SigmoidAI · Ghenntoggy1 · Sep 6, 2025 · Sep 6, 2025 · Sep 6, 2025 · Sep 6, 2025
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+# Image Segmentation in Medical Radiology: A State of the Art Review
+
+**Author:** Gusev Roman¹  
+**Affiliation:** ¹Sigmoid, Machine Learning Engineer Intern, Chișinău, Republic of Moldova  
+**Email:** [gusev.roman.work@gmail.com](mailto:gusev.roman.work@gmail.com)  
+**Date:** 08.09.2025
+
+## Article Summary
+
+With modern day technologies and advancements, Radiology became more sophisticated in technological aspect - MRIs, Rontgen and CTs evolved significantly and output images generated by them, has grown in number and resolution. Radiologists work, that deal with them on a daily basis, requires manual thorough analysis of each of them, becoming time-consuming and, overall, a routine work. Image Segmentation can help with identification of anomalies in human body in radiologic images by segmentation using ML/DL models, and reduce the time required to process images. Research paper reviews the state-of-the-art of Machine Learning and Radiology, focusing the attention on proposed solutions that involves advanced Deep Learning models, such as Convolutional Neural Networks and, less sophisticated Distance-based models, for example, K-Means Clustering, for the task of Image Segmentation. Provided code focuses on dataset preprocessing, since main research paper focus is on the state of the art.
+
+## Getting Started
+
+In order to work with this python file, Python 3.13.4 is recommended. To get started, do the following:
+
+1. **Prepare Your Environment**: Create Virtual Environment for Python or use existing:
+
+    ```sh
+    python -m venv myenv
+    source myenv/bin/activate      # On Windows: .\myenv\Scripts\activate
+    ```
+
+2. **Install Requirements**: Install required packages from `requirements.txt` file using next command:
+
+   ```sh
+   pip install -r requirements.txt
+   ```
+
+3. **Execute Python File in the preferred IDE**: Execute `main.py` file in the preferred IDE to see the example in action.
+    ```sh
+    python -u "<path/to/file>"
+    ```
+
+## Source Code Explanation
+
+The code in this folder provides basic example of Image Dataset Preprocessing before training an DL Model. Key components are described below:
+
+- `src/main.py`: This file contains the example provided for the article (Dataset Preprocessing Step). It takes raw input radiologic images and applies preprocessing steps - RGB-LAB Conversion, CLA Histogram Equalization, Gaussian Filtering, Resize, trasnforming them into clean input data for the ML/DL algorithms.
+
+- `src/data/`: This folder contains the images used to demonstrate the input dataset in a Brain Tumor Segmentation Problem. `mask/` folder contains ground-truth binary mask.
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diff --git a/article-image_segmentation_in_radiology/src/main.py b/article-image_segmentation_in_radiology/src/main.py
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+import cv2
+import os
+import matplotlib.pyplot as plt
+import numpy as np
+
+script_path = os.path.abspath(__file__)
+script_directory = os.path.dirname(script_path)
+data_directory = os.path.join(script_directory, 'data')
+masks_directory = os.path.join(data_directory, 'masks')
+
+masks_dict = {}
+for filename in os.listdir(masks_directory):
+    if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.tiff')):
+        mask = cv2.imread(os.path.join(masks_directory, filename), cv2.IMREAD_GRAYSCALE)
