nils-herrmann/clean_scirepeval huggingface dataset:
Sparse Dissimilarity-Constrained Coding for Glaucoma Screening

Glaucoma is an irreversible chronic eye disease that leads to vision loss. As it can be slowed down through treatment, detecting the disease in time is important. However, many patients are unaware of the disease because it progresses slowly without easily noticeable symptoms. Currently, there is no effective method for low-cost population-based glaucoma detection or screening. Recent studies have shown that automated optic nerve head assessment from 2-D retinal fundus images is promising for low-cost glaucoma screening. In this paper, we propose a method for cup to disc ratio (CDR) assessment using 2-D retinal fundus images.  In the proposed method, the optic disc is first segmented and reconstructed using a novel sparse dissimilarity-constrained coding (SDC) approach which considers both the dissimilarity constraint and the sparsity constraint from a set of reference discs with known CDRs. Subsequently, the reconstruction coefficients from the SDC are used to compute the CDR for the testing disc.  The proposed method has been tested for CDR assessment in a database of 650 images with CDRs manually measured by trained professionals previously. Experimental results show an average CDR error of 0.064 and correlation coefficient of 0.67 compared with the manual CDRs, better than the state-of-the-art methods. Our proposed method has also been tested for glaucoma screening. The method achieves areas under curve of 0.83 and 0.88 on datasets of 650 and 1676 images, respectively, outperforming other methods.  The proposed method achieves good accuracy for glaucoma detection.  The method has a great potential to be used for large-scale population-based glaucoma screening.
Efficient optic cup localization using regional propagation based on retinal structure priors

We present a regional propagation approach based on retinal structure priors to localize the optic cup in 2D fundus images, which is the primary image component clinically used for identifying glaucoma. This method provides three major contributions. First, it proposes processing of the fundus images at the superpixel level, which leads to more descriptive and effective features than those employed by pixel based techniques, without additional computational cost. Second, the proposed approach does not need manually labeled training samples, but uses the structural priors on relative cup and disc positions. Third, a refinement scheme that utilizes local context information is adopted to further improve the accuracy. Tested on the ORIGA-light clinical dataset, which comprises of 325 images from a population-based study, the proposed method achieves a 34.9% non-overlap ratio with manually-labeled ground-truth and a 0.104 absolute cup-to-disc ratio (CDR) error. This level of accuracy is much higher than the state-of-the-art pixel based techniques, with a comparable or even less computational cost.
Automatic glaucoma diagnosis from fundus image

Glaucoma is currently diagnosed by glaucoma specialists using specialized imaging devices like HRT and OCT. Fundus imaging is a modality widely used in primary healthcare. An automatic glaucoma diagnosis system based on fundus image can be deployed to primary healthcare clinics and has potential for early disease diagnosis. A mass glaucoma screening program can also be facilitated using such a system. We present an automatic fundus image based cup-to-disc ratio measurement system; and demonstrate its potential for automatic objective glaucoma diagnosis and screening. It provides strong support to use fundus image as the modality for automatic glaucoma diagnosis.
Superpixel Classification Based Optic Disc and Optic Cup Segmentation for Glaucoma Screening

Glaucoma is a chronic eye disease that leads to vision loss. As it cannot be cured, detecting the disease in time is important. Current tests using intraocular pressure (IOP) are not sensitive enough for population based glaucoma screening. Optic nerve head assessment in retinal fundus images is both more promising and superior. This paper proposes optic disc and optic cup segmentation using superpixel classification for glaucoma screening. In optic disc segmentation, histograms, and center surround statistics are used to classify each superpixel as disc or non-disc. A self-assessment reliability score is computed to evaluate the quality of the automated optic disc segmentation. For optic cup segmentation, in addition to the histograms and center surround statistics, the location information is also included into the feature space to boost the performance. The proposed segmentation methods have been evaluated in a database of 650 images with optic disc and optic cup boundaries manually marked by trained professionals. Experimental results show an average overlapping error of 9.5% and 24.1% in optic disc and optic cup segmentation, respectively. The results also show an increase in overlapping error as the reliability score is reduced, which justifies the effectiveness of the self-assessment. The segmented optic disc and optic cup are then used to compute the cup to disc ratio for glaucoma screening. Our proposed method achieves areas under curve of 0.800 and 0.822 in two data sets, which is higher than other methods. The methods can be used for segmentation and glaucoma screening. The self-assessment will be used as an indicator of cases with large errors and enhance the clinical deployment of the automatic segmentation and screening.
Automated basal cell carcinoma detection in high-definition optical coherence tomography.

