K-Means Segmentation of Alzheimer’s Disease in Pet Scan Datasets – An Implementation

A. Meena; K. Raja

Signal Processing and Information Technology. Second International Joint Conference, SPIT 2012, Dubai, UAE, September 20-21, 2012, Revised Selected Papers

Research Article

K-Means Segmentation of Alzheimer’s Disease in Pet Scan Datasets – An Implementation

Download

387 downloads

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.1007/978-3-319-11629-7_24,
    author={A. Meena and K. Raja},
    title={K-Means Segmentation of Alzheimer’s Disease in Pet Scan Datasets -- An Implementation},
    proceedings={Signal Processing and Information Technology. Second International Joint Conference, SPIT 2012, Dubai, UAE, September 20-21, 2012, Revised Selected Papers},
    proceedings_a={SPIT},
    year={2014},
    month={11},
    keywords={Clustering K- means PET scan images AForge .NET framework MATLAB MIPAV},
    doi={10.1007/978-3-319-11629-7_24}
}

A. Meena
K. Raja
Year: 2014
K-Means Segmentation of Alzheimer’s Disease in Pet Scan Datasets – An Implementation
SPIT
Springer
DOI: 10.1007/978-3-319-11629-7_24

A. Meena¹^,*, K. Raja²^,*

1: Sathyabama University
2: Narasu’s Sarathy Institute of Technology

*Contact email: kabimeena2@hotmail.com, raja_koth@yahoo.co.in

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: Clustering K- means PET scan images AForge .NET framework MATLAB MIPAV

Published: 2014-11-28

: http://dx.doi.org/10.1007/978-3-319-11629-7_24