LUNG CANCER DETECTION USING MORPHOLOGICAL WATERSHED OPERATIONS

Abstract

Cancer is one of the most serious and widespread disease across the world accounting for a large number of deaths every year. Lung cancer is the leading cause of cancer deaths in the world, having high mortality rate. The main problem in lung cancer is that most of these cases are diagnosed at the later stages of cancer, making treatments more problematic and reducing the survival chances. The survival rate of Cancer patients’ increment from 14-49% if the illness is recognized in time. Early discovery of lung cancer can expand the possibility of survival among individuals. A CT scan is more likely to show lung tumors than routine chest x-rays as it can show the size, shape, and position of any lung tumor and can help find enlarged lymph nodes that might contain cancer that has spread. Visual interpretation of CT scan can be difficult and error prone. Hence image processing techniques are used to detect the cancer nodules at an early stage. This paper deals with an automated approach for early detection of lung cancer. This algorithm is proposed using methods such as Median Filtering for Image Smoothing, Contrast Adjustment for enhancing the image, Segmentation using Morphological Watershed Operations and Otsu’s Thresholding. Key Words: CT, Median Filtering, Contrast Adjustment, Morphological Watershed Operations, Otsu Thresholding.