A Segmentation Technique To Detect The Alzheimer's Disease Using Image Processing

Alzheimer's disease is a neurological disorder in which the death of brain cells causes memory Loss and cognitive decline. A neurodegenerative type of dementia, the disease starts mild and gets progressively worse. An important area under medical research is Brain image analysis, results to detect brain diseases. The main causes for Alzheimer's diseases is Low brain activity and blood flow. In general Segmentation technique is using for the medical images. One of the important component of the brain is Hippocampus. The normal behavior of human beings is depends on the functionality of Hippocampus. Manual Segmentation by a specialist on the Hippocampus takes many hours. In image processing there are various techniques available for segmentation process. In this paper a modified approach based on the watershed algorithm is used for segmenting the hippocampus region. The brain images converted into binary form using two approaches. The first approach is block mean, mask and Labeling concepts and in the second approach top hat, mask and Labeling concepts. However it is found that some part of the image contains holes which interrupt the segmentation process. To overcome this problem image hole filling techniques are implemented and related components are grouped into connected components. The shape analysis of hippocampus structure will result in classifying the Alzheimer's disease.