ABSTRACT

Image segmentation plays a vital role in medical image processing. Eventually, the proposed work is subjected to classify the tumour and non-tumour parts, followed by the segmentation of tumour region in PET scan images. Lung cancer has been the largest cause of cancer deaths. This paper focuses on Fuzzy C means algorithm for Lung tumour part segmentation of PET scan images to diagnose accurately the region of cancer. A PET scan can often detect cellular level metabolic changes at the earliest, whereas a CT or MRI detect changes a little later as the disease begins to cause changes in the structure of organs or tissues. Cancerous tumours are usually more active, have a higher metabolic rate than normal tissue, and appear differently on a PET scan. It has been shown that effective and automatic segmentation can be achieved with this method for lung and area for segmented tumour part is calculated.