Segmentation Method of Deterministic Feature Extraction for Identification of Brain Tumor Using MRI

Segmentation of brain tumors from magnetic resonance imaging (MRI) scans plays a crucial role in diagnosis and treatment planning. This paper presents a segmentation method utilizing deterministic feature clustering for the accurate identification of brain tumors from MRI images. The proposed method integrates feature extraction techniques to capture relevant characteristics of tumor regions, followed by a deterministic clustering algorithm to partition the extracted features into distinct classes corresponding to tumor and non-tumor regions. The feature clustering process incorporates spatial constraints to enhance segmentation accuracy and mitigate noise sensitivity. Experimental evaluations on diverse MRI datasets demonstrate the effectiveness of the proposed approach in accurately delineating tumor boundaries and differentiating tumor regions from healthy brain tissue. Comparative analyses against state-of-the-art segmentation methods underscore the superiority of the proposed method in terms of segmentation accuracy, robustness, and computational efficiency. The proposed segmentation method holds significant promise for improving the precision and reliability of brain tumor identification in clinical practice, thereby facilitating more effective treatment planning and patient care.