Detection of Liver Cancer Using Image Segmentation

Abstract

In the recent days, image processing methods are widely adopted in the medical field for enhancing the earlier detection of certain abnormalities, such as the breast cancer, lung cancer, brain cancer and so on. This paper mainly concentrates on the segmentation of liver cancer tumors from Utrasongram (USD), Computed Tomography (CT) images ,MRI images, PET Images, PET-CT and PET-MRI images. Image processing methods are adopted in segmenting the images. In the pre-processing stage mean ,median filters , prewit filters are used. In the enhancing stage power law transforamation(PLT) is used. In the image segmentation stage, Otsu's thresholding and k-Means clustering segmentation techniques are used to segment the liver images and locate the tumors. To evaluate the performance of the methods used for segmentation, the performance evaluation parameters such as Signal to noise Ratio(SNR) ,Mean Square Error (MSE) and Peak Signal Noise to Ratio (PSNR)) are computed on the segmented images of the two different segmentation methods and the different images used for segmentation. Better results are obtained for the K-Means segmentation and the PET,PET-C and PET-MRI images..