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ALES: an assistive system for fundus image readersAuthors: rangrej.bharat ,Jayanthi Sivaswamy Journal: Journal of Medical Imaging (link) Volume Number: 4, Issue: 02 Date: 2017-04-15 Report no: IIIT/TR/2017/41 AbstractComputer assisted diagnosis (CAD) tools are of interest as they enable efficient decision making in clinics and screening of diseases. Traditional approach to CAD algorithm design focuses on automated detection of abnormalities independent of the end-user who can be an image reader or an expert. We propose a novel, reader-centric system design wherein a readers 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. 1 The CNN is used to fine-tune standard low-level filters and learn new 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. Full article: pdf Centre for Visual Information Technology |
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