Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption

B. Shoban Babu*, S.Varadarajan**, S. Swarnalatha***
* Associate Professor, Department of Electronics and Communication Engineering, SVCET, Chittoor, India.
** Secretary, Telangana State Council of Higher Education, Tirupati, India.
*** Associate Professor, Department of Electronics and Communication Engineering, SVUCE, Tirupati, India.
DOI : https://doi.org/

Abstract

Medical image processing is the most challenging topic in the research field. Brain tumour is a serious life altering disease condition. Image segmentation plays a significant role in the estimation of suspicious regions from the medical images. In this paper, two algorithms have been proposed; one algorithm for image enhancement and the other algorithm for image segmentation. The MRI image wherein the brain tumour is to be detected, is enhanced using the proposed technique called Power Constrained Contrast Enhancement (PCCE). Thus, the obtained enhanced image is subjected to image segmentation using threshold point method, to detect the brain tumour. The performance of the proposed method is analysed by computing the parameters of the brain tumour like volume, area, Contrast, Emissive display, mean and standard deviation. These parameter values are compared with the earlier methods, and it found that the performance of the proposed technique of brain tumour detection is quite satisfactory over the earlier existing techniques.

Keywords

Contrast Enhancement, Emissive Displays, Histogram Equalization (HE), Histogram Modification (HM), Log Based Histogram Modification (LHM), Low-Power Image Processing, Power Constraint Contrast Enhancement (PCCE), Total Dissipation Power, Image Segmentation, Thresholding Approach, Features.

How to Cite this Article?

Babu, B. S., Varadarajan, S., and Swarnalatha, S. (2016). Contrast Enhancement based Brain Tumour MRI Image Segmentation and Detection with Low Power Consumption. i-manager's Journal on Image Processing, 3(2), 18-25. https://doi.org/

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