| Autor: |
Babu, K. Rajesh, Nagajaneyulu, P.V., Prasad, K. Satya |
| Zdroj: |
International Journal of Signal and Imaging Systems Engineering; 2020, Vol. 12 Issue: 1 p62-70, 9p |
| Abstrakt: |
Early diagnosis of a brain tumour may increase life expectancy. Magnetic resonance imaging (MRI) accompanied by several segmentation algorithms is preferred as a reliable method for assessment. In this study, first noise removed by median filter and dimensionality of datasets reduced by using random projection transformation (RPT). Next, the pre-processed images are clustered by using K-means and fuzzy c-means (FCM). In the very next step, the clustered images multi-features are fused by different data fusion approaches, and then segment the exact tumour area by using the active contour models such as level set method (LSM) and Chan-Vese (C-V). The performance of clustered based segmentation and fusion-based segmentation in terms of various fusion metrics. The results of both clustered based and fusion-based methods revealed that the CNN fusion-based segmentation performs better than clustered- based segmentation to detect the tumour with low segmentation error and minimal loss of information. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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