Segmentation of Alzheimers Disease in PET scan datasets using MATLAB
This work addresses the need for automated segmentation in medical imaging for Alzheimer's Disease, but it is incremental as it applies existing clustering methods to a specific dataset.
The paper tackled the problem of automating segmentation of Alzheimer's Disease in PET scan images by implementing K-Means and Fuzzy C-Means clustering algorithms in MATLAB, using sample data from the ADNI dataset and comparing results with MIPAV tools.
Positron Emission Tomography (PET) scan images are one of the bio medical imaging techniques similar to that of MRI scan images but PET scan images are helpful in finding the development of tumors.The PET scan images requires expertise in the segmentation where clustering plays an important role in the automation process.The segmentation of such images is manual to automate the process clustering is used.Clustering is commonly known as unsupervised learning process of n dimensional data sets are clustered into k groups so as to maximize the inter cluster similarity and to minimize the intra cluster similarity.This paper is proposed to implement the commonly used K Means and Fuzzy CMeans (FCM) clustering algorithm.This work is implemented using MATrix LABoratory (MATLAB) and tested with sample PET scan image. The sample data is collected from Alzheimers Disease Neuro imaging Initiative ADNI. Medical Image Processing and Visualization Tool (MIPAV) are used to compare the resultant images.