Loading...
Medical image visual appearance improvement using bihistogram bezier curve contrast enhancement: Data from the osteoarthritis initiative
Author(s)
Gan, Hong Seng
Swee, Tan Tian
Abdul Karim, Ahmad Helmy
Sayuti, Khairil Amir
Abdul Kadir, Mohammed Rafiq
Tham, Weng Kit
Wong, Liang Xuan
Chaudhary, Kashif T.
Ali, Jalil
Yupapin, Preecha P.
Date Issued
January 1, 2014
Type
Article
Abstract
Well-defined image can assist user to identify region of interest during segmentation. However, complex medical image is usually characterized by poor tissue contrast and low background luminance. The contrast improvement can lift image visual quality, but the fundamental contrast enhancement methods often overlook the sudden jump problem. In this work, the proposed bihistogram Bezier curve contrast enhancement introduces the concept of "adequate contrast enhancement" to overcome sudden jump problem in knee magnetic resonance image. Since every image produces its own intensity distribution, the adequate contrast enhancement checks on the image's maximum intensity distortion and uses intensity discrepancy reduction to generate Bezier transform curve. The proposed method improves tissue contrast and preserves pertinent knee features without compromising natural image appearance. Besides, statistical results from Fisher's Least Significant Difference test and the Duncan test have consistently indicated that the proposed method outperforms fundamental contrast enhancement methods to exalt image visual quality. As the study is limited to relatively small image database, future works will include a larger dataset with osteoarthritic images to assess the clinical effectiveness of the proposed method to facilitate the image inspection. © 2014 Hong-Seng Gan et al.
Citation
Scientific World Journal, 2014, 2014