Kedir M. Adal

Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning

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.

Statistical atlas based exudate segmentation

Diabetic macular edema (DME) is characterized by hard exudates. In this article, we propose a novel statistical atlas based method for segmentation of such exudates. Any test fundus image is first warped on the atlas co-ordinate and then a distance map is obtained with the mean atlas image. This leaves behind the candidate lesions. Post-processing schemes are introduced for final segmentation of the exudate. Experiments with the publicly available HEI-MED data-set shows good performance of the method. A lesion localization fraction of 82.5% at 35% of non-lesion localization fraction on the FROC curve is obtained. The method is also compared to few most recent reference methods.

A Hierarchical Coarse-to-Fine Approach for Fundus Image Registration

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.

Automated detection of microaneurysms using robust blob descriptors

Microaneurysms (MAs) are among the first signs of diabetic retinopathy (DR) that can be seen as round dark-red structures in digital color fundus photographs of retina. In recent years, automated computer-aided detection and diagnosis (CAD) of MAs has attracted many researchers due to its low-cost and versatile nature. In this paper, the MA detection problem is modeled as finding interest points from a given image and several interest point descriptors are introduced and integrated with machine learning techniques to detect MAs. The proposed approach starts by applying a novel fundus image contrast enhancement technique using Singular Value Decomposition (SVD) of fundus images. Then, Hessian-based candidate selection algorithm is applied to extract image regions which are more likely to be MAs. For each candidate region, robust low-level blob descriptors such as Speeded Up Robust Features (SURF) and Intensity Normalized Radon Transform are extracted to characterize candidate MA regions. The combined features are then classified using SVM which has been trained using ten manually annotated training images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. Preliminary results show the competitiveness of the proposed candidate selection techniques against state-of-the art methods as well as the promising future for the proposed descriptors to be used in the localization of MAs from fundus images.

Accuracy assessment of intra- and intervisit fundus image registration for diabetic retinopathy screening.

PURPOSE We evaluated the accuracy of a recently developed fundus image registration method (Weighted Vasculature Registration, or WeVaR) and compared it to 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 2012 and 2013 for a DR 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 subsequently were evaluated and ranked by two expert graders to assess the registration accuracy. RESULTS Merge Eye Care PACS had high registration failure rates compared to WeVaR and i2k Retina (P = 8 × 10(-6) and P = 0.002, respectively). WeVaR showed significantly higher registration accuracy than i2k Retina in intravisit (P ≤ 0.0036) and intervisit (P ≤ 0.0002) mosaics. Therefore, fundus mosaics processed by WeVaR were more likely to have a higher score (odds ratio [OR] = 2.5, P = 10(-5) for intravisit and OR = 2.2, P = 0.006 for intervisit mosaics). WeVaR was preferred more often by the graders than i2k Retina (OR = 6.1, P = 7 × 10(-6)). CONCLUSIONS WeVaR produced intra- and intervisit 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.

Exudate segmentation on retinal atlas space

Diabetic macular edema is characterized by hard exudates. Presence of such exudates cause vision loss in the affected areas. We present a novel approach of segmenting exudates for screening and follow-ups by building an ethnicity based statistical atlas. The chromatic distribution in such an atlas gives a good measure of probability of the pixels belonging to the healthy retinal pigments or to the abnormalities (like lesions, imaging artifacts etc.) in the retinal fundus image. Post-processing schemes are introduced in this paper for the enhancement of the edges of such exudates for final segmentation and to separate lesion from false positives. A sensitivity(recall) of 82.5 % at 35% of positive predictive value on FROC-curve is achieved. Results are obtained on a publicly available HEI-MED dataset and have been compared to two reference methods on the same dataset showing the competitiveness of the proposed algorithm.

Steerable wavelet transform for atlas based retinal lesion segmentation

Diabetic macular edema (DME) characterized by discrete white{yellow lipid deposits due to vascular leakage is one of the most severe complication seen in diabetic patients that cause vision loss in affected areas. Such vascular leakage can be treated by laser surgery. A regular follow{up and laser photocoagulation can reduce the risk of blindness by 90%. In an automated retina screening system, it is thus very crucial to make the segmentation of such hard exudates accurate and register these images taken over time to a reference co-ordinate system to make the necessary follow-ups more precise. We introduce a novel method of ethnicity based statistical atlas for exudates segmentation and follow-up. Ethnic background plays a significant role in retinal pigment epithelium, visibility of the choroidal vasculature and overall retinal luminance in patients and retinal images. Such statistical atlas can thus help to provide a solution, simplify the image processing steps and increase the detection rate. In this paper, bright lesion segmentation is investigated and experimentally verified for the gold standard built from African American fundus images. 40 automatically generated landmark points on the major vessel arches with macula and optic centers are used to warp the retinal images. PCA is used to obtain a mean shape of the retinal major arches (both lower and upper). The mean of the co-ordinates of the macula and optic disk center are obtained resulting 42 landmark points and together they provide a reference co-ordinate frame ( or the atlas co-ordinate frame) for the images. The retinal funds images of an ethnic group without any artifact or lesion are warped to this reference co-ordinate frame from which we obtain a mean image representing the statistical measure of the chromatic distribution of the pigments in the eye of that particular ethnic group. 400 images of African American eye has been used to build such a gold standard for this ethnic group. Any test image of the patient of that ethnic group is first warped to the reference frame and then a distance map is obtained with this mean image. Finally, the post-processing schemes are applied on the distance map image to enhance the edges of the exudates. A multi-scale and multi-directional steerable filters along with the Kirsch edge detector was found to be promising. Experiments with the publicly available HEI-MED dataset showed the good performance of the proposed method. We achieved the lesion localization fraction (LLF) of 82.5% at 35% of non{lesion localization fraction (NLF) on the FROC curve.

Non-conventional imaging systems for 3D digitization of transparent objects: Shape from polarization in the IR and shape from visible fluorescence induced UV

This paper presents a comparison between recent advances made in the field of non-conventional imaging techniques for 3D digitization of transparent object. After a survey, this paper focuses on two recent techniques later called: shape from Visible Fluorescence UV-induced and shape from polarization in the IR which recently emerged. Results obtained with the technique of Scanning from Heating which, originally developed in 2008 for the digitization of transparent objects, has successfully been modified and applied to the digitization of specular objects.

An Automated System for the Detection and Classification of Retinal Changes Due to Red Lesions in Longitudinal Fundus Images

People with diabetes mellitus need annual screening to check for the development of diabetic retinopathy (DR). Tracking small retinal changes due to early diabetic retinopathy lesions in longitudinal fundus image sets is challenging due to intra- and intervisit 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

Detection of retinal changes from illumination normalized fundus images using convolutional neural networks

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