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Title:	Medical images enhancement based on adaptive histogram equalization
Authors:	Hanan Saleh S. Ahmed (P47943)
Supervisor:	Jan Bin Nordin, Prof.
Keywords:	Medical images enhancement Adaptive histogram equalization Image processing -- Digital techniques
Issue Date:	28-Mar-2011
Description:	Nowadays applications require several kinds of images for perception and analysis. In addition, an image is converted from one shape to another such as, scanning, storing, digitizing, etc. some of 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. Image enhancement (IE) is essentially improving the perception or interpretation of image information for supplying better input data for other image processing techniques. 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 destroyed by several types of noises. In this research, 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. The proposed techniques start by the median filter for removing noise on images followed by unsharp mask filter which is believably the most usual type of sharpening. An UnSharp Mask (USM) does not create extra information or details, but it can basically enhance the appearing of detail by increasing small-scale acutance. medical images are 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 is used to amend contrast in images. CLAHE and histogram equalization (HE) are different in that CLAHE calculates all histograms and uses them for redistributing the light values of the image, ordinary histogram equalization applies one histogram for an entire image. For testing purposes, different sizes and various types of medical images were used and more than 50 images in different parts of the body. From the experts’ evaluation, they noted that the enhanced images have improved significantly compared with the original images. Therefore, our work leads to improve the visually information in the diagnostic medical images. The combined algorithms result increases the visibleness of relatively details without distorting the image by comparing with Global Histogram Equalization (GHE).,Master/Sarjana
Pages:	100
Call Number:	TA1637.A375 2011
Publisher:	UKM, Bangi
URI:	https://ptsldigital.ukm.my/jspui/handle/123456789/476130
Appears in Collections:	Faculty of Information Science and Technology / Fakulti Teknologi dan Sains Maklumat

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