Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation
Medical image processing is the most challenging and emerging field of neuroscience. The ultimate goal of medical image analysis in brain MRI is to extract important clinical features that would improve methods of diagnosis & treatment of disease. This paper focuses on methods to detect &...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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EduSoft publishing
2013-10-01
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| Series: | Brain: Broad Research in Artificial Intelligence and Neuroscience |
| Subjects: | |
| Online Access: | http://brain.edusoft.ro/index.php/brain/article/view/420 |
| Summary: | Medical image processing is the most challenging and emerging field of neuroscience. The ultimate goal of medical image analysis in brain MRI is to extract important clinical features that would improve methods of diagnosis & treatment of disease. This paper focuses on methods to detect & extract brain tumour from brain MR images. MATLAB is used to design, software tool for locating brain tumor, based on unsupervised clustering methods. K-Means clustering algorithm is implemented & tested on data base of 30 images. Performance evolution of unsupervised clustering<br />methods is presented. |
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| ISSN: | 2068-0473 2067-3957 |