An Adaptive Fuzzy Rule Based Approach with Laplacian Gaussian Filtering for Screening of Chest CT Scans

Chronic Obstructive Pulmonary Disease (COPD) is a name that refers to two lung diseases; they are chronic bronchitis and emphysema. The name COPD is used since both diseases are characterized by impediment to airflow that interferes with normal breathing and the two frequently co-exist with each other. Many researchers have developed different techniques to improve the performance of automatic screening process. In this paper, first the input image is pre-processed; the lung region is segmented from that image, segmented the cavity region in that lung region, extracted some features for training the classifier and used the FRB classifier to identify the COPD affected lung. The preprocessing is done by using the gaussian filter and the lung segmentation is done by comparing the region growing technique and the Local Gabor XOR pattern (LGXP) based region growing technique. The cavity segmentation is done by evaluating the pixel range in the segmented lung region and setting a threshold value from that evaluated pixels and comparing every pixel with that threshold value. After the lung and cavity segmentation, some parameters are chosen to train the classifier to identify whether an x-ray image is a normal or affected. The classifier used in proposed technique is FRB classifier. The FRB Classifier is then trained using the parameters chosen from the sample chest CT scan images to identify the normal lung and tuberculosis affected lung.