Abstract—According to WHO (World Health Organization) report, because of chest diseases more than 12 million death cases are reported during the year 2008. If chest disease is detected in its early stage then the possibility of surviving the patient is more. One of the most preferred scan for the early detection of chest disease is X-ray scan. X-ray is inexpensive, painless and required less time to generate image. X-ray image contain a lot of irrelevant information and has intensity problems, which makes the task of locating and analyzing suspicious area difficult by the doctor. By using pre-processing and segmentation technique, suspicious area is easily separate out from the rest of the structure. In the present paper, segmentation techniques and the segmentation results after applying on the X-ray images are discussed. In segmentation, image is partition into a meaningful region. The result of image segmentation is a set of segments that collectively cover the entire image and all pixel in the segmented region which are similar with respect to some characteristic or computed property such as color, intensity, texture etc. In present paper, some of the segmentation techniques such as edge detection, thresholding, skeletonization, contour and watershed transform are applied on the chest X-ray image and the effectiveness of each techniques are shown with the help of images and properties extracted.

Index Terms—Anatomical, contour, segmentation, skeletonization, thresholding.

Manoj R. Tarambale is with Marathwada Mitra Mandal‟s college of Engineering, Pune, Pin-411052, University of Pune, India (e-mail: manoj_tarambale@ yahoo.com).
Nitin S. Lingayat is with Electrical Department, DR. Babasaheb Ambedkar Technological University‟s Institute of Petrochemical Engineering, Lonere, Pin–402103 India (e-mail: nslingayat@yahoo.com).

 

Cite:Manoj R. Tarambale and Nitin S. Lingayat, "Computer Based Performance Evaluation of Segmentation Methods for Chest X-Ray Image," International Journal of Bioscience, Biochemistry and Bioinformatics vol. 3, no. 6, pp. 545-551, 2013.