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Computer Aided Detection of Large Lung Nodules using Chest Computer Tomography Images

Mai Mabrouk, Ayat Karrar, Amr Sharawy Published in Artificial Intelligence

International Journal of Applied Information Systems
Year of Publication 2012
© 2010 by IJAIS Journal
Info Co-published with IJCA
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  1. Mai Mabrouk, Ayat Karrar and Amr Sharawy. Article: Computer Aided Detection of Large Lung Nodules using Chest Computer Tomography Images. International Journal of Applied Information Systems 3(9):12-18, August 2012. BibTeX

    	author = "Mai Mabrouk and Ayat Karrar and Amr Sharawy",
    	title = "Article: Computer Aided Detection of Large Lung Nodules using Chest Computer Tomography Images",
    	journal = "International Journal of Applied Information Systems",
    	year = 2012,
    	volume = 3,
    	number = 9,
    	pages = "12-18",
    	month = "August",
    	note = "Published by Foundation of Computer Science, New York, USA"


Lung cancer is the most common cancer which leads to death for both women and men, so the early detection of lung cancer increases the therapy success. Different techniques are used to provide the early detection such as Computer Aided Detection (CAD) system. In this paper, we present an automatic Computer Aided Detection (CAD) system to detect a large lung nodule from lateral Chest Radiographs of computed tomography (CT) images to reduce false positive rates. Basic image processing techniques such as Bit-Plane Slicing, Erosion, Median Filter, Dilation, Outlining, radon transform and edge detection are applied to the CT scan images in order to detect the lung region. A total of 22 image features were extracted from the enhanced image based on statistical features such as standard deviation, average and mean. A fisher score ranking method is used as a feature selection method to select best ten features (standard deviation, variance, range, maximum grey level, seven invariant moments except the second, sixth and seventh invariant moments and 5th percentile, 9th percentile). Thus optimal screening modalities have both high sensitivity and specificity based on artificial neural network (ANN) significantly more accurate than using K-Nearest Neighborhood (KNN) classifier with accuracy 98% and 96% respectively in detecting large lung nodule with equivalent diameter ranging from 22. 65 mm to 41. 62 mm.


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Computer Aided Diagnosis (CAD), Computed tomography (CT), Radon transform, Artificial Neural Network (ANN), K-Nearest Neighborhood (KNN)