Segmentation of Mammogram Images Using Optimized Kernel Fuzzy AGCWD Based Level Set Method

Segmentation of Mammogram Images Using Optimized Kernel Fuzzy AGCWD Based Level Set Method

Автор: Azmeera Srinivas, V.V.K.D.V Prasad, B. Leela Kumari

Журнал: International Journal of Image, Graphics and Signal Processing @ijigsp

Статья в выпуске: 3 vol.16, 2024 года.

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Image enhancement technology is widely used to improve images and help radiologists make more accurate cancer diagnoses. In this research work presents an integrating approach for contrast enhancement followed by the segmentation of breast cancer from the mammogram images. The proposed method has been effectively utilized the three different algorithms such as differential Evolution (DE) Algorithm, Kernel Based Fuzzy C Means (KFCM) Clustering and Cuckoo Search Optimization (CSO) algorithm. Here an integrating approach introduced, called Optimized Kernel Fuzzy Adaptive Gamma Correction with Weighed Distribution (OKF-AGCWD) based Level Set Method. The performance of proposed method is enhanced over existing level set methods such as image and vision computing (IVC)-2010, IVC-2013, and Expert Systems with Applications (ESA) 2021. The performance metric parameters like F1_score, Sensitivity, Specificity and accuracy are considered to assess the quality of different methods. The simulation was performed on 16 distinct images from the RIDER mammography database. The experimental results were compared with existing level set approaches such as image and vision computing (IVC)2010, IVC2013 and expert systems and applications (ESA)2021 with respect to OKF-AGCWD. The proposed OKF-AGCWD with contextual level set method (CLSM) minimizes boundary leakage problem of mammogram segmented image and improves segmentation accuracy.

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Mammogram Images, Differential Evolution, Kernel FCM, Cuckoo Search Optimization and Level Set Method

Короткий адрес: https://sciup.org/15019454

IDR: 15019454   |   DOI: 10.5815/ijigsp.2024.03.06

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