Khosro Rezaee - Department of Biomedical Engineering, Meybod University, Meybod, Iran.
Mohammad Khalil Nakhl Ahmadi - Islamic Azad University, Torbat-e Jam Branch, Torbat, Iran.
Maryam Saberi Anari - Department of Computer Engineering, Technical and Vocational University (TVU), Tehran, Iran.

Segmentation is a fundamental element in medical image processing (MIP) and has been extensively researched and developed to aid in clinical interpretation and utilization. This article discusses a method for segmenting abnormal masses or tumors in medical images that is both robust and effective. We suggested a method based on Active Contour (AC) and modified Level-set techniques to detect malignancies in magnetic resonance imaging (MRI), mammography, and computed tomography (CT). To segment malignant masses, the active contour approach, the energy function, the level-set method, and the proposed F function are employed. The system was evaluated using ۱۶۰ medical images from two databases, including ۸۰ mammograms and ۸۰ MRI brain scans. The algorithm for segmenting suspicious segments has an accuracy, recall, and precision of ۹۶.۲۵%, ۹۵.۶۰%, and ۹۵.۷۱%, respectively. By adding this technique into tissue imaging devices, the accuracy of diagnosing images with a relatively large volume that are evaluated fast is increased. Cost savings, time savings, and high precision are all advantages of the approach that set it apart from similar systems.

image processing, Medical Image, Highboost, Active Contour, Levelset, F-Energy