Improved FCM Brain MRI Image Segmentation Algorithm Based on Tamura Texture Feature

• Published in: Ji suan ji ke xue Vol. 48; no. 8; pp. 111 - 117
• Main Authors: Qiao, Ying-jing, Gao, Bao-lu, Shi, Rui-xue, Liu, Xuan, Wang, Zhao-hui
• Format: Journal Article
• Language: Chinese
• Published: Chongqing Guojia Kexue Jishu Bu 01.08.2021; Editorial office of Computer Science
• Subjects: Algorithms; Brain; Clustering; Feature extraction; fuzzy clustering|magnetic resonance imaging|tamura texture information|linear fusion|initial cluster center; Image segmentation; Inhomogeneity; Magnetic resonance imaging; Medical imaging; Noise; Noise sensitivity; Texture
• ISSN: 1002-137X
• DOI: 10.11896/jsjkx.200700003

To solve the problems of noise sensitivity and initial clustering center randomness in the segmentation of brain MRI images by FCM algorithm, an improved FCM image segmentation algorithm based on Tamura texture feature is proposed.Firstly, the Tamura texture feature of the image is extracted, and it is linearly weighted with the gray feature to form a fusion feature.Then, the density of pixel is calculated by using fuzzy neighborhood relation, and the initial cluster center is selected by combining it with distance relation.Finally, the fusion feature is used as a feature constraint for updating membership and clustering center.In the experiment, FCM,D-FCM,WKFCM and the proposed method are used to segment the images in Brain Web MRI dataset, and their anti-noise performance, accuracy and operation efficiency are compared.Experimental results show that the proposed algorithm can obtain better initial clustering centers, has better robustness in processing noise and gray inhomogeneity images, and can segment br