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Analysis of Segmentation Algorithms for Lung CT Images

One of the most serious diseases is in today’s world is lung cancer, accounting for the majority of deaths each year. Image processing in the medical field has a considerable influence on the identification of lung cancers using the computer tomography (CT) images. Image segmentation is a technique for selecting the region of interest which helps to get the exact lung region. Despite the use of many image processing and machine learning methods for lung cancer recognition, the best way for accurate detection that improves accuracy and boosts a patient's endurance rate remains to be developed. To detect tumor accurately the exact lung region, need to be extracted. This paper attempts to compare various segmentation algorithms and analyze them to prove the best segmentation method. The proposed method consists of filtering an image, segmentation of filtered image, comparison of segmented image and ground truth image. To denoise and improve the image quality, an efficient median filter is applied. The filtered image is then segmented using various segmentations algorithms such as the Fuzzy C Means Clustering, Thresholding, K means Clustering and OTSU’s segmentation Algorithm. Considering the experimental results, it is concluded that thresholding segmentation method obtained improved segmentation performance for images which was 80.9604% for accuracy, 84.3595% for precision, 79.7482% for recall and 57.3742% for F1-score compared to other segmentation methods. Hence it is concluded that thresholding segmentation method segments the lung CT images accurately compared to other segmentation methods.