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Proceedings Paper

A study of retrieval accuracy of pulmonary nodules based on external attachment
Author(s): Ashis Kumar Dhara; Sudipta Mukhopadhyay; Shrikant A. Mehre; Niranjan Khandelwal; Nidhi Prabhakar; Mandeep Garg; Naveen Kalra
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Paper Abstract

In this paper, retrieval accuracy of different types of pulmonary nodules is studied. The trainee radiologists could enrich their knowledge using the visual information of the retrieved nodules. In the proposed retrieval system, the pulmonary nodules are segmented using a semi-automated technique. Several 3D features are explored to improve the performance of the proposed retrieval system. A set of relevant shape and texture features is determined for efficient representation of the nodules in the feature space. The proposed CBIR system is validated on a data set of 542 nodules of Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI). The nodules with composite rank of malignancy “1”, “2” are considered as benign and “4”, “5” are considered as malignant. Considering top five retrieved images, the precision of the proposed retrieval system are 84.76%, 80.75%, and 80.34% for well-circumscribed, juxta-pleural, and juxtavascular nodules, respectively.

Paper Details

Date Published: 3 March 2017
PDF: 6 pages
Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343T (3 March 2017); doi: 10.1117/12.2254249
Show Author Affiliations
Ashis Kumar Dhara, Indian Institute of Technology Kharagpur (India)
Sudipta Mukhopadhyay, Indian Institute of Technology Kharagpur (India)
Shrikant A. Mehre, Indian Institute of Technology Kharagpur (India)
Niranjan Khandelwal, Postgraduate Institute of Medical Education & Research (India)
Nidhi Prabhakar, Postgraduate Institute of Medical Education & Research (India)
Mandeep Garg, Postgraduate Institute of Medical Education & Research (India)
Naveen Kalra, Postgraduate Institute of Medical Education & Research (India)


Published in SPIE Proceedings Vol. 10134:
Medical Imaging 2017: Computer-Aided Diagnosis
Samuel G. Armato; Nicholas A. Petrick, Editor(s)

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