Unsupervised learning‐based clustering approach for smart identification of pathologies and segmentation of tissues in brain magnetic resonance imaging. Issue 4 (23rd March 2019)

• Record Type: Journal Article
• Authors: Vigneshwaran, S.; Govindaraj, Vishnuvarthanan; Murugan, Pallikonda R.; Zhang, Yudong; Arun Prasath, Thiyagarajan
• Is Part Of: International journal of imaging systems and technology. Volume 29:Issue 4(2019)
• Page Start: 439
• Page End: 456
• Publication Date: 2019-03-23
• Subjects: medical image analysis -- modified fuzzy K‐means -- self‐organizing map -- tissue segmentation -- tumors and lesion identification; Imaging systems -- Periodicals; Image processing -- Periodicals; 621.367
• DOI: 10.1002/ima.22321
• Languages: English
• ISSNs: 0899-9457

Abstract

Abstract: Human‐made/developed algorithms provide automatic identification and segmentation of the tissues, lesions and tumor regions available in brain magnetic resonance scan images, which invocates predicaments such as high computational cost and low accuracy rate. Such hassles are reconciled with the utilization of an unsupervised approach in combination with clustering techniques. Initially, static features are chosen from the input image, which is fed to the self‐organizing map (SOM), where the algorithm employs the dimensionality reduction of input images. Consecutively, the reduced SOM prototype of data is clustered by the modified fuzzy K‐means (MFKM) algorithm. The MFKM algorithm can be modified in terms of membership variables because it operates with spatial information and converges quickly, and this would be of greater benefit to radiologists as they reduce the wrong predictions and voluminous time that normally occur owing to human involvement. The proposed algorithm provides 98.77% sensitivity and 97.5% specificity, which are better than any other traditional algorithms mentioned in this article.