Abstract
Segmentation plays a vital role in medical image processing. Manual image segmentation is very tedious and time-consuming. Also, results of manual segmentation are subjected to errors due to huge and varying data. Therefore, automated segmentation systems are gaining enormous importance nowadays. This study presents an automated system for segmentation of brain tissues from brain Magnetic Resonance Imaging (MRI) images. Segmentation of three main brain tissues is carried out, namely, white matter, gray matter, and cerebrospinal fluid. In this work, we performed the initialization step for Fuzzy C-means (FCM) clustering algorithm using Ant Colony Optimization (ACO). Also, Mahalanobis distance metric is used instead of Euclidean distance metric in clustering process to avoid any relative dependency upon the geometrical shapes of different clustering classes. The results of the system are evaluated and validated against the ground truth images for both real and simulated databases.
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Hooda, H., Verma, O.P., Arora, S. (2019). Optimal Fuzzy C-Means Algorithm for Brain Image Segmentation. In: Malik, H., Srivastava, S., Sood, Y., Ahmad, A. (eds) Applications of Artificial Intelligence Techniques in Engineering. Advances in Intelligent Systems and Computing, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-13-1819-1_56
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DOI: https://doi.org/10.1007/978-981-13-1819-1_56
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