Open Access

ARTICLE

A Post-Processing Algorithm for Boosting Contrast of MRI Images

by B. Priestly Shan¹, O. Jeba Shiney¹, Sharzeel Saleem², V. Rajinikanth³, Atef Zaguia⁴, Dilbag Singh^5,*

1 Department of Electronics & Communication Engineering, Chandigarh University, Mohali, 140413, India
2 Department of Electrical, Electronics & Communication Engineering, Galgotias University, Greater Noida, 201310, India
3 Department of Electronics and Instrumentation Engineering, St. Joseph's College of Engineering, Chennai, 600119, Tamil Nadu, India
4 Department of Computer Science, College of Computers and Information Technology, Taif University, Taif, 21944, Saudi Arabia
5 School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju, 61005, Korea

* Corresponding Author: Dilbag Singh. Email:

Computers, Materials & Continua 2022, 72(2), 2749-2763. https://doi.org/10.32604/cmc.2022.023057

Received 26 August 2021; Accepted 16 November 2021; Issue published 29 March 2022

Abstract

Low contrast of Magnetic Resonance (MR) images limits the visibility of subtle structures and adversely affects the outcome of both subjective and automated diagnosis. State-of-the-art contrast boosting techniques intolerably alter inherent features of MR images. Drastic changes in brightness features, induced by post-processing are not appreciated in medical imaging as the grey level values have certain diagnostic meanings. To overcome these issues this paper proposes an algorithm that enhance the contrast of MR images while preserving the underlying features as well. This method termed as Power-law and Logarithmic Modification-based Histogram Equalization (PLMHE) partitions the histogram of the image into two sub histograms after a power-law transformation and a log compression. After a modification intended for improving the dispersion of the sub-histograms and subsequent normalization, cumulative histograms are computed. Enhanced grey level values are computed from the resultant cumulative histograms. The performance of the PLMHE algorithm is compared with traditional histogram equalization based algorithms and it has been observed from the results that PLMHE can boost the image contrast without causing dynamic range compression, a significant change in mean brightness, and contrast-overshoot.

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Keywords

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