Basal cell carcinoma (BCC) is the most common non-melanoma skin cancer. Conventional diagnosis of BCC requires invasive biopsies. Recently, a high-definition optical coherence tomography (HD-OCT) technique has been developed, which provides a non-invasive in vivo imaging method of skin. Good agreements of BCC features between HD-OCT images and histopathological architecture have been found. Therefore it is possible to automatically detect BCC using HD-OCT. This paper presents a novel BCC detection method that consists of four steps: graph based skin surface segmentation, surface flattening, deep feature extraction and the BCC classification. The effectiveness of the proposed method is well demonstrated on a dataset of 5,040 images. It can therefore serve as an automatic tool for screening BCC.
Automated anterior chamber angle localization and glaucoma type classification in OCT images

To identify glaucoma type with OCT (optical coherence tomography) images, we present an image processing and machine learning based framework to localize and classify anterior chamber angle (ACA) accurately and efficiently. In digital OCT photographs, our method automatically localizes the ACA region, which is the primary structural image cue for clinically identifying glaucoma type. Next, visual features are extracted from this region to classify the angle as open angle (OA) or angle-closure (AC). This proposed method has three major contributions that differ from existing methods. First, the ACA localization from OCT images is fully automated and efficient for different ACA configurations. Second, it can directly classify ACA as OA/AC based on only visual features, which is different from previous work for ACA measurement that relies on clinical features. Third, it demonstrates that higher dimensional visual features outperform low dimensional clinical features in terms of angle closure classification accuracy. From tests on a clinical dataset comprising of 2048 images, the proposed method only requires 0.26s per image. The framework achieves a 0.921 ± 0.036 AUC (area under curve) value and 84.0% ± 5.7% balanced accuracy at a 85% specificity, which outperforms existing methods based on clinical features.
Automatic pupil detection on retro-illumination lens images from a population-based study

Automatic detection of the pupil is an important step in cataract assessment. This paper proposes a new pupil detection method which addresses the challenges faced in current methods employed for this task. We evaluate the performance of the proposed method against the current methods on a large population-based image dataset of more than 9000 images from the Singapore Malay Eye Study (SiMES) database. The accuracy achieved is 98.60% for the proposed method for SiMES-1.  The results are encouraging towards a fully automatic cataracts detection and assessment.
An augmented reality assistance platform for eye laser surgery

This paper presents a novel augmented reality assistance platform for eye laser surgery. The aims of the proposed system are for the application of assisting eye doctors in pre-planning as well as providing guidance and protection during laser surgery. We developed algorithms to automatically register multi-modal images, detect macula and optic disc regions, and demarcate these as protected areas from laser surgery. The doctor will then be able to plan the laser treatment pre-surgery using the registered images and segmented regions. Thereafter, during live surgery, the system will automatically register and track the slit lamp video frames on the registered retina images, send appropriate warning when the laser is near protected areas, and disable the laser function when it points into the protected areas. The proposed system prototype can help doctors to speed up laser surgery with confidence without fearing that they may unintentionally fire laser in the protected areas.
ACHIKO-K: Database of fundus images from glaucoma patients

Developing image-based automatic glaucoma diagnosis systems requires a large amount of fundus image groundtruth data, especially that from glaucomatous patients. We develop ACHIKO-K database which contains 258 manually annotated retinal images and their clinical diagnosis information. All images in ACHIKO-K are taken from glaucoma patients and contain rich information on glaucoma related pathological signs, e.g. hemorrhage, optic nerve drusen and optic cup notching etc. Using our in-house developed image marking tool, optic cup/disc boundaries and various pathological signs are carefully annotated, providing accurate groundtruth for the development of image based glaucoma detection algorithms. The metadata embedded in the digital image files are extracted and make available in ACHIKO-K database, which, possesses rich information on camera setting and are critical for developing preprocess components for image normalization. Furthermore, the fundus images in ACHIKO-K contains timely information recording retinal changes and can be precious resources for retrospective study.
Speckle Reduction in 3D Optical Coherence Tomography of Retina by A-Scan Reconstruction

Optical coherence tomography (OCT) is a micrometer-scale, cross-sectional imaging modality for biological tissue. It has been widely used for retinal imaging in ophthalmology. Speckle noise is problematic in OCT. A raw OCT image/volume usually has very poor image quality due to speckle noise, which often obscures the retinal structures. Overlapping scan is often used for speckle reduction in a 2D line-scan. However, it leads to an increase of the data acquisition time. Therefore, it is unpractical in 3D scan as it requires a much longer data acquisition time. In this paper, we propose a new method for speckle reduction in 3D OCT. The proposed method models each A-scan as the sum of underlying clean A-scan and noise. Based on the assumption that neighboring A-scans are highly similar in the retina, the method reconstructs each A-scan from its neighboring scans. In the method, the neighboring Ascans are aligned/registered to the A-scan to be reconstructed and form a matrix together. Then low rank matrix completion using bilateral random projection is utilized to iteratively estimate the noise and recover the underlying clean A-scan. The proposed method is evaluated through the mean square error, peak signal to noise ratio and the mean structure similarity index using high quality line-scan images as reference. Experimental results show that the proposed method performs better than other methods. In addition, the subsequent retinal layer segmentation also shows that the proposed method makes the automatic retinal layer segmentation more accurate. The technology can be embedded into current OCT machines to enhance the image quality for visualization and subsequent analysis such as retinal layer segmentation.
Graph based lumen segmentation in optical coherence tomography images

