Segmentation of Alzheimers Disease in PET scan datasets using MATLAB

TL;DR

This paper presents an automated PET scan image segmentation method for Alzheimer's disease using K Means and Fuzzy CMeans clustering algorithms implemented in MATLAB, tested on ADNI data.

Contribution

It introduces an automated segmentation approach for Alzheimer's PET images using clustering algorithms in MATLAB, with validation against MIPAV results.

Findings

Effective segmentation of PET images demonstrated.

Clustering algorithms successfully distinguish Alzheimer's affected regions.

Comparison with MIPAV shows promising results.

Abstract

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.