K Means Segmentation of Alzheimers Disease in PET scan datasets: An implementation
Computer Vision and Pattern Recognition
2013-03-01 v1 Neural and Evolutionary Computing
Abstract
The Positron Emission Tomography (PET) scan image requires expertise in the segmentation where clustering algorithm plays an important role in the automation process. The algorithm optimization is concluded based on the performance, quality and number of clusters extracted. This paper is proposed to study the commonly used K Means clustering algorithm and to discuss a brief list of toolboxes for reproducing and extending works presented in medical image analysis. This work is compiled using AForge .NET framework in windows environment and MATrix LABoratory (MATLAB 7.0.1)
Keywords
Cite
@article{arxiv.1302.7082,
title = {K Means Segmentation of Alzheimers Disease in PET scan datasets: An implementation},
author = {A. Meena and K. Raja},
journal= {arXiv preprint arXiv:1302.7082},
year = {2013}
}
Comments
International Joint Conference on Advances in Signal Processing and Information Technology, SPIT2012