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A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images

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Abstract

Classification of malignant and benign pulmonary nodules is important for further treatment plan. The present work focuses on the classification of benign and malignant pulmonary nodules using support vector machine. The pulmonary nodules are segmented using a semi-automated technique, which requires only a seed point from the end user. Several shape-based, margin-based, and texture-based features are computed to represent the pulmonary nodules. A set of relevant features is determined for the efficient representation of nodules in the feature space. The proposed classification scheme is validated on a data set of 891 nodules of Lung Image Database Consortium and Image Database Resource Initiative public database. The proposed classification scheme is evaluated for three configurations such as configuration 1 (composite rank of malignancy “1” and “2” as benign and “4” and “5” as malignant), configuration 2 (composite rank of malignancy “1”,“2”, and “3” as benign and “4” and “5” as malignant), and configuration 3 (composite rank of malignancy “1” and “2” as benign and “3”,“4” and “5” as malignant). The performance of the classification is evaluated in terms of area (A z) under the receiver operating characteristic curve. The A z achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 0.9505, 0.8822, and 0.8488, respectively. The proposed method outperforms the most recent technique, which depends on the manual segmentation of pulmonary nodules by a trained radiologist.

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Authors and Affiliations

  1. Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, 721302, India

    Ashis Kumar Dhara & Sudipta Mukhopadhyay

  2. Electronics and Communication Engineering, Birla Institute of Technology Mesra, Ranchi, 835215, India

    Anirvan Dutta

  3. Department of Radio Diagnosis & Imaging, Post Graduate Institute of Medical Education and Research, Chandigarh, 160 012, India

    Mandeep Garg & Niranjan Khandelwal

Authors
  1. Ashis Kumar Dhara
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  2. Sudipta Mukhopadhyay
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  3. Anirvan Dutta
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  4. Mandeep Garg
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  5. Niranjan Khandelwal
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Corresponding author

Correspondence to Sudipta Mukhopadhyay.

Ethics declarations

This work is done using a public lung CT image data set and for this type of study formal consent is not required. This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of interests

This study was funded by Department of Electronics Technology, Govt. of India, Grant number 1(3)2009-ME&TMD and 1(2)/2013-ME &TMD/ESDA, respectively. The authors declare that they have no conflict of interest.

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Dhara, A.K., Mukhopadhyay, S., Dutta, A. et al. A Combination of Shape and Texture Features for Classification of Pulmonary Nodules in Lung CT Images. J Digit Imaging 29, 466–475 (2016). https://doi.org/10.1007/s10278-015-9857-6

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  • DOI: https://doi.org/10.1007/s10278-015-9857-6

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