Edge detection for diagnosis early Alzheimer's disease by using Weibull distribution
Wafaa Kamel Al-Jibory, Ali El‐Zaart · 2013
Alzheimer's disease (AD) is the most common form of dementia among older people. Dementia is a brain disorder that seriously affects a person's ability to carry out daily activities. Several reasons makes the early diagnosis in Alzheimer's Disease important. One of them is that it allows treatments for Alzheimer's disease that help slow its progression. In patients with Alzheimer's disease, the CT scan shows a degree of generalized cerebral atrophy. Thus, analysis the CT scan images are very important in early diagnosis in Alzheimer's Disease. Image processing uses for detecting for objects of CT images. Edge detection; which is a method of determining the discontinuities gray level images, is a very important initial step in Image processing. Many classical edge detectors have been developed over time. Some of the well-known edge detection operators based on the first derivative of the image are Roberts, Prewitt, Sobel which are traditionally implemented by convolving the image with masks. Also Gaussian distribution has been used to build masks for the first and second derivative. However, this distribution has a limit to only symmetric shape. This paper will use to construct the masks. The Weibull distribution which was more general than Gaussian because it has symmetric and asymmetric shape. The constructed masks are applied to images and we obtained good results.