01925nas a2200193 4500008004100000020001400041245007900055210006900134260000900203300001400212490000700226520127600233100001901509700001701528700002801545700002301573700002101596856011401617 2010 eng d a1361-841500aGlaucoma Risk Index: Automated glaucoma detection from color fundus images0 aGlaucoma Risk Index Automated glaucoma detection from color fund c2010 a471 - 4810 v143 a

Glaucoma as a neurodegeneration of the optic nerve is one of themost common causes of blindness. Because revitalization of the degenerated nerve fibers of the optic nerve is impossible early detection of the disease is essential. This can be supported by a robust and automated mass-screening. We propose a novel automated glaucoma detection system that operates on inexpensive to acquire and widely used digital color fundus images. After a glaucoma specific preprocessing, different generic feature types are compressed by an appearance-based dimension reduction technique. Subsequently, a probabilistic two-stage classification scheme combines these features types to extract the novel Glaucoma Risk Index (GRI) that shows a reasonable glaucoma detection performance. On a sample set of 575 fundus images a classification accuracy of 80% has been achieved in a 5-fold cross-validation setup. The GRI gains a competitive area under ROC (AUC) of 88% compared to the established topography- based glaucoma probability score of scanning laser tomography with AUC of 87%. The proposed color fundus image-based GRI achieves a competitive and reliable detection performance on a low-priced modality by the statistical analysis of entire images of the optic nerve head.

1 aBock, Rudriger1 aMeier, Jörg1 aNyúl, László, Gábor1 aHornegger, Joachim1 aMichelson, Georg uhttps://www.inf.szte.hu/publication/glaucoma-risk-index-automated-glaucoma-detection-from-color-fundus-images03866nas a2200733 4500008004100000020001400041245008100055210006900136260027000205300001600475490000700491520149600498100002001994700002402014700002202038700002002060700001902080700002102099700001802120700002202138700002302160700001902183700002002202700001702222700001902239700002202258700002302280700001402303700002102317700002202338700002002360700002002380700002202400700002002422700002302442700002402465700002302489700002302512700001902535700001802554700001802572700001802590700001202608700001402620700002402634700002002658700002202678700002202700700002502722700002002747700002102767700002002788700002102808700002002829700002102849700002202870700002002892700001902912700002702931700002302958700002002981700001403001856011703015 2009 eng d a0278-006200aComparison and evaluation of methods for liver segmentation from CT datasets0 aComparison and evaluation of methods for liver segmentation from aPrice, K., Anything you can do, I can do better (no you can't) (1986) Comput. Vis. Graph. Image Process, 36 (2-3), pp. 387-391;S. G. Armato, G. McLennan, M. F. McNitt-Gray, C. R. Meyer, D. Yankelevitz, D. R. Aberle, C. I. Henschke, E. A. Hoffman, E. A. KacAug 2009 a1251 - 12650 v283 a

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance. Participants were also allowed to use additional proprietary training data for that purpose. All teams then had to apply their methods to 10 test datasets and submit the obtained results. Employed algorithms include statistical shape models, atlas registration, level-sets, graph-cuts and rule-based systems. All results were compared to reference segmentations five error measures that highlight different aspects of segmentation accuracy. All measures were combined according to a specific scoring system relating the obtained values to human expert variability. In general, interactive methods reached higher average scores than automatic approaches and featured a better consistency of segmentation quality. However, the best automatic methods (mainly based on statistical shape models with some additional free deformation) could compete well on the majority of test images. The study provides an insight in performance of different segmentation approaches under real-world conditions and highlights achievements and limitations of current image analysis techniques. © 2009 IEEE.

