An automated detection and classification approach in MRI tumor diagnosis

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 second observing factor in region segmentation is the marking of small region patterns which are derived due to misclassification of actual and detected regions. the complexity of detection logic, due to recurrent coding is an additional factor to observe. In this paper, a new recurrent coding approach of region segmentation is proposed, overcoming the issue of region marking, discontinuity issue and small region miss-classification. The suggested approach is a simpler and robust to region detection, test over different MRI samples.

Authors and Affiliations

Somashekhar Swamy, P. K. Kulkarni

Keywords

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  • EP ID EP392528
  • DOI 10.9790/9622-0710020110.
  • Views 97
  • Downloads 0

How To Cite

Somashekhar Swamy, P. K. Kulkarni (2017). An automated detection and classification approach in MRI tumor diagnosis. International Journal of engineering Research and Applications, 7(10), 1-10. https://europub.co.uk/articles/-A-392528