Intravascular optical coherence tomography (IVOCT) is a new invasive imaging system which produces high-resolution images of coronary arteries. Lumen segmentation plays an important role in subsequent analysis of IVOCT images. In this work, we develop a fully automatic lumen segmentation method on IVOCT images. A graph based method is applied to segment the vessel lumen and a match filter based method is employed to detect the guide-wire artifact which caused by guide-wire. A dataset of 500 IVOCT images with manually labeled lumen boundaries is used to evaluate the proposed approach. Overlap dice (OD) is computed to quantitatively evaluate the segmentation result. Results show that the proposed graph based segmentation method is accurate and efficient.
Closed angle glaucoma detection in RetCam images

Closed/Open angle glaucoma classification is important for glaucoma diagnosis. RetCam is a new imaging modality that captures the image of iridocorneal angle for the classification. However, manual grading and analysis of the RetCam image is subjective and time consuming. In this paper, we propose a system for intelligent analysis of iridocorneal angle images, which can differentiate closed angle glaucoma from open angle glaucoma automatically. Two approaches are proposed for the classification and their performances are compared. The experimental results show promising results.
A comparative study on macular optical coherence tomography image alignment

Optical coherence tomography (OCT) is an emerging biomedical imaging technique. In recent years, it is applied in diagnosing eye diseases such as glaucoma and age-related macular degeneration. 3D scans can be obtained by stacking 2D OCT scans into a volume. However, due to the relatively low scan speed, there exists severe misalignment between the 2D scans. In this paper, we propose an exhaustive experimental study on macular OCT image alignment using intensity based image registrations techniques. More than 10 combinations of registration metric and transformations have been explored. Experimental results show that the combinations of sum of squared differences and normalized correlation coefficients and rigid transform are the most effective.
Automatic localization of retinal landmarks

Retinal landmark detection is a key step in retinal screening and computer-aided diagnosis for different types of eye diseases, such as glaucomma, age-related macular degeneration(AMD) and diabetic retinopathy. In this paper, we propose a semantic image transformation(SIT) approach for retinal representation and automatic landmark detection. The proposed SIT characterizes the local statistics of a fundus image and boosts the intrinsic retinal structures, such as optic disc(OD), macula. We propose our salient OD and macular models based on SIT for retinal landmark detection. Experiments on 5928 images show that our method achieves an accuracy of 99.44% in the detection of OD and an accuracy of 93.49% in the detection of macula, while having an accuracy of 97.33% for left and right eye classification. The proposed SIT can automatically detect the retinal landmarks and be useful for further eye-disease screening and diagnosis.
Automatic fovea detection in retinal fundus images

The fovea (centre of the retinal macula region) is responsible for central vision. Conditions which can affect the macula region in particular include AMD and macular oedema, which lead to a direct loss in visual acuity due to the effect on central vision. The detection of the fovea is an important step in the assessment of retinal images for pathologies relating to these visual conditions. In this paper, we propose a method to automatically detect the fovea in retinal fundus images. The method makes use of optic disc detection as a starting point. Using information from an extensive database, typical locations of the fovea are used to define search regions for subsequent analysis. We make use of a combination of the image characteristics of the macular region with noise and vessel removal for fovea detection. The proposed method is tested on a large database of 750 retinal fundus images, achieving an accuracy of 86.53%.
Automated segmentation of optic disc and optic cup in fundus images for glaucoma diagnosis

The vertical Cup-to-Disc Ratio (CDR) is an important indicator in the diagnosis of glaucoma. Automatic segmentation of the optic disc (OD) and optic cup is crucial towards a good computer-aided diagnosis (CAD) system. This paper presents a statistical model-based method for the segmentation of the optic disc and optic cup from digital color fundus images. The method combines knowledge-based Circular Hough Transform and a novel optimal channel selection for segmentation of the OD. Moreover, we extended the method to optic cup segmentation, which is a more challenging task. The system was tested on a dataset of 325 images. The average Dice coefficient for the disc and cup segmentation is 0.92 and 0.81 respectively, which improves significantly over existing methods. The proposed method has a mean absolute CDR error of 0.10, which outperforms existing methods. The results are promising and thus demonstrate a good potential for this method to be used in a mass screening CAD system.
Determining the difference in eyegaze measurements in individuals with Age related Macular Degeneration