1 aHeimann, Tobias1 aVan Ginneken, Brahm1 aStyner, Martin, A1 aArzhaeva, Yulia1 aAurich, Volker1 aBauer, Christian1 aBeck, Andreas1 aBecker, Christoph1 aBeichel, Reinhardt1 aBekes, György1 aBello, Fernando1 aBinnig, Gerd1 aBischof, Horst1 aBornik, Alexander1 aCashman, Peter, MM1 aChi, Ying1 aCórdova, Andres1 aDawant, Benoit, M1 aFidrich, Márta1 aFurst, Jacob, D1 aFurukawa, Daisuke1 aGrenacher, Lars1 aHornegger, Joachim1 aKainmüller, Dagmar1 aKitney, Richard, I1 aKobatake, Hidefumi1 aLamecker, Hans1 aLange, Thomas1 aLee, Jeongjin1 aLennon, Brian1 aLi, Rui1 aLi, Senhu1 aMeinzer, Hans-Peter1 aNémeth, Gábor1 aRaicu, Daniela, S1 aRau, Anne-Mareike1 aVan Rikxoort, Eva, M1 aRousson, Mikael1 aRuskó, László1 aSaddi, Kinda, A1 aSchmidt, Günter1 aSeghers, Dieter1 aShimizu, Akinobi1 aSlagmolen, Pieter1 aSorantin, Erich1 aSoza, Grzegorz1 aSusomboon, Ruchaneewan1 aWaite, Jonathan, M1 aWimmer, Andreas1 aWolf, Ivo uhttps://www.inf.szte.hu/publication/comparison-and-evaluation-of-methods-for-liver-segmentation-from-ct-datasets03174nas a2200193 4500008004100000020001400041245011300055210006900168260000900237300000800246490000700254520246200261100001902723700001702742700002802759700002302787700002102810856014902831 2009 eng d a0146-040400aMultimodal Automated Glaucoma Detection Combining the Glaucoma Probability Score and the Glaucoma Risk Index0 aMultimodal Automated Glaucoma Detection Combining the Glaucoma P c2009 a3240 v503 a

Purpose:Fundus camera and Heidelberg Retina Tomograph (HRT) arecommonly used for reliable glaucoma diagnosis. Quantitative glaucoma scores, however, do not utilize both image content simultaneously. We propose the combination of topography and fundus image based indices for automated glaucoma detection which outperforms their sole application of either. Methods:The probabilistic values of topography based Glaucoma Probability Score (GPS) and our fundus image based Glaucoma Risk Index (GRI) are assembled to a two-dimensional feature space. In contrast to established methods the subsequent application of a probabilistic nu-Support Vector Machine classifier (nu = 0.5, kernel: radial basis function) uses both the topographic and the textural information to determine a final glaucoma probability. Instances labeled with a final probability greater than 0.5 are considered glaucomatous.For the evaluations in a 10-fold cross- validation setup, we took a sample set (mean age: 55.4 ± 10.9 years) of papilla images of 149 glaucomatous patients (FDT test time 67.4 ± 35.6 s) and 246 normals from the Erlangen Glaucoma Registry. The gold standard diagnosis was given by a glaucoma specialist based on an elaborate ophthalmological examination with ophthalmoscopy, visual field, IOP, FDT, and HRT II. The GPS was calculated by HRT device while papilla centered color fundus images (Kowa non-myd, FOV 22°) were used to calculate the GRI. Results:The classification of the GRI resulted in an area under ROC curve (AUC) of 0.81 with an F-measure of 0.71 for glaucomatous cases and 0.83 for normals. The GPS achieved an AUC of 0.86 while the F-measure for glaucoma was 0.74 (F-measure for healthy was 0.84).The combination of both indices clearly increased the AUC by 4% up to 0.9 compared to the sole application of the GPS. The F-measure for glaucomatous images was improved up to 0.76 (F-measure for healthy images was 0.86). Conclusions:The proposed combination of the topography based GPS and the fundus image based GRI shows superior performance compared to either index alone.Both indices utilize complementary information about the glaucoma disease. Consequently, this multimodal combined application of both indices is promising to reach a more reliable automated glaucoma detection performance. The approach can be used in large screening applications where an automated tool is essential to support the experts in finding glaucomatous eyes.

