Lung Cancer Diagnosis Using Watershed Segmentation
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This MATLAB implementation utilizes watershed segmentation for lung cancer diagnosis. The method processes medical images to extract pulmonary lesion regions, assisting physicians in cancer diagnosis and treatment planning. The watershed algorithm effectively separates pathological areas from surrounding healthy tissues in medical images through gradient-based boundary detection. Key implementation steps include image preprocessing (noise reduction and contrast enhancement), gradient magnitude calculation using Sobel or Prewitt operators, and marker-controlled watershed transformation to prevent oversegmentation. This segmentation approach improves diagnostic accuracy and efficiency by precisely isolating suspicious regions. The watershed-based lung cancer diagnosis method has been widely adopted in medical imaging applications and has demonstrated significant clinical results in early detection and treatment monitoring.
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