A Novel Supervised Approach for Segmentation of Lung Parenchyma from Chest CT for Computer-Aided Diagnosis
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A Novel Supervised Approach for Segmentation of Lung Parenchyma from Chest CT for Computer-Aided Diagnosis Shiloah Elizabeth Darmanayagam & Khanna Nehemiah Harichandran & Sunil Retmin Raj Cyril & Kannan Arputharaj Published online: 18 October 2012 # Society for Imaging Informatics in Medicine 2012
Abstract Segmentation of lung parenchyma from the chest computed tomography is an important task in analysis of chest computed tomography for diagnosis of lung disorders. It is a challenging task especially in the presence of peripherally placed pathology bearing regions. In this work, we propose a segmentation approach to segment lung parenchyma from chest. The first step is to segment the lungs using iterative thresholding followed by morphological operations. If the two lungs are not separated, the lung junction and its neighborhood are identified and local thresholding is applied. The second step is to extract shape features of the two lungs. The third step is to use a multilayer feed forward neural network to determine if the segmented lung parenchyma is complete, based on the extracted features. The final step is to reconstruct the two lungs in case of incomplete segmentation, by exploiting the fact that in majority of the cases, at least one of the two
S. E. Darmanayagam Department of Computer Science and Engineering, Anna University, Chennai 600 025, Tamilnadu, India S. E. Darmanayagam e-mail: [email protected] K. N. Harichandran (*) : S. R. R. Cyril Ramanujan Computing Centre, Anna University, Chennai 600 025, Tamilnadu, India e-mail: [email protected] K. N. Harichandran e-mail: [email protected] S. R. R. Cyril e-mail: [email protected] K. Arputharaj Department of Information Science and Technology, Anna University, Chennai 600 025, Tamilnadu, India K. Arputharaj e-mail: [email protected]
lungs would have been segmented correctly by the first step. Hence, the complete lung is determined based on the shape and region properties and the incomplete lung is reconstructed by applying graphical methods, namely, reflection and translation. The proposed approach has been tested in a computeraided diagnosis system for diagnosis of lung disorders, namely, bronchiectasis, tuberculosis, and pneumonia. An accuracy of 97.37 % has been achieved by the proposed approach whereas the conventional thresholding approach was unable to detect peripheral pathology-bearing regions. The results obtained prove to be better than that achieved using conventional thresholding and morphological operations. Keywords Segmentation . Lung parenchyma . Chest CT . Thresholding . Morphological operations . Multilayer feed forward neural network
Introduction Lung disorders are threatening the health of individuals all over the world especially in South-East Asia. According to a WHO report [1], an estimated 1.3 million people died from tuberculosis in 2008; the highest number of deaths was in South-East Asia and the number of new cases arising each year is still increasing globally in the WHO regions of Africa, the East Mediterra
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