1 aBock, Rudriger1 aMeier, Jörg1 aNyúl, László, Gábor1 aHornegger, Joachim1 aMichelson, Georg uhttps://www.inf.szte.hu/publication/multimodal-automated-glaucoma-detection-combining-the-glaucoma-probability-score-and-the-glaucoma-risk-index02689nas a2200193 4500008004100000020001400041245006300055210006300118260000900181300000900190490000700199520208200206100001902288700001702307700002802324700002302352700002102375856009902396 2008 eng d a0146-040400aAutomated Glaucoma Detection From Color Fundus Photographs0 aAutomated Glaucoma Detection From Color Fundus Photographs c2008 a18630 v493 a

Purpose:The presentation of a novel fully automated system thatseparates glaucomatous from healthy cases based on digital fundus images. Methods:A pre-processing step eliminates certain disease independent variations such as illumination inhomogeneities, papilla size differences and vessel structures from the input images. In order to characterize glaucomatous changes, generic feature types (pixel intensities, frequency coefficients, histogram parameters, Gabor textures, spline coefficients) are extracted. In contrast to existing approaches, each feature vector is compressed by Principal Component Analysis. The classification of the transformed features is done by a state- of-the-art nu-Support Vector Machine.For the elaborate experimental evaluation of the proposed system architecture we took a large set of papilla-centered color fundus images of 100 glaucoma patients (FDT test time 67.25 ± 33.4 s) and 100 normals (overall mean age 57.0 ± 10.0 years) from the Erlangen Glaucoma Registry (Kowa non-myd, FOV 22,5°). The gold standard was given by an experienced ophthalmologist based on a complete ophthalmological examination with ophthalmoscopy, visual field, IOP, FDT, and HRT II. Results:Classification of compressed raw pixel intensities gained a success rate of 83% with a specificity of 0.72 and a sensitivity of 0.94 to detect glaucomatous cases. A success rate of 86% was achieved by using spline coefficients with a specificity of 0.78 and a sensitivity of 0.94 to detect glaucoma. The combination of both features slightly increased specificity to 0.82 (sensitivity = 0.92). The kappa statistic of 0.74 states a robust classification scheme. Conclusions:The proposed algorithm achieves a robust and competitive glaucoma detection rate. It is comparable to known methods applied to topographic papilla images and does not depend on segmentation-based measurements. For the first time, automated glaucoma detection is performed on color fundus images. Thus, fundus photography is an appropriate modality for computer-assisted glaucoma screening.

1 aBock, Rudriger1 aMeier, Jörg1 aNyúl, László, Gábor1 aHornegger, Joachim1 aMichelson, Georg uhttps://www.inf.szte.hu/publication/automated-glaucoma-detection-from-color-fundus-photographs02750nas a2200205 4500008004100000020001400041245012500055210006900180260001900249300000900268490000700277520197900284100001702263700001902280700001402299700002802313700002302341700002102364856015902385 2008 eng d a0146-040400aThe Erlanger Glaucoma Matrix - A Visualization Approach Towards Optimal Glaucomatous Optic Nerve Head Image Presentation0 aErlanger Glaucoma Matrix A Visualization Approach Towards Optima bArvocMay 2008 a18930 v493 a

Purpose:Presentation of a two-dimensional visualization approachfor intuitive and reliable glaucoma diagnosis and for setting a current observation into a relationship with pre-diagnosed data. Methods:We present a new matrix visualization technique for digital optic nerve head images. The matrix is filled with 300 pre-diagnosed reference images which show different papilla sizes and varying stages of glaucoma disease. In matrix rows the samples range from healthy ones to advanced glaucoma cases. In matrix columns the papillas are ordered by the size of the optic nerve head. The approach generalizes such that the samples can be ordered by additional criteria, too, e. g. subjects' age or anamnestic risk factors. Furthermore arbitrary image modalities and image numbers can be incorporated. Results:The glaucoma classification of a single image is difficult even for experts. Our proposed visualization provides an intuitive way for neighborhood comparisons of optic nerve head images. It allows to evaluate an image in the context of given pre-diagnosed reference samples. By the two-dimensional presentation one can study disease-dependent changes separate from other variations. Glaucoma progression can be observed separated from size variations. Thus, it supports diagnosis even in problematic cases such as macropapillas. The trustworthiness of physicians' diagnosis can be improved. Conclusions:Our approach gives insights on glaucomatous optic nerve appearance in relation to varying papilla sizes. The novel visualization of a single image within the context of other images is considered as an important tool for learning and training medical glaucoma detection. This approach visualizes computer calculated risk estimations by presenting the result within context of given gold-standard images. In contrast to pure classification systems our method does not come up with a hard decision but explains the relationship to similar pre- diagnosed cases.