+        mask_bin = (mask > 0).astype(np.uint8)
+        masks_dict[filename] = mask_bin
+
+
+
+clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+
+for filename in os.listdir(data_directory):
+    if filename.lower().endswith(('.png', '.jpg', '.jpeg', '.bmp', '.tiff')):
+
+        # Load images
+        img_rgb = cv2.imread(os.path.join(data_directory, filename))
+        img_rgb = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2RGB)
+
+        mask = cv2.imread(os.path.join(masks_directory, filename), cv2.IMREAD_GRAYSCALE)
+        mask_bin = (mask > 0).astype(np.uint8)
+
+        # CLAHE on RGB 
+        clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+        img_rgb_clahe = cv2.merge([
+            clahe.apply(img_rgb[:, :, 0]),
+            clahe.apply(img_rgb[:, :, 1]),
+            clahe.apply(img_rgb[:, :, 2])
+        ])
+
+        # LAB conversion 
+        img_lab = cv2.cvtColor(img_rgb, cv2.COLOR_RGB2LAB)
+
+        l, a, b = cv2.split(img_lab)
+        l_clahe = clahe.apply(l)
+        img_lab_clahe = cv2.merge([l_clahe, a, b])
+
+        # Convert back to RGB for visualization
+        img_lab_rgb = cv2.cvtColor(img_lab, cv2.COLOR_LAB2RGB)
+        img_lab_clahe_rgb = cv2.cvtColor(img_lab_clahe, cv2.COLOR_LAB2RGB)
+
+        # Gaussian Filter on CLAHE LAB 
+        img_lab_clahe_gauss = cv2.GaussianBlur(img_lab_clahe, (5, 5), 0)
+        img_lab_clahe_gauss_rgb = cv2.cvtColor(img_lab_clahe_gauss, cv2.COLOR_LAB2RGB)
+
+        # Create mask overlay 
+        mask_colored = np.zeros_like(img_rgb)
+        mask_colored[:, :, 0] = mask_bin * 255  # red mask
+
+        img_rgb_overlay = cv2.addWeighted(img_rgb, 0.7, mask_colored, 0.3, 0)
+        img_rgb_clahe_overlay = cv2.addWeighted(img_rgb_clahe, 0.7, mask_colored, 0.3, 0)
+
+        img_lab_overlay = cv2.addWeighted(img_lab_rgb, 0.7, mask_colored, 0.3, 0)
+        img_lab_clahe_overlay = cv2.addWeighted(img_lab_clahe_rgb, 0.7, mask_colored, 0.3, 0)
+        img_lab_clahe_gauss_overlay = cv2.addWeighted(img_lab_clahe_gauss_rgb, 0.7, mask_colored, 0.3, 0)
+
+        # Plot 
+        fig, axes = plt.subplots(3, 5, figsize=(20, 10))  # 5 columns now
+
+        # Row 1: Originals
+        axes[0, 0].imshow(img_rgb); axes[0, 0].set_title("Original RGB"); axes[0, 0].axis("off")
+        axes[0, 1].imshow(img_rgb_clahe); axes[0, 1].set_title("CLAHE on RGB"); axes[0, 1].axis("off")
+        axes[0, 2].imshow(img_lab_rgb); axes[0, 2].set_title("Original LAB (as RGB)"); axes[0, 2].axis("off")
+        axes[0, 3].imshow(img_lab_clahe_rgb); axes[0, 3].set_title("CLAHE on L (LAB)"); axes[0, 3].axis("off")
+        axes[0, 4].imshow(img_lab_clahe_gauss_rgb); axes[0, 4].set_title("Gaussian Filter (CLAHE LAB)"); axes[0, 4].axis("off")
+
+        # Row 2: Masks
+        for j in range(5):
+            axes[1, j].imshow(mask, cmap="gray")
+            axes[1, j].set_title("Mask"); axes[1, j].axis("off")
+
+        # Row 3: Overlays
+        axes[2, 0].imshow(img_rgb_overlay); axes[2, 0].set_title("Original + Mask"); axes[2, 0].axis("off")
+        axes[2, 1].imshow(img_rgb_clahe_overlay); axes[2, 1].set_title("CLAHE RGB + Mask"); axes[2, 1].axis("off")
+        axes[2, 2].imshow(img_lab_overlay); axes[2, 2].set_title("LAB + Mask"); axes[2, 2].axis("off")
+        axes[2, 3].imshow(img_lab_clahe_overlay); axes[2, 3].set_title("CLAHE LAB + Mask"); axes[2, 3].axis("off")
+        axes[2, 4].imshow(img_lab_clahe_gauss_overlay); axes[2, 4].set_title("Gaussian CLAHE LAB + Mask"); axes[2, 4].axis("off")
+
+        plt.tight_layout()
+        plt.show()