Content-based image retrieval for interstitial lung diseases using classification confidence

Jatindra Kumar Dash; Sudipta Mukhopadhyay; Nidhi Prabhakar; Mandeep Garg; Niranjan Khandelwal

doi:10.1117/12.2006832

28 February 2013 Content-based image retrieval for interstitial lung diseases using classification confidence

Jatindra Kumar Dash, Sudipta Mukhopadhyay, Nidhi Prabhakar, Mandeep Garg, Niranjan Khandelwal

Proceedings Volume 8670, Medical Imaging 2013: Computer-Aided Diagnosis; 86702Y (2013) https://doi.org/10.1117/12.2006832
Event: SPIE Medical Imaging, 2013, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

Content Based Image Retrieval (CBIR) system could exploit the wealth of High-Resolution Computed Tomography (HRCT) data stored in the archive by finding similar images to assist radiologists for self learning and differential diagnosis of Interstitial Lung Diseases (ILDs). HRCT findings of ILDs are classified into several categories (e.g. consolidation, emphysema, ground glass, nodular etc.) based on their texture like appearances. Therefore, analysis of ILDs is considered as a texture analysis problem. Many approaches have been proposed for CBIR of lung images using texture as primitive visual content. This paper presents a new approach to CBIR for ILDs. The proposed approach makes use of a trained neural network (NN) to find the output class label of query image. The degree of confidence of the NN classifier is analyzed using Naive Bayes classifier that dynamically takes a decision on the size of the search space to be used for retrieval. The proposed approach is compared with three simple distance based and one classifier based texture retrieval approaches. Experimental results show that the proposed technique achieved highest average percentage precision of 92.60% with lowest standard deviation of 20.82%.

Citation Download Citation

Jatindra Kumar Dash, Sudipta Mukhopadhyay, Nidhi Prabhakar, Mandeep Garg, and Niranjan Khandelwal "Content-based image retrieval for interstitial lung diseases using classification confidence", Proc. SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 86702Y (28 February 2013); https://doi.org/10.1117/12.2006832

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