DocumentCode :
631767
Title :
Lung nodule detection in CT images using neuro fuzzy classifier
Author :
Tariq, Anum ; Akram, M. Usman ; Javed, M. Younus
Author_Institution :
Dept. of Comput. Eng., Nat. Univ. of Sci. & Technol., Islamabad, Pakistan
fYear :
2013
fDate :
16-19 April 2013
Firstpage :
49
Lastpage :
53
Abstract :
Automated lung cancer detection using computer aided diagnosis (CAD) is an important area in clinical applications. As the manual nodule detection is very time consuming and costly so computerized systems can be helpful for this purpose. In this paper, we propose a computerized system for lung nodule detection in CT scan images. The automated system consists of two stages i.e. lung segmentation and enhancement, feature extraction and classification. The segmentation process will result in separating lung tissue from rest of the image, and only the lung tissues under examination are considered as candidate regions for detecting malignant nodules in lung portion. A feature vector for possible abnormal regions is calculated and regions are classified using neuro fuzzy classifier. It is a fully automatic system that does not require any manual intervention and experimental results show the validity of our system.
Keywords :
cancer; computerised tomography; feature extraction; fuzzy neural nets; image classification; image enhancement; image segmentation; lung; medical image processing; tumours; automated lung cancer detection; computer aided diagnosis; computerised tomography scan images; computerized systems; feature extraction; feature vector; lung classification; lung enhancement; lung nodule detection; lung segmentation; lung tissue separation; malignant nodule detection; manual nodule detection; neuro-fuzzy classifier; Biomedical imaging; Cancer; Computed tomography; Computers; Feature extraction; Image segmentation; Lungs;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence in Medical Imaging (CIMI), 2013 IEEE Fourth International Workshop on
Conference_Location :
Singapore
ISSN :
2326-991X
Print_ISBN :
978-1-4673-5919-1
Type :
conf
DOI :
10.1109/CIMI.2013.6583857
Filename :
6583857
Link To Document :
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