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|
|Authors : B.MAGESH,Mrs.P.VIJAYALAKSHMI,Ms. M. ABIRAMI.|
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 2231-2803.www.ijcttjournal.org. 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
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Keywordscomputer - aided diagnosis,segmentation,canny method,extraction