Figures -using Particle Swarm Optimization (PSO)-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
| Main Authors: | M. Anitha, Prof. P. Tamije Selvy (SG) |
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| Format: | info Image |
| Bahasa: | eng |
| Terbitan: |
, 2012
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| Subjects: | |
| Online Access: |
https://www.edusoft.ro/brain/index.php/brain/issue/view/21 |
Daftar Isi:
- The performance of WML quantification is evaluated using clustering algorithms. When the image is pre-processed, contrast of the image is enhanced. The resulting enhanced image is clustered using the effective clustering algorithms. Figure 3 represents the input image for WML detection. In order to increase robustness, the noisy medical image is pre-processed. Figure 4 depicts the pre-processed image. Bright contrast stretching, which is one of the image enhancement (pre-processing) techniques is applied. After pre-processing the enhanced image is subjected to clustering. Three clustering models are proposed to provide accurate results. All scans obtained from different image clustering models are manually ranked based on values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO) are applied on a dataset of 208 images and ranking is done in terms of under detected, over detected, properly detected as shown in figure 8 and figure 9. The number of images detected properly in GFCM is comparatively high than FCM and GPC.