K Means Segmentation of Alzheimers Disease in PET scan datasets: An implementation
This work addresses the need for automated segmentation in medical image analysis for Alzheimer's Disease, but it is incremental as it applies an existing method to a specific domain without major innovations.
The paper implemented K-Means clustering for segmenting Alzheimer's Disease in PET scan datasets, focusing on algorithm optimization based on performance, quality, and cluster count, using AForge .NET and MATLAB for compilation.
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)