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Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer


Visual information of similar nodules could assist the budding radiologists in self-learning. This paper presents a content-based image retrieval (CBIR) system for pulmonary nodules, observed in lung CT images. The reported CBIR systems of pulmonary nodules cannot be put into practice as radiologists need to draw the boundary of nodules during query formation and feature database creation. In the proposed retrieval system, the pulmonary nodules are segmented using a semi-automated technique, which requires a seed point on the nodule from the end-user. The involvement of radiologists in feature database creation is also reduced, as only a seed point is expected from radiologists instead of manual delineation of the boundary of the nodules. The performance of the retrieval system depends on the accuracy of the segmentation technique. Several 3D features are explored to improve the performance of the proposed retrieval system. A set of relevant shape and texture features are considered for efficient representation of the nodules in the feature space. The proposed CBIR system is evaluated for three configurations such as configuration-1 (composite rank of malignancy “1”,“2” as benign and “4”,“5” as malignant), configuration-2 (composite rank of malignancy “1”,“2”, “3” as benign and “4”,“5” as malignant), and configuration-3 (composite rank of malignancy “1”,“2” as benign and “3”,“4”,“5” as malignant). Considering top 5 retrieved nodules and Euclidean distance metric, the precision achieved by the proposed method for configuration-1, configuration-2, and configuration-3 are 82.14, 75.91, and 74.27 %, respectively. The performance of the proposed CBIR system is close to the most recent technique, which is dependent on radiologists for manual segmentation of nodules. A computer-aided diagnosis (CAD) system is also developed based on CBIR paradigm. Performance of the proposed CBIR-based CAD system is close to performance of the CAD system using support vector machine.

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Correspondence to Sudipta Mukhopadhyay.

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Conflict of interests

This study was funded by the Department of Electronics and Information Technology, Govt. of India, Grant number 1(2)/2013-ME &TMD/ESDA. The authors declare that they have no conflict of interest. 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. Informed consent was obtained from all individual participants included in the study.

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Dhara, A.K., Mukhopadhyay, S., Dutta, A. et al. Content-Based Image Retrieval System for Pulmonary Nodules: Assisting Radiologists in Self-Learning and Diagnosis of Lung Cancer. J Digit Imaging 30, 63–77 (2017).

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  • CT images
  • Content-based image retrieval
  • Diagnosis of lung cancer
  • Lung cancer
  • Pulmonary nodules
  • Self-learning tool of radiology
  • CBIR based CAD system