сегментація зображень, сегментация изображений, image segmentation ; нейронні мережі, нейронные сети ; магнітний резонанс, магнитный резонанс, magnetic resonance ; оптимізація, оптимизация, optimization

Image segmentation is an indispensable process in the visualization of human tissues, particularly during clinical analysis of magnetic resonance (MR) images. Unfortunately, MR images always contain a significant amount of noise caused by operator performance, equipment, and the environment, which can lead to serious inaccuracies with segmentation. A robust segmentation technique based on an extension to the traditional fuzzy c-means (FCM) clustering algorithm is proposed in this paper. A neighborhood attraction, which is dependent on the relative location and features of neighboring pixels, is shown to improve the segmentation performance dramatically. The degree of attraction is optimized by a neural-network model. Simulated and real brain MR images with different noise levels are segmented to demonstrate the superiority of the proposed technique compared to other FCM-based methods. This segmentation method is a key component of an MR image-based classification system for brain tumors, currently

• http://libsearch.kture/ftplib/DM/Information_Technology_Biomed/2005_9_3/01504816.pdf

IEEE Transactions on Information Technology in Biomedicine [Электронный ресурс] [Текст]. — USA, 2005. — 3.