Feature extraction and LDA based classification of lung nodules in chest CT scan images

• Published in: 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI) pp. 1189 - 1193
• Main Authors: Aggarwal, Taruna, Furqan, Asna, Kalra, Kunal
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.08.2015
• Subjects: Accuracy; Cancer; Chest CT images; Computed tomography; Diseases; Feature extraction; Geometrical features; GLCM; Gray level characteristics; histogram based threshold; Image segmentation; Lungs; Nodule detection; Statistical features
• ISBN: 9781479987900; 1479987905
• DOI: 10.1109/ICACCI.2015.7275773

This paper presents a computational based system for detection and classification of lung nodules from chest CT scan images. In this study we consider the case of a primary lung cancer. Optimal thresholding and gray level characteristics are used for segmentation of lung nodules from the lung volume area. After detection of lung mass tissue, geometrical features are extracted. Simple image processing techniques like filtering, morphological operation etc. are used on CT images collected from Cancer Imaging Archive database to make the study effective and efficient. To distinguish between the nodule and normal pulmonary structure, geometrical features are merged with LDA (linear discriminate analysis) classifier. GLCM technique is used for calculating statistical features. The results show that proposed methodology successfully detects and provides prior classification of nodules and normal anatomy structure effectively, based on geometrical, statistical and gray level characteristics. Results also provide 84 % accuracy, 97.14 % sensitivity and 53.33 % specificity.