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Details for article 11 of 12 found articles
| Title: |
Performance analysis of unsupervised optimal fuzzy clustering algorithm for MRI brain tumor segmentation |
| Author: |
Blessy, S.A. Praylin Selva Sulochana, C. Helen |
| Appeared in: |
Technology & health care |
| Paging: | Volume 23 (2014) nr. 1 pages 23-35 |
| Year: |
2014-11-18 |
| Contents: |
BACKGROUND: Segmentation of brain tumor from Magnetic Resonance Imaging (MRI) becomes very complicated due to the structural complexities of human brain and the presence of intensity inhomogeneities. OBJECTIVE: To propose a method that effectively segments brain tumor from MR images and to evaluate the performance of unsupervised optimal fuzzy clustering (UOFC) algorithm for segmentation of brain tumor from MR images. METHODS: Segmentation is done by preprocessing the MR image to standardize intensity inhomogeneities followed by feature extraction, feature fusion and clustering. RESULTS: Different validation measures are used to evaluate the performance of the proposed method using different clustering algorithms. The proposed method using UOFC algorithm produces high sensitivity (96%) and low specificity (4%) compared to other clustering methods. CONCLUSIONS: Validation results clearly show that the proposed method with UOFC algorithm effectively segments brain tumor from MR images. |
| Publisher: |
IOS Press |
| Source file: |
Elektronische Wetenschappelijke Tijdschriften |
Details for article 11 of 12 found articles
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