International Journal of Information Technology & Computer Science ( IJITCS )
Image segmentation plays a vital role in medical imaging applications. Many image segmentation methods have been proposed for the process of successive image analysis tasks in last decades. Segmentation of pulmonary Chest Computer Tomography (CT) images is a precursor to most pulmonary image analysis applications. In this paper an automated lung image segmentation system for the identification of lungs using contectual clustering method is presented. Matlab software ‘regionprops’ function has been used as one of the criteria to show performance of CC. The CC segmentaion shows more segmented objects with least discontuinty within the objects in the CT lung image. From the experimental results, it has been proved that the Contextual clustering method shows a better segmentation result when compared to other conventional segmentation methods
: Contextual clustering, segmentation performance, CT lung image
- Hansell DM. Imaging the lungs with computed tomography. IEEE Engineering in Medicine and Biology Magazine 2000;19(5):71–9.
- Way T, Chan HP, Hadjiiski L, Sahiner B, Chughtai A, Song TK, et al. Computer aided diagnosis of lung nodules on CT scans: ROC study of its effect on radiologists’ performance. Academic Radiology 2010;17(3):323–32.
- Ye X, Lin X, Beddoe G, Dehmeshki J. Efficient computer-aided detection of ground-glass opacity nodules in thoracic CT images. Conf Proc IEEE Eng Med Biol Soc 2007;2007:4449–52.
- Golosio B, Masala GL, Piccioli A, Oliva P, Carpinelli M, Cataldo R, et al. A novel multi-threshold method for nodule detection in lung CT. Medical Physics 2009;36(8):3607–18.
- Yao J, Dwyer A, Summers RM, Mollura DJ. Computer-aided diagnosis of pulmonary infections using texture analysis and support vector machine classification. Academic Radiology 2011;18(3):306–14.
- Giger ML, Chan HP, Boone J. Anniversary paper: history and status of CAD and quantitative image analysis: the role of medical physics and AAPM. Medical Physics 2008;35(12):5799–820.
- Ulas¸ Ba˘gcı, Mike Bray, Jesus Caban, Jianhua Yao, Daniel J. Mollura, Computer-assisted detection of infectious lung diseases: A review, Computerized Medical Imaging and Graphics 36 (2012) 72– 84
- Hansell DM, Bankier AA, MacMahon H, McLoud TC, Muller NL, Remy J. Fleischner society: glossary of terms tor thoracic imaging. Radiology, 2008;246(3):697–722.
- Lee S. L. A. · Kouzani A. Z. · Hu E. J.,Automated detection of lung nodules in computed tomography images: a review, Machine Vision and Applications DOI 10.1007/s00138-010-0271-2
- Diciotti, S., Picozzi, G., Falchini, M., et al. : 3D segmentation algorithm of small lung nodules in spiral CT images. IEEE Trans. Inf. Technol. Biomedical 12, 7–19 (2008).
- Ochs, R.A., Goldin, J.G., Fereidoun, A., et al.: Automated classification of lung bronchovascular anatomy in CT using Adaboost. Med. Image Anal. 11, 315–324 (2007)
- Ozekes S., Camurcu A.Y.: Automatic lung nodule detection using template matching. In: Yakhno T. E. N. (ed.) Lecture Notes in Computer Science, vol. 4243. pp. 247–253 (2006)
- Kim, H., Nakashima, T., Itai, Y., et al.: Automatic detection of ground glass opacity from the thoracic MDCT images by using density features. In: International Conference on Control, Automation and Systems, pp. 1274–1277. IEEE Xplore,COEX, Seoul, Korea (2007)