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Cluster Based Performance Analysis of Pet Scan Image for Alzheimer's Disease
Imaging plays a central role in the diagnosis and treatment planning of brain neurodegenerative disorders such as Alzheimer's disease, Parkinson and more. Currently, Positron Emission Tomography (PET) is an important tool to identify the functional activities of brain metabolism. An accurate segmentation is critical, especially when the brain functional changes are difficult to assess by clinical examination. Basically, in clinical environment segmentation is done by manually. However, segmentation in PET brain images has many challenges with regards to characteristics of an image, which is solved by preprocessing step. Here, segmentation is performed using the process of clustering. Clustering means the homogeneous intensity particles are grouped together. The intensity value is compared with threshold value. This paper presents a cluster based performance analysis of PET scan image for Alzheimer's disease. Here, Euclidean distance and Chebyshev distance measure is compared to identify the suitable K value for clustering. It has demonstrated its effectiveness by testing it for real world patient data sets. The experiment was compiled in MATLAB environment and an experimental result supports the comparative study.
Distance Measure, Euclidean Distance, Chebyshev Distance, Clustering, Pet Image, Alzheimer’s Disease
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