A case study was conducted of the slicing of 14 cerebroma PET images

of which interesting parts were divided

with a calculation of their volume features followed. Firstly

all pixels in these images were extracted

and then classified according to the self-organizing neural network

the input node of which was determined as 2×2 through repeated experiments. By marking each element point through the self-organizing neural network

it is easier and more accurate to extract images and to calculate the number of elements within the target zone. With the use of Matlab

it built the three-dimension chart of the target zone within the 14 slicings. Finally

the volume of target areas was calculated by using the integral principle. Experimental results show this theory is a plausible one with effective outcome

which can offer assistance in clinical diagnosis.