Abstract
In this article, a combination of fuzzy logic and color features-based fuzzy C means (FCM) clustering approach is proposed for segmenting the parenchyma of the computed tomography (CT) lung images. In this approach, the lung parenchyma is considered as the region of interest (ROI). This method is carried out in five steps. First step is the pre-processing of CT lung images to remove the noise and artefacts present in it. The border detection process is carried out as a second step where all the regions including the tissue and lung parenchyma is separated. Third step is the color formation process in which the image along with its border is formed in magenta color. Fourth step is the color features extraction from the obtained image border. In this process, the obtained features are given as input towards the FCM clustering method to produce a segmented foreground image from the background. This method is tested over 150 CT scans for finding the efficiency. From the experimental results, it can be observed that the proposed approach gives an overall accuracy of 96.31% for segmenting lung parenchyma.