  Positron Emission Tomography scan images are extensively used in radiotherapy
planning, clinical diagnosis, assessment of growth and treatment of a tumor.
These all rely on fidelity and speed of detection and delineation algorithm.
Despite intensive research, segmentation remained a challenging problem due to
the diverse image content, resolution, shape, and noise. This paper presents a
fast positron emission tomography tumor segmentation method in which
superpixels are extracted first from the input image. Principal component
analysis is then applied on the superpixels and also on their average. Distance
vector of each superpixel from the average is computed in principal components
coordinate system. Finally, k-means clustering is applied on distance vector to
recognize tumor and non-tumor superpixels. The proposed approach is implemented
in MATLAB 2016 which resulted in an average Dice similarity of 84.2% on the
dataset. Additionally, a very fast execution time was achieved as the number of
superpixels and the size of distance vector on which clustering was done was
very small compared to the number of raw pixels in dataset images.
