International Journal of System Signal Control and Engineering Application
ISSN: OnlineISSN: Print 1997-5422
Abstract
Medical imaging and analysis has become an integrated tool for effective and efficient management of any disease. The significance of medical imaging is diagnosing. Diseased state of human brain is huge. One such diagnosis is identification and extraction of brain tumor. The utility of any image is influenced by the quantity and quality of information that can be extracted lent of it. The processing power of humans are tremendous and they posses every complex interpreting skill. Humans are capable of performing cognitive analysis of an image. Some of the typical issues in regard to visual interpretation and manual analysis include wide difference in sense of perception between different users, human fatigue and for most of the time human one capable of providing a qualitative analysis rather than a quantifying one. A computer based image analysis accounts for most of these problems and can help in saving crucial time needed to respond to a medical emergency. Effective image processing methods can serve as potent tools that can help in an affordable and effective healthcare practices. This research work illustrates one such tool that will significantly contribute towards the analysis and the interpretation of MRI images for tumor detection and classification. An enhanced and modified Gaussian mixture mode model and the ICA segmentation approach has been employed for segmenting brain tumors in MR images.
How to cite this article:
Shaik Basheera and M. Satya Sai Ram. A Hybrid Enhanced ICA Approach for Segmentation of Brain MR Image.
DOI: https://doi.org/10.36478/ijssceapp.2019.48.58
URL: https://www.makhillpublications.co/view-article/1997-5422/ijssceapp.2019.48.58