Kedir M. Adal’s research while affiliated with ASML and other places

People with diabetes mellitus need annual screening to check for the development of diabetic retinopathy. Tracking small retinal changes due to early diabetic retinopathy lesions in longitudinal fundus image sets is challenging due to intra- and inter-visit variability in illumination and image quality, the required high registration accuracy, and the subtle appearance of retinal lesions compared to other retinal features. This paper presents a robust and flexible approach for automated detection of longitudinal retinal changes due to small red lesions by exploiting normalized fundus images that significantly reduce illumination variations and improve the contrast of small retinal features. To detect spatio-temporal retinal changes, the absolute difference between the extremes of the multiscale blobness responses of fundus images from two time-points is proposed as a simple and effective blobness measure. DR related changes are then identified based on several intensity and shape features by a support vector machine classifier. The proposed approach was evaluated in the context of a regular diabetic retinopathy screening program involving subjects ranging from healthy (no retinal lesion) to moderate (with clinically relevant retinal lesions) DR levels. Evaluation shows that the system is able to detect retinal changes due to small red lesions with a sensitivity of 80% at an average false positive rate of 1 and 2.5 lesions per eye on small and large fields-of-view of the retina, respectively.

Purpose: To evaluate the accuracy of a recently developed fundus image registration method (Weighted Vasculature Registration or WEVAR) and to compare it with two top-ranked state-of-the-art commercial fundus mosaicking programs (i2k Retina, DualAlign LLC and Merge Eye Care PACS, formerly named OIS AutoMontage) in the context of diabetic retinopathy (DR) screening. Methods: Fundus images of 70 diabetic patients who visited the Rotterdam Eye Hospital in and for a diabetic retinopathy screening program were registered by all three programs. The registration results were used to produce mosaics from fundus photos that were normalized for luminance and contrast to improve the visibility of small details. These mosaics were subsequently evaluated and ranked by two expert graders to assess the registration accuracy. Results: Merge Eye Care PACS had high registration failure rates compared to both WEVAR and i2k Retina (p = 8 x 10-6 and p = 0.002, respectively). WEVAR showed significantly higher registration accuracy than i2k Retina in both intra-visit (p<=0.0036) and inter-visit (p<=0.0002) mosaics. Fundus mosaics processed by WEVAR were therefore more likely to have a higher score (odds ratio (OR) = 2.5, p = 10-5 for intra-visit and OR = 2.2, p = 0.006, for inter-visit mosaics). WEVAR was preferred more often by the graders than i2k Retina (OR = 6.1, p = 7 x 10-6). Conclusions: WEVAR produced intra- and inter-visit fundus mosaics with higher registration accuracy than Merge Eye Care PACS and i2k Retina. Merge Eye Care PACS had higher registration failures than the other two programs. Highly accurate registration methods such as WEVAR may potentially be used for more efficient human grading and in computer-aided screening systems for detecting DR progression. Copyright © 2015 by Association for Research in Vision and Ophthalmology.

This paper presents a method to automatically assess the accuracy of image registration. It is applicable to images in which vessels are the main landmarks such as fundus images and angiography. The method simultaneously exploits not only the position, but also the intensity profile across the vasculatures. The accuracy measure is defined as the energy of the odd component of the 1D vessel profile in the difference image divided by the total energy of the corresponding vessels in the constituting images. Scale and orientation-selective quadrature filter banks have been employed to analyze the 1D signal profiles. Subsequently, the relative energy measure has been calibrated such that the measure translates to a spatial misalignment in pixels. The method was validated on a fundus image dataset from a diabetic retinopathy screening program at the Rotterdam Eye Hospital. An evaluation showed that the proposed measure assesses the registration accuracy with a bias of -0.1 pixels and a precision (standard deviation) of 0.9 pixels. The small Fourier footprint of the orientation selective quadrature filters makes the method robust against noise.

Accurate registration of retinal fundus images is vital in computer aided diagnosis of retinal diseases. This paper presents a robust registration method that makes use of the intensity as well as structural information of the retinal vasculature. In order to correct for illumination variation between images, a normalized-convolution based luminosity and contrast normalization technique is proposed. The normalized images are then aligned based on a vasculature-weighted mean squared difference (MSD) similarity metric. To increase robustness, we designed a multiresolution matching strategy coupled with a hierarchical registration model. The latter employs a deformation model with increasing complexity to estimate the parameters of a global second-order transformation model. The method was applied to combine 400 fundus images from 100 eyes, obtained from an ongoing diabetic retinopathy screening program, into 100 mosaics. Accuracy assessment by experienced clinical experts showed that 89 (out of 100) mosaics were either free of any noticeable misalignment or have a misalignment smaller than the width of the misaligned vessel.

Abstract Despite several attempts, automated detection of microaneurysm (MA) from digital fundus images still remains to be an open issue. This is due to the subtle nature of MAs against the surrounding tissues. In this paper, the microaneurysm detection problem is modeled as finding interest regions or blobs from an image and an automatic local-scale selection technique is presented. Several scale-adapted region descriptors are introduced to characterize these blob regions. A semi-supervised based learning approach, which requires few manually annotated learning examples, is also proposed to train a classifier which can detect true MAs. The developed system is built using only few manually labeled and a large number of unlabeled retinal color fundus images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. A competition performance measure (CPM) of 0.364 shows the competitiveness of the proposed system against state-of-the art techniques as well as the applicability of the proposed features to analyze fundus images.