Age-related Macular Degeneration (AMD) is one of the leading causes of blindness in the elderly. Visual loss associated with AMD often results in a central scotoma which is an alteration in the central vision, leading to distortion or loss of vision. Current methods of detecting AMD are typically manual, require holding fixation and an external response trigger. In this paper, we propose the use of eyegaze tracking to detect for the presence of AMD, using a simple set of test patterns. Experimental results show that the derived eyegaze measurements can help to identify individuals with AMD from healthy individuals. This could lead to the detection of AMD using eye tracking data, and could result in a potential system device for screening.
Anterior chamber angle classification using multiscale histograms of oriented gradients for glaucoma subtype identification

Glaucoma subtype can be identified according to the configuration of the anterior chamber angle(ACA). In this paper, we present an ACA classification approach based on histograms of oriented gradients at multiple scales. In digital optical coherence tomography (OCT) photographs, our method automatically localizes the ACA, and extracts histograms of oriented gradients (HOG) features from this region to classify the angle as an open angle (OA) or an angle-closure(AC). This proposed method has three major features that differs from existing methods. First, the ACA localization from OCT images is fully automated and efficient for different ACA configurations. Second, the ACA is directly classified as OA/AC by using multiscale HOG visual features only, which is different from previous ACA assessment approaches that on clinical features. Third, it demonstrates that visual features with higher dimensions outperform low dimensional clinical features in terms of angle closure classification accuracy. Testing was performed on a large clinical dataset, comprising of 2048 images. The proposed method achieves a 0.835±0.068 AUC value and 75.8% ± 6.4% balanced accuracy at a 85% specificity, which outperforms existing ACA classification approaches based on clinical features.
Optical Design and Multipath Analysis for Broadband Optical Wireless in an Aircraft Passenger Cabin Application

The design of an optical wireless system for a typical aircraft cabin based on a Boeing 777 will be presented. Optical wireless is ideal for such applications, as there is no potential for interference with avionics, which is a major concern regarding the use of radio frequency devices on board an aircraft. The choice of wavelength with regard to eye safety and ambient lighting levels will be considered. A realistic aircraft cabin geometry will be modeled, and ray-tracing simulations using this model will be performed. Using the ray-trace information, together with an optimization process, the method of designing the optical sources to achieve uniformity of signal distribution within the cabin will be presented. The peak-to-peak variation in the signal intensity for the targeted aisle positions was obtained to be less than 5%. Using the ray-trace information to obtain the multipath information, a communications model is used to determine the performance of the optical wireless system under ambient noise and multipath interference. For a bit error rate (BER) of 10-9 with equalization, a bit rate of more than 170 Mb/s is shown. A transmission bit rate of more than 400 Mb/s is achievable if the acceptable BER is raised to 10-3 .
Superpixel classification for initialization in model based optic disc segmentation

Optic disc segmentation in retinal fundus image is important in ocular image analysis and computer aided diagnosis. Because of the presence of peripapillary atrophy which affects the deformation, it is important to have a good initialization in deformable model based optic disc segmentation. In this paper, a superpixel classification based method is proposed for the initialization. It uses histogram of superpixels from the contrast enhanced image as features. In the training, bootstrapping is adopted to handle the unbalanced cluster issue due to the presence of peripapillary atrophy. A self-assessment reliability score is computed to evaluate the quality of the initialization and the segmentation. The proposed method has been tested in a database of 650 images with optic disc boundaries marked by trained professionals manually. The experimental results show an mean overlapping error of 10.0% and standard deviation of 7.5% in the best scenario. The results also show an increase in overlapping error as the reliability score reduces, which justifies the effectiveness of the self-assessment. The method can be used for optic disc boundary initialization and segmentation in computer aided diagnosis system and the self-assessment can be used as an indicator of cases with large errors and thus enhance the usage of the automatic segmentation.
Local patch reconstruction framework for optic cup localization in glaucoma detection

Optic cup localization/segmentation has attracted much attention from medical imaging researchers, since it is the primary image component clinically used for identifying glaucoma, which is a leading cause of blindness. In this work, we present an optic cup localization framework based on local patch reconstruction, motivated by the great success achieved by reconstruction approaches in many computer vision applications recently. Two types of local patches, i.e. grids and superpixels are used to show the variety, generalization ability and robustness of the proposed framework. Tested on the ORIGA clinical dataset, which comprises of 325 fundus images from a population-based study, both implementations under the proposed frameworks achieved higher accuracy than the state-of-the-art techniques.