diff --git a/tests/calibration_files/config.toml b/tests/calibration_files/config.toml
index 1ab4b08..18bed01 100644
--- a/tests/calibration_files/config.toml
+++ b/tests/calibration_files/config.toml
@@ -1,5 +1,5 @@
-# Config file for bubble analyser
-
-[image_resolution]
-value = 183.06589
-units = "px/mm"
+# Config file for bubble analyser
+
+[image_resolution]
+value = 183.06589
+units = "px/mm"