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An Adaptive Learning and Classifier Model in MRI Tumor Detection
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 175 - Number 5 |
| Year of Publication: 2017 |
| Authors: Somashekhar Swamy, P. K. Kulkarni |
10.5120/ijca2017915546
|
Somashekhar Swamy, P. K. Kulkarni . An Adaptive Learning and Classifier Model in MRI Tumor Detection. International Journal of Computer Applications. 175, 5 ( Oct 2017), 32-38. DOI=10.5120/ijca2017915546
Abstract
In the process of image coding, external noises impact a lot in processing efficiency. In the application of medical image processing, this effect is more, important due to its finer content details. It is required to minimize the noise effect with preserving the image content information, without losing the image generality. Towards the objective of image denoising, in this work, a dynamic block coding approach for noise minimization in medical image processing is presented. The filtration approach is an enhancement to the objective of noise elimination using median filtration. The suggested approach, improves the retrieval accuracy more effectively under variant noise condition in consideration to conventional filtration approach.
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