Tuberculosis: Image Segmentation Approach Using OpenCV
Tuberculosis (TB) is one of the major disease spreading whole over the world. TB caused by bacteria known as Mycobacterium tuberculosis. Nowadays, TB is increasing widely in the region of Karachi and now it’s becoming a challenging task for all researchers. The process is to partitioning digital image into different segments according to the set of pixels is known as image segmentation. It’s used to identify segments & extract meaningful information of an image. Image segmentation approaches are providing new ways in the field of medical and it’s exactly suitable for TB images, block-based & layer-based segmentation which helps to find edge, thresholding, regional growth, clustering, water shading, erosion & dilation, utilizing histogram for the betterment of TB patients. Chest X-ray is playing a vital role to diagnose TB. X-ray contains two colors, foreground and background that’s why the overall work depends on binary coloring. It’s helping to identify symptoms and intensity of TB in a patient. The purpose is to write this research, to reduce the ratio of TB patients in Karachi region by using image segmentation approaches (edge detection, thresholding, reginal growth etc.) on chest X-ray and calculates the alternative way to detect the intensity level of TB in individual patient’s report with effectively, efficiently & accurately with minimum amount of time by using Python OpenCV.
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The SJCMS is licensed under Creative Commons Attribution-NonCommercial 4.0 International License.