Computer-Aided Diagnosis

Assistive lesion-emphasis system: an assistive system for fundus image readers

[+] Author Affiliations
Samrudhdhi B. Rangrej, Jayanthi Sivaswamy

International Institute of Information Technology, Hyderabad, India

J. Med. Imag. 4(2), 024503 (May 24, 2017). doi:10.1117/1.JMI.4.2.024503
History: Received January 5, 2017; Accepted May 8, 2017
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Abstract.  Computer-assisted diagnostic (CAD) tools are of interest as they enable efficient decision-making in clinics and the screening of diseases. The traditional approach to CAD algorithm design focuses on the automated detection of abnormalities independent of the end-user, who can be an image reader or an expert. We propose a reader-centric system design wherein a reader’s attention is drawn to abnormal regions in a least-obtrusive yet effective manner, using saliency-based emphasis of abnormalities and without altering the appearance of the background tissues. We present an assistive lesion-emphasis system (ALES) based on the above idea, for fundus image-based diabetic retinopathy diagnosis. Lesion-saliency is learnt using a convolutional neural network (CNN), inspired by the saliency model of Itti and Koch. The CNN is used to fine-tune standard low-level filters and learn high-level filters for deriving a lesion-saliency map, which is then used to perform lesion-emphasis via a spatially variant version of gamma correction. The proposed system has been evaluated on public datasets and benchmarked against other saliency models. It was found to outperform other saliency models by 6% to 30% and boost the contrast-to-noise ratio of lesions by more than 30%. Results of a perceptual study also underscore the effectiveness and, hence, the potential of ALES as an assistive tool for readers.

© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Samrudhdhi B. Rangrej and Jayanthi Sivaswamy
"Assistive lesion-emphasis system: an assistive system for fundus image readers", J. Med. Imag. 4(2), 024503 (May 24, 2017). ; http://dx.doi.org/10.1117/1.JMI.4.2.024503


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