Parallel Segmentation of Tumor from MRI Using Image Processing

Modern medical image analysis relies heavily on medical picture segmentation, which has important ramifications for computer-aided diagnosis and therapy planning. This research presents a novel framework that combines morphological methods, K-means clustering, and U-Net architecture for the parallel...

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Bibliographic Details
Published in:MATEC web of conferences 2024, Vol.393, p.2006
Main Authors: Kalsi, Rajveer, G, Anusooya
Format: Article
Language:English
Subjects:
Cluster analysis
Clustering
Design parameters
Image analysis
Image processing
Image segmentation
Literature reviews
Magnetic resonance imaging
medical image analysis
Medical imaging
mri
parallel segmentation
simd parallelization
tumor
Tumors
u-net architecture
Vector quantization
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Summary:Modern medical image analysis relies heavily on medical picture segmentation, which has important ramifications for computer-aided diagnosis and therapy planning. This research presents a novel framework that combines morphological methods, K-means clustering, and U-Net architecture for the parallel segmentation of brain tumours using magnetic resonance imaging (MRI). In response to issues brought up in the literature, our work includes a thorough analysis of current segmentation techniques and makes explicit links between the literature review and the suggested methodology. We go into detail about the iterative design process, explaining the results of each design iteration and how the literature influenced our approach. Important design parameters are clearly stated, illustrating the framework's evolutionary path. Our method, which makes use of Single Instruction Multiple Data (SIMD) parallelization, improves speed while maintaining accuracy. Our framework's thorough analysis of design parameters, literature influences, and design iterations establishes it as a notable development in the field of medical picture segmentation.
ISSN:2261-236X
2274-7214
2261-236X
DOI:10.1051/matecconf/202439302006