%T A generalized contrast enhancement approach for knee MR images
%A A. Gandhamal
%A S. Talbar
%A S. Gajre
%A A.F.M. Hani
%A D. Kumar
%I Institute of Electrical and Electronics Engineers Inc.
%K Equalizers; Graphic methods; Image acquisition; Luminance; Magnetic resonance imaging; Morphology; Tissue, Contrast Enhancement; Feature similarities; Gray-level transform; Gray-level transformation; Histogram equalizations; Knee osteoarthritis; Morphological changes; Quantitative measurement, Joints (anatomy)
%X Knee Osteoarthritis (OA) is a most prevalent joint disease that can be diagnosed by measuring physiology and morphology of knee joint organs using Magnetic Resonance Imaging (MRI). Measurement of morphological changes in the knee joint organs is a highly challenging task as it requires interpretation and analysis from MR images acquired using different MR pulse sequences. In general, most knee MR images acquired in clinical routines exhibit insignificant tissue contrast and low background luminance in the regions presenting different tissues. This results in the difficulties for further processing and quantitative measurement. In this paper, a method for contrast enhancement in knee MR images acquired using different MR pulse sequences is presented. Local Gray Level Transformation using S-curve technique that has originated from original Gray Level Transform is developed and tested on 6 different knee MR image sequences. The performance of the developed technique is measured by calculating Enhancement Measure (EME), Feature Similarity Index Measure (FSIM) and Absolute Mean Brightness Error (AMBE) and comparing results with existing methods such as Histogram Equalization (THE) and Bi-histogram Based Histogram Equalization (BBHE). Results show significant improvements over existing methods in resolving improper brightness and contrast distribution issues that will contribute to the development of quantitative methods for morphological assessment of knee joint osteoarthritis. © 2016 IEEE.
%D 2017
%R 10.1109/ICONSIP.2016.7857499
%O cited By 6; Conference of 2016 International Conference on Signal and Information Processing, IConSIP 2016 ; Conference Date: 6 October 2016 Through 8 October 2016; Conference Code:126490
%L scholars8834
%J 2016 International Conference on Signal and Information Processing, IConSIP 2016