@article{Akintunde_Adelabu_Taiwo_2019, title={ENHANCEMENT TECHNIQUES FOR ABDOMINAL ORGANS’ MAGNETIC RESONANCE IMAGING}, volume={7}, url={http://ujmst.unilag.edu.ng/article/view/551}, abstractNote={<p>Medical images which carry important information about the human body most times appear with<br>low visual quality; hence, it is often strenuous to detect and extract adequate information from them.<br>Information extracted can lead to right or wrong diagnosis and prognosis. To obtain optimum<br>images for accurate diagnosis therefore, these images must pass through an enhancement process<br>which consists of a series of methods that aim at improving their visual assessment. Magnetic<br>Resonance Imaging (MRI) produces most of the important medical images of which the raw data<br>acquired can be corrupted by several types of noise and artefacts. This paper aims at improving the<br>quality of abdominal MR images in the spatial domain. The method began with the application of<br>the median filter for the noise reduction. Thereafter, the filtered image was further enhanced with<br>Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and a<br>proposed technique; the Power Law Transformed Adaptive Histogram Equalization (PLTAHE). A<br>sharpening effect was applied using the unsharp mask filter for the result. The results of the applied<br>techniques were compared side by side for the best enhancement effect, and an objective assessment<br>was carried out on by evaluating the Peak Signal to Noise Ratio (PSNR) and Structural Similarity<br>Index Measure (SSIM) values of the images. A subjective assessment was also carried out with a<br>radiologist who gave a better perception to the effects of the enhancement techniques on the samples<br>studied. The results of the study showed that the proposed technique Power Law Transformed<br>Adaptive Histogram Equalization produced the best contrast for the analysed MR images.</p&gt;}, number={2}, journal={UNILAG Journal of Medicine, Science and Technology}, author={Akintunde , I. A. and Adelabu , M. A and Taiwo , Y. F}, year={2019}, month={Dec.}, pages={99- 118} }