Spatial Fuzzy C Means PET Image Segmentation of Neurodegenerative Disorder
This work addresses the challenge of precise disease localization in PET imaging for neurodegenerative disorders like Alzheimer's, but it is incremental as it builds on existing clustering methods.
The authors tackled the problem of segmenting PET images for neurodegenerative disorders by introducing a Spatial Fuzzy C Means algorithm that incorporates spatial neighborhood information, achieving satisfactory results compared to conventional FCM and K-Means in tests on real-world patient datasets.
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