Improving Diagnostic Viewing of Medical Images using Enhancement Algorithms
Hanan S. Saleh Ahmed and Md Jan Nordin
DOI : 10.3844/jcssp.2011.1831.1838
Journal of Computer Science
Volume 7, Issue 12
Problem statement: Various images are low quality and difficultly to detect and extract information. Therefore, the image has to get under a process called image enhancement which contains an aggregation of techniques that look for improving the visual aspect of an image. Medical images are one of the fundamental images, because they are used in more sensitive field which is a medical field. The raw data obtained straight from devices of medical acquisition may afford a comparatively poor image quality representation and may destroy by several types of noises. Image Enhancement (IE) and denoising algorithms for executing the requirements of digital medical image enhancement is introduced. The main goal of this study is to improve features and gain better characteristics of medical images for a right diagnosis. Approach: The proposed techniques start by the median filter for removing noise on images followed by unsharp mask filter which is believable the usual type of sharpening. Medical images were usually poor quality especially in contrast. For solving this problem, we proposed Contrast Limited Adaptive Histogram Equalization (CLAHE) which is one of the techniques in a computer image processing domain. It was used to amend contrast in images. Results: For testing purposes, different sizes and various types of medical images were used and more than 60 images in different parts of the body. From the experts’ evaluation, they noted that the enhanced images improved up to 80% from the original images depends on medical images modalities. Conclusion: The proposed algorithms results were significant for increasing the visibleness of relatively details without distorting the images.
© 2011 Hanan S. Saleh Ahmed and Md Jan Nordin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.