FRFCM clustering segmentation method for medical MR image feature diagnosis

Issue title: Artificial Intelligence as a maturing and growing technology: An urgent need for intelligent systems

Guest editors: X. Yuan and M. Elhoseny

Article type: Research Article

Authors: He, Qian^{; 1} | Shao, Juwei^{; 1} | Pu, Jian | Zhou, Minjie | M, M. | Xiang, Shutian | Su, Wei^{; *}

Affiliations: Department of Radiology, The Second People’s Hospital of Yunnan Province, Kunming, PR China

Correspondence: [*] Corresponding author. Wei Su, Department of Radiology, The Second People’s Hospital of Yunnan Province, PR China. E-mail: suwei1970@163.com.

Note: [1] They are Co-first author.

Abstract: Medical image recognition is affected by characteristics such as blur and noise, which cause medical image features that cannot be effectively identified and directly affects clinical diagnostics. In order to improve the diagnostic effect of medical MR image features, based on the FRFCM clustering segmentation method, this study combines the medical MR image feature reality, collects data for traditional clustering method analysis, and sorts out the shortcomings of traditional clustering methods. Simultaneously, this study improves the traditional clustering method by combining medical image feature diagnosis requirements. In addition, this study carried out image data processing through simulation, and designed comparative experiments to analyze the performance of the algorithm. The research shows that the FRFCM combined with the intuitionistic fuzzy set proposed in this paper has greatly improved the noise immunity and segmentation performance compared with the FCM based fuzzy set.

Keywords: FRFCM clustering, feature extraction, image segmentation, medical diagnosis

DOI: 10.3233/JIFS-189327

Journal: Journal of Intelligent & Fuzzy Systems, vol. 40, no. 2, pp. 2871-2879, 2021