  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
