Spatial Fuzzy C Means PET Image Segmentation of Neurodegenerative Disorder

TL;DR

This paper introduces a novel spatial Fuzzy C Means clustering algorithm tailored for PET images to improve the localization of neurodegenerative disorders like Alzheimer's, demonstrating superior performance over traditional methods.

Contribution

The paper presents a new PET SFCM clustering algorithm that incorporates spatial neighborhood information, enhancing segmentation accuracy in neurodegenerative disorder imaging.

Findings

PET SFCM outperforms conventional FCM and K Means in accuracy

Effective in localizing neurodegenerative disease regions

Validated on real patient datasets

Abstract

Nuclear image has emerged as a promising research work in medical field. Images from different modality meet its own challenge. Positron Emission Tomography (PET) image may help to precisely localize disease to assist in planning the right treatment for each case and saving valuable time. In this paper, a novel approach of Spatial Fuzzy C Means (PET SFCM) clustering algorithm is introduced on PET scan image datasets. The proposed algorithm is incorporated the spatial neighborhood information with traditional FCM and updating the objective function of each cluster. This algorithm is implemented and tested on huge data collection of patients with brain neuro degenerative disorder such as Alzheimers disease. It has demonstrated its effectiveness by testing it for real world patient data sets. Experimental results are compared with conventional FCM and K Means clustering algorithm. The performance of the PET SFCM provides satisfactory results compared with other two algorithms