Pulmonary Lesion Detection and Staging from CT Images Using Watershed Algorithm

Nowadays, various image processing methods are broadly being used as a part of the biomedical zones. It is crucial to diagnose the disease and to classify the specific stage for the radiologists to give reasonable remedial to the patients. Lung cancer is the most widely recognized known cancer among individuals, which can be delegated little cell and non-little cell. In this paper, we have proposed a model for the detection of pulmonary lesions at the initial and advanced stages of lung disease on CT (Computed Tomography) images. The proposed framework consists of four stages; change of RGB to grey scale image, smoothing will be performed using median filter to lessen the effect of noise from images, segmentation will be performed using thresholding and watershed techniques and after that the features are extracted for processed image. A framework has been tested with 12,645 images, a dataset of 50 patients. We have noticed that the proposed model perform better than already existing techniques and performance of this model is zero false positive acceptances.