1 aMeier, Jörg1 aBock, Rudriger1 aForman, C1 aNyúl, László, Gábor1 aHornegger, Joachim1 aMichelson, Georg uhttps://www.inf.szte.hu/publication/the-erlanger-glaucoma-matrix-a-visualization-approach-towards-optimal-glaucomatous-optic-nerve-head-image-presentation01524nas a2200205 4500008004100000245010500041210006900146260004900215300001400264520073300278100001701011700001901028700002801047700002301075700002101098700001601119700002201135700002001157856014101177 2008 eng d00aNovel Visualization Approach of an Automated Image Based Glaucoma Risk Index for Intuitive Diagnosis0 aNovel Visualization Approach of an Automated Image Based Glaucom aBrnobBrno University of Technologyc2008/// a205 - 2093 a

Glaucoma is one of the most common causes for blindnessworldwide. Screening is adequate to detect glaucoma at an early stage. Although it is supported by computer assisted tools no further information from former clinical studies is incorporated. We devised a novel visualization tool that presents additional comparative image data for the diagnosis process. Automated computation of a glaucoma risk index on color fundus photographs is used to initially position an undiagnosed image in reference data. The index achieves a competitive glaucoma detection rate. The combination of the automated risk index and the new visualization technique is an important tool towards a faster and more reliable diagnosis of glaucoma.

1 aMeier, Jörg1 aBock, Rudriger1 aNyúl, László, Gábor1 aHornegger, Joachim1 aMichelson, Georg1 aJan, Jiří1 aKonzuplik, Jiří1 aProvazník, Ivo uhttps://www.inf.szte.hu/publication/novel-visualization-approach-of-an-automated-image-based-glaucoma-risk-index-for-intuitive-diagnosis01805nas a2200289 4500008004100000245008600041210006900127260007000196300001400266490000600280520081400286100001901100700001701119700002801136700002201164700002101186700002301207700002301230700001901253700002101272700002301293700001801316700002201334700001801356700001901374856012201393 2007 eng d00aAppearance-based Approach to Extract an Age-related Biomarker from Retinal Images0 aAppearancebased Approach to Extract an Agerelated Biomarker from aErlangenbFriedrich-Alexander University Erlangen-Nurembergc2007 a127 - 1310 v13 a

We present an appearance-based method that extracts a new age-related biomarker from retina images. The Principal Component Analysis is applied on intensity values of the illumination corrected green channel of fundus images. The algorithm does not use segmentation, is robust and shows a high range of reliability. It identified an age-related feature with a strong influence of the temporal parapapillary area and the optic nerve head. The feature correlates with chronological age of the participants and is significantly influenced by the appearance of cardiovascular risk factors such as smoking and hypertension, and thus it can be designated a biomarker. We extract and validate a medical parameter from retina images applying a purely data-driven approach without using any prior knowledge.

1 aBock, Rudriger1 aMeier, Jörg1 aNyúl, László, Gábor1 aWärntges, Simone1 aMichelson, Georg1 aHornegger, Joachim1 aHornegger, Joachim1 aMayr, Ernst, W1 aSchookin, Sergey1 aFeußner, Hubertus1 aNavab, Nassir1 aGulyaev, Yuri, V.1 aHöller, Kurt1 aGanzha, Victor uhttps://www.inf.szte.hu/publication/appearance-based-approach-to-extract-an-age-related-biomarker-from-retinal-images02145nas a2200229 4500008004100000020002200041022001400063245007500077210006900152260004200221300001400263520135500277100001901632700001701651700002101668700002801689700002301717700002301740700002301763700001801786856011101804 2007 eng d a978-3-540-74933-2 a0302-974300aClassifying Glaucoma with Image-based Features from Fundus Photographs0 aClassifying Glaucoma with Imagebased Features from Fundus Photog aHeidelbergbSpringer VerlagcSep 2007 a355 - 3643 a

