DETECTION OF LUNG CANCER USING IMAGE SEGMENTATION

In modern 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 lung cancer tumors from X-ray images, Computed Tomography (CT) images and MRI images. Image processing methods are adopted in segmenting the images. In the pre-processing stage mean and median filters are used. In the image segmentation stage, Otsu's thresholding and k-Means clustering segmentation approaches are used to segment the lung 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 used for segmentation. Better results are obtained for the K-Means segmentation irrespective of the images.