Computer Aided Diagnosis System For The Identification And Classification Of Lessions In Lungs

International Journal of Computer Trends and Technology (IJCTT)          
© May to June Issue 2011 by IJCTT Journal
Volume-1 Issue-2                          
Year of Publication : 2011


B.MAGESH,Mrs.P.VIJAYALAKSHMI,Ms. M. ABIRAMI. "Computer Aided Diagnosis System For The Identification And Classification Of Lessions In Lungs"International Journal of Computer Trends and Technology (IJCTT),V1(2):216-219 May to June Issue 2011 .ISSN Published by Seventh Sense Research Group.

AbstractThe Computer Aided Diagnosing (CAD) system is proposed in this project for detection of lu ng cancer form the analysis of computed tomography (CT) images. To produce a successful Computer Aided Diagnos is system, several problems has to be resolved. Segmentation is the first problem to be considered whic h helps in generation of candidate region f or detecting cancer nodules. The second problem is identification of affected nodules from all the candidate nodules. Initially, the basic image processing techniques such as Ero sion, Median Filter, Dilation, Outlining, Lung Border Extraction and Flood - Fil l algorithms are applied to the CT scan image in order to detect the lung region. Then the segmentation algorithm is applied in order to detect the c ancer nodules from the extracted lung image.After segmentation, rule based technique is applied to classify the cancer nodules. Finally, a set of diagnosis rules are generated from the extracted features. From these rules, the occurrences of cancer nodules are identifiedclearly. The learning is performed with the help of Extreme Learning Ma chine because of its better classification. For experimentation of the proposed technique, the CT images are collected fr om reputed hospital. The main objective of the project is to develop a CAD (Computer Aided Diagnosis) system fo r finding the lung fissures and lesions using the lung CT images and classify the lesions as Benign or Malignant


[1] Yamomoto. S, Jiang. H, Matsumoto. M, Tateno. Y, Iinuma. T, Matsumoto. T, “Image processing for computer - aided diagnosis of lung cancer b y CT (LSCT)”, Proceedings 3rd IEEE Workshop on Applications of Computer Vision, WACV `96, pp: 236 – 241, 1996.
[2] Yeny Yim, Helen Hong and Yeong Gil Shin, “Hybrid lung segmentation in chest CT images for computer - aided diagnosis”, Proceedings of 7th Inter national Workshop on Enterprise networking and Computing in Healthcare Industry, HEALTHCOM 2005. pp: 378 – 383, 2005.
[3] Penedo. M. G, Carreira. M. J, Mosquera. A and Cabello. D, “Computer - aided diagnosis: a neural - network - based approach to lung nodule de tection”, IEEE Transactions on Medical Imaging, vol: 17, pp: 872 – 880, 1998.
[4] Kanazawa. K, Kubo. M and Niki. N, “Computer aided diagnosis system for lung cancer based on helical CT images”, Proceedings of the 13th International Conference on P attern Recognition, pp: 381 - 385 vol.3, 1996.
[5] Yamamoto. T, Ukai. Y, Kubo. M, Niki. N, Satou. H and Eguchi. K, “Computer aided diagnosis system with functions to assist comparative reading for lung cancer based on helical CT image”, Image Processing, 2 000 International Conference on Proceedings, pp: 180 - 183 vol.1, 2000.
[6] Cheran. S. C, Gargano. G, “Computer aided diagnosis for lung CT using artificial life models”, Seventh International Symposium on Symbolic and Numeric Algorithms for Scientific Com puting, SYNASC 2005, 2005.
[7] R. Wiemker, P. Rogalla, T. Blaffert, D. Sifri, O. Hay, Y. Srinivas and R. Truyen “Computer - aided detection (CAD) and volumetry of pulmonary nodules on high - resolution CT data“, 2003.
[8] D. Lin and C. Yan, “Lung nodules ident ification rules extraction with neural fuzzy network”, IEEE, Neural Information Processing, vol. 4, 2002.
[9] S. G. Armato, M. L. Giger and H. MacMahon, “Automated detection of lung nodules in CT scans: Preliminary results”, Med. Phys., Vol. 28,2001

Keywordscomputer - aided diagnosis,segmentation,canny method,extraction