Fused Feature Vector and Dual FCM for Lung Segmentation from Chest X-Ray Images

Segmentation of Lung Region is an important step in the clinical diagnosis of lung diseases like Pneumonia, Tuberculosis, and COVID-19.However, most the methods employed Deep learning algorithms without providing any additional information at the segmentation phase.Hence this paper proposes a new segmentation Mechanism based on the Fuzzy C-Means (FCM) and different features.A new Variant of FCM called as Dual FCM is introduced which forms an objective function based on two different Sub-Objective Functions (SOFs).The first SOF segments the images through gray pixel intestines while the second SOF used features for segmentation.Two different features are derived namely morphological features and gradient features from every CXR image.Finally, the segmented results at individual SOFs are fused to get a final segmented result.Extensive experiments have been carried out over the proposed approach through two publicly available datasets namely Montgomery County (MC) Dataset and Shenzhen Dataset.The performance is assessed through two evaluation measures namely Jaccard Index (JI) and Dice Coefficient (DC) and the average obtained is noticed as 92.985% and 95.295% respectively.Further the proposed segmentation mechanism is compared with several past lung image segmentation approaches.The approximate improvement in the JI and DC from past methods is noticed as 4.2210% and 2.3150% respectively.