Glaucoma is one of the most common causes of blindness and it isbecoming even more important considering the ageing society. Because healing of died retinal nerve fibers is not possible early detection and prevention is essential. Robust, automated mass-screening will help to extend the symptom-free life of affected patients. We devised a novel, automated, appearance based glaucoma classification system that does not depend on segmentation based measurements. Our purely data-driven approach is applicable in large-scale screening examinations. It applies a standard pattern recognition pipeline with a 2-stage classification step. Several types of image-based features were analyzed and are combined to capture glaucomatous structures. Certain disease independent variations such as illumination inhomogeneities, size differences, and vessel structures are eliminated in the preprocessing phase. The “vessel-free” images and intermediate results of the methods are novel representations of the data for the physicians that may provide new insight into and help to better understand glaucoma. Our system achieves 86 % success rate on a data set containing a mixture of 200 real images of healthy and glaucomatous eyes. The performance of the system is comparable to human medical experts in detecting glaucomatous retina fundus images.

1 aBock, Rudriger1 aMeier, Jörg1 aMichelson, Georg1 aNyúl, László, Gábor1 aHornegger, Joachim1 aHamprecht, Fred, A1 aSchnorr, Christoph1 aJähne, Bernd uhttps://www.inf.szte.hu/publication/classifying-glaucoma-with-image-based-features-from-fundus-photographs01870nas a2200229 4500008004100000020002200041022001400063245009100077210006900168260005000237300001400287520104100301100001701342700001901359700002101378700002801399700002301427700002501450700001901475700001901494856012701513 2007 eng d a978-3-540-74271-5 a0302-974300aEffects of Preprocessing Eye Fundus Images on Appearance Based Glaucoma Classification0 aEffects of Preprocessing Eye Fundus Images on Appearance Based G aBerlin; HeidelbergbSpringer VerlagcAug 2007 a165 - 1723 a

Early detection of glaucoma is essential for preventing one ofthe most common causes of blindness. Our research is focused on a novel automated classification system based on image features from fundus photographs which does not depend on structure segmentation or prior expert knowledge. Our new data driven approach that needs no manual assistance achieves an accuracy of detecting glaucomatous retina fundus images compareable to human experts. In this paper, we study image preprocessing methods to provide better input for more reliable automated glaucoma detection. We reduce disease independent variations without removing information that discriminates between images of healthy and glaucomatous eyes. In particular, nonuniform illumination is corrected, blood vessels are inpainted and the region of interest is normalized before feature extraction and subsequent classification. The effect of these steps was evaluated using principal component analysis for dimension reduction and support vector machine as classifier.

1 aMeier, Jörg1 aBock, Rudriger1 aMichelson, Georg1 aNyúl, László, Gábor1 aHornegger, Joachim1 aKropatsch, Walter, G1 aKampel, Martin1 aHanbury, Allan uhttps://www.inf.szte.hu/publication/effects-of-preprocessing-eye-fundus-images-on-appearance-based-glaucoma-classification02486nas a2200193 4500008004100000020001400041245009500055210006900150260000900219300000900228490000700237520180400244100002102048700002202069700001902091700002802110700002302138856013102161 2007 eng d a0146-040400aExtraction of an Age-Related Biomarker From Retinal Images Using Appearance Based Approach0 aExtraction of an AgeRelated Biomarker From Retinal Images Using  c2007 a21670 v483 a

