24 May 2017 Assistive lesion-emphasis system: an assistive system for fundus image readers
Author Affiliations +
J. of Medical Imaging, 4(2), 024503 (2017). doi:10.1117/1.JMI.4.2.024503
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 (SPIE)
Samrudhdhi B. Rangrej, Jayanthi Sivaswamy, "Assistive lesion-emphasis system: an assistive system for fundus image readers," Journal of Medical Imaging 4(2), 024503 (24 May 2017). https://doi.org/10.1117/1.JMI.4.2.024503 Submission: Received 5 January 2017; Accepted 8 May 2017
Submission: Received 5 January 2017; Accepted 8 May 2017

Performance modeling

Computer aided design

Computer aided diagnosis and therapy

Focus stacking software

Convolutional neural networks

Data modeling

Detection and tracking algorithms

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