Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/15665
Title: A Framework for Identification of Brain Tumors From Mr Images Using Progressive Segmentation
Authors: Narayana, M V
Rao, J Nageswara
Shrivastava, Sanjeev
Ghantasala, G S Pradeep
Ioannou, Iacovos
Vassiliou, Vasos
Keywords: Brain Tumor
Framework
Identification
Image Processing
Mr Images
Progressive Segmentation
Issue Date: 2024
Publisher: Health and Technology
Springer Science and Business Media Deutschland GmbH
Citation: Vol. 14, No. 3; pp. 539-556
Abstract: Purpose: This study addresses the critical health issue of brain tumors, focusing on enhancing the accuracy of tumor segmentation from Magnetic Resonance Imaging (MRI) images. The primary research question investigates the effectiveness of a novel Hybrid Watershed–Clustering framework and its underlying Progressive Segmentation of the MR Images using the Radius and Intensity Measure (PS-RIM) algorithm. The aim is to improve the detection and segmentation of brain tumors within MR images, surpassing the efficacy of current methodologies. Methods: The methodology involves a three-stage process. In the preprocessing stage, noise reduction and intensity normalization techniques are applied to clarify the images. The next stage is region-based segmentation, which includes morphological processing, edge detection, and thresholding to delineate tumor areas accurately. The final post-processing stage enhances segmentation accuracy and reduces false positives by integrating clustering machine learning techniques, specifically the K-Means cluster algorithm, to refine tumor identification. Results: The framework's comprehensive evaluation across various MR images shows a significant improvement in accuracy over existing segmentation methods. The PS-RIM algorithm within the framework effectively captures the diverse presentations of tumor appearances in MR images. The research recorded an impressive accuracy rate of 98.11% in tumor detection, demonstrating enhanced identification and segmentation quality. Conclusions: The study concludes that the proposed Hybrid Watershed–Clustering framework, powered by the PS-RIM algorithm, markedly improves the detection and differentiation of brain tumors in MR images. It exhibits exceptional accuracy, resilience, and computational efficiency. These findings hold substantial potential for advancing computer vision and image analysis in medical diagnostics, which could improve patient outcomes in managing brain tumors. Graphical abstract: (Figure presented.) © The Author(s) 2024.
URI: http://dx.doi.org/10.1007/s12553-024-00844-9
http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/15665
ISSN: 2190-7188
Appears in Collections:Journal Articles

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