Abstract: Lung disorders have become really common in today’s world due to growing amount of air pollution, our increased exposure to harmful radiations and our unhealthy lifestyles. Hence, the diagnosis of lung disorders has become of paramount importance. The commonly used Thresholding approaches and morphological operations often fail to detect the peripheral pathology bearing areas. Hence, we present the segmentation approach of the lung tissue for computer aided diagnosis system. We use a novel technique for segmentation of lungs from CT scan (Computed Tomography) of the chest or upper torso. The accuracy of analysis and its implication majorly depends on the kind of segmentation technique used. Hence, it is important that the method used is highly reliable and is successful in nodule detection and classification. We use MATLAB and OpenCV libraries to apply segmentation on CT scan images to get the desired output. We have also created a working proprietary user interface called “PULMONIS” for the ease of doctors and patients to upload the CT scan images and get the output after the image processing is done in the backend. Keywords: Lung nodule detection, Image Processing, Computed Tomography, Image Segmentation, Lung Cancer, Contour Segmentation, MATLAB, OpenCV, Computer Vision.
Weakly supervised semantic segmentation of tomographic images in the diagnosis of stroke
