Intensity-based statistical features for classification of lungs CT scan nodules using artificial intelligence techniques

• Published in: Journal of experimental & theoretical artificial intelligence Vol. 27; no. 6; pp. 737 - 751
• Main Authors: Akram, Sheeraz, Javed, Muhammad Younus, Hussain, Ayyaz, Riaz, Farhan, Usman Akram, M.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 02.11.2015; Taylor & Francis Ltd
• Subjects: Artificial intelligence; Classification; Computer aided design; computer-aided diagnostic (CAD) system; computerised tomographic images; Expert systems; Feature extraction; Lungs; Medical diagnosis; Medical imaging; nodule classification; Nodules; pulmonary nodules detection; statistical features for intensity values; Statistics; support vector machine (SVM); Support vector machines
• ISSN: 0952-813X; 1362-3079
• DOI: 10.1080/0952813X.2015.1020526

A computer-aided diagnostic (CAD) system for effective and accurate pulmonary nodule detection is required to detect the nodules at early stage. This paper proposed a novel technique to detect and classify pulmonary nodules based on statistical features for intensity values using support vector machine (SVM). The significance of the proposed technique is, it uses the nodules features in 2D & 3D and also SVM for the classification that is good to classify the nodules extracted from the image. The lung volume is extracted from Lung CT using thresholding, background removal, hole-filling and contour correction of lung lobe. The candidate nodules are extracted and pruned using the rules based on ground truth of nodules. The statistical features for intensity values are extracted from candidate nodules. The nodule data are up-samples to reduce the biasness. The classifier SVM is trained using data samples. The efficiency of proposed CAD system is tested and evaluated using Lung Image Consortium Database (LIDC) that is standard data-set used in CAD Systems for Lungs Nodule classification. The results obtained from proposed CAD system are good as compare to previous CAD systems. The sensitivity of 96.31% is achieved in the proposed CAD system.