Abstract
Abstract Medical image segmentation plays a crucial role in various clinical applications, including disease diagnosis and treatment planning. In the context of spine imaging, accurate segmentation is essential for precise analysis and intervention. This study presents a comparative analysis of two prominent segmentation algorithms: fuzzy c-means (FCM) and region growing, applied to spine image segmentation. The dataset consists of spine images obtained from medical imaging modalities, preprocessed to enhance clarity and remove noise. Both FCM and region-growing algorithms are implemented with appropriate parameter settings and evaluated using quantitative metrics such as the Dice similarity coefficient, sensitivity, and specificity. Additionally, qualitative assessments are conducted through visual inspection of segmented images. The results reveal distinct performance characteristics of each algorithm, highlighting their respective strengths and weaknesses in spine image segmentation tasks. Through comprehensive analysis and discussion, this study provides valuable insights into the effectiveness of FCM and region-growing algorithms, aiding clinicians and researchers in selecting suitable segmentation approaches for spine imaging applications.
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