Purpose:To develop an appropriate algorithm from retina imagesusing an appearance-based version of the Principal Component Analysis and to test the age-related biomarker’s significance for patients at cardiovascular risk. Methods:Sixty-five men (age, 44.2 ± 11.4 years) and 60 women (age, 48.8 ± 12.6 years) without cardiovascular risk factors and without pathologic eye diagnosis were acquired during a clinical non-experimental cross-sectional survey and represented the control group. Forty-four hypertensive men (age, 45.5 ± 9.4 years; hypertensive for 5.9 ± 6.7 years) and 26 hypertensive women (age, 51.2 ± 7.3 years; hypertensive for 7.9 ± 7.1 years) as well as 57 male smokers (age, 41.8 ± 8.5 years; smoking for 20.6 ± 9.8 years; 15.3 ± 8.6 cigarettes per day) and 60 female smokers (age, 43.2 ± 9.5 years; smoking for 20.1 ± 10.7 years; 13.5 ± 8.1 cigarettes per day) were matched for age and sex to the respective number of control subjects. Results:The reliability of the algorithm was 0.958. The retinal biomarker correlated with age (men, -0.284, p = 0.017; women, -0.374, p = 0.001). Smokers showed a lower biomarker value (male, -0.16 ± 1.29; female, -0.12 ± 0.11) than age-matched control subjects (male, 0.72 ± 0.92, p < 0.001; female, 0.24 ± 0.98, p = 0.048). Hypertension had a similar influence to the biomarker in men (0.10 ± 0.84), but not in women (-0.46 ± 1.23) as compared to age-matched controls (male, 0.57 ± 0.95, p = 0.01; female, 0.06 ± 0.99, p = 0.09). Conclusions:The algorithm of the appearance-based version of the Principal Component Analysis identified an age-related image feature dependent on light intensity with a strong influence to the temporal parapapillary area. It may be used to identify patients at cardiovascular risk.

1 aMichelson, Georg1 aWärntges, Simone1 aBock, Rudriger1 aNyúl, László, Gábor1 aHornegger, Joachim uhttps://www.inf.szte.hu/publication/extraction-of-an-age-related-biomarker-from-retinal-images-using-appearance-based-approach00529nas a2200145 4500008004100000245005600041210005600097260003000153100001900183700001700202700002800219700002100247700002300268856009200291 2007 eng d00aRetina Image Analysis System for Glaucoma Detection0 aRetina Image Analysis System for Glaucoma Detection aAachen, GermanycSep 20071 aBock, Rudriger1 aMeier, Jörg1 aNyúl, László, Gábor1 aMichelson, Georg1 aHornegger, Joachim uhttps://www.inf.szte.hu/publication/retina-image-analysis-system-for-glaucoma-detection02102nas a2200253 4500008004100000020002200041022001400063245004500077210004500122260005100167300001400218520131100232100002001543700002801563700002001591700001801611700002301629700002201652700002801674700001901702700002401721700002201745856008101767 2007 eng d a978-3-540-71090-5 a1431-472X00aWhole Body MRI Intensity Standardization0 aWhole Body MRI Intensity Standardization aMünchen, GermanybSpringer-VerlagcMarch 2007 a459 - 4633 a

A major problem of segmentation of magnetic resonance images isthat intensities are not standardized like in computed tomography. This article deals with the correction of inter volume intensity differences that lead to a missing anatomical meaning of the observed gray values. We present a method for MRI intensity standardization of whole body MRI scans. The approach is based on the alignment of a learned reference and the current histogram. Each of these histograms is at least 2-d and represents two or more MRI sequences (e.g., T1- and T2-weighted images). From the matching a non-linear correction function is gained which describes a mapping between the intensity spaces and consequently adapts the image statistics to a known standard. As the proposed intensity standardization is based on the statistics of the data sets only, it is independent from spatial coherences or prior segmentations of the reference and newly acquired images. Furthermore, it is not designed for a particular application, body region or acquisition protocol. The method was evaluated on whole body MRI scans containing data sets acquired by T1/FL2D and T2/TIRM sequences. In order to demonstrate the applicability, examples from noisy and pathological image series acquired on a whole body MRI scanner are given.

1 aJäger, Florian1 aNyúl, László, Gábor1 aFrericks, Bernd1 aWacker, Frank1 aHornegger, Joachim1 aHorsch, Alexander1 aDeserno, Thomas, Martin1 aHandels, Heinz1 aMeinzer, Hans-Peter1 aTolxdorff, Thomas uhttps://www.inf.szte.hu/publication/whole-body-mri-intensity-standardization