nils-herrmann/clean_scirepeval huggingface dataset:
Automatic detection of posterior subcapsular cataract opacity for cataract screening

Cataract is the leading cause of blindness and posterior subcapsular cataract (PSC) leads to significant visual impairment. An automatic approach for detecting PSC opacity in retro-illumination images is investigated. The features employed include intensity, edge, size and spatial location. The system was tested using 441 images. The automatic detection was compared with the human expert. The sensitivity and specificity are 82.6% and 80% respectively. The preliminary research indicates it is feasible to apply automatic detection in the clinical screening of PSC in the future.
Eye movement correction for 3D OCT volume by using saliency and center bias constraint

The eye movement artifacts occurring during 3D Optical Coherence Tomography (OCT) volume scanning is a problem that easily affect the image analysis and diagnosis. The existing correction methods are influenced by the background noise and strong vessel, which cause the over-correction. To address this problem, we propose an eye movement correction method based on saliency and center bias constraint. Given a 3D OCT volume, our method firstly utilizes the OCT saliency detection to determine the major layer structure for each slice, and assigns a higher weight to the foreground region. Then an image registration with a center bias constraint is employed to estimate the transformation between the neighbor slices, which is employed to wrap the slice based on the first slice of the OCT volume. Our method contains two key insights: (1) applying the OCT saliency detection to extract the layer structure, (2) utilizing the center bias constraint to avoid the distortion caused by vessel matching. Experiments on both synthetic and real datasets show that our method obtain the satisfied results with the saliency and center bias constraint.
Automatic anterior chamber angle structure segmentation in AS-OCT image based on label transfer.

The anterior chamber angle (ACA) plays an important role for diagnosis and treatment of angle-closure glaucoma. Anterior Segment Optical Coherence Tomography (AS-OCT) imaging is qualitative and quantitative assessment for the ACA structure. In this paper, we propose a novel fully automatic segmentation method for anterior chamber angle structure in AS-OCT. In our method, the initial labels are obtained by using label transfer from the AS-OCT reference dataset. Then, these labels are refined and utilized as the landmarks to support the structure segmentation. Finally, the major clinical structures: corneal boundary, iris region, and trabecular-iris contact, are extracted as the segmentation result. Experiments show that our proposed method achieve the satisfactory segmentation performance on the clinical AS-OCT dataset. Our proposed method has potential in the applications of clinical ACA parameter measurement and automatic glaucoma classification.
Computerized Systems for Cataract Grading

Cataract is the leading cause of blindness worldwide. Two automatic grading systems are presented in this paper for nuclear cataract and cortical cataract diagnosis respectively. Model-based approach was applied to detect anatomical structure in slit-lamp images. Features were extracted based on the lens structure and severity of nuclear cataract was predicted using Support Vector Machines (SVM) regression. For cortical cataract, the opacity was detected using region growing. The seeds were selected by local thresholding and edge detection in radial direction. Cortical cataract was graded based on the area of cortical opacity. Both of the systems were tested by clinical data and results show that the automatic systems can provide objective grading of cataracts.
Method of detecting kink-bearing vessels in a retinal fundus image

Glaucoma is the second leading cause of blindness worldwide. The risk of glaucoma can be determined by calculating the cup to disc ratio in retinal fundus images. To accurately detect the optic cup, kinks or bends in small and medium vessels are important indicators of the cup boundary. In this paper, we present a method of detecting such vessels, through the extraction of patches and generation of hybrid features in a SVM-based model. The segmentation results show good potential for the further development of this method.
Fibroatheroma identification in Intravascular Optical Coherence Tomography images using deep features

Identifying vulnerable plaque is important in coronary heart disease diagnosis. Recent emerged imaging modality, Intravascular Optical Coherence Tomography (IVOCT), has been proved to be able to characterize the appearance of vulnerable plaques. Comparing with the manual method, automated fibroatheroma identification would be more efficient and objective. Deep convolutional neural networks have been adopted in many medical image analysis tasks. In this paper, we introduce deep features to resolve fibroatheroma identification problem. Deep features which extracted using four deep convolutional neural networks, AlexNet, GoogLeNet, VGG-16 and VGG-19, are studied. And a dataset of 360 IVOCT images from 18 pullbacks are constructed to evaluate these features. Within these 360 images, 180 images are normal IVOCT images and the rest 180 images are IVOCT images with fibroatheroma. Here, one pullback belongs to one patient; leave-one-patient-out cross-validation is employed for evaluation. Data augmentation is applied on training set for each classification scheme. Linear support vector machine is conducted to classify the normal IVOCT image and IVOCT image with fibroatheroma. The experimental results show that deep features could achieve relatively high accuracy in fibroatheroma identification.
Retina verification using a combined points and edges approach

This paper presents a novel retina biometric scheme that performs person verification based on passing 2 stages: robust feature points matching and edge dissimilarity measure. Our approach differs from those in the literature as we propose the use of edges and edge dissimilarity measure for retina verification. Our first-stage matching/authentication utilizes robust feature points' matching to determine tentatively whether there is a “match” and if so, performs image registration between the test and template retina image. The robust feature points' matching is achieved in 2 steps: graph-based feature points' matching followed by pruning of wrongly matched feature points using a Least-Median-Squares estimator that enforces an affine transformation geometric constraint. To compute edge dissimilarity measure in our second-stage matching/authentication, we propose the “robustified Hausdorff distance”. We show that our proposed approach outperforms two of the state-of-the-art approaches when tested on the same dataset.
A low-dimensional step pattern analysis algorithm with application to multimodal retinal image registration

Existing feature descriptor-based methods on retinal image registration are mainly based on scale-invariant feature transform (SIFT) or partial intensity invariant feature descriptor (PIIFD). While these descriptors are often being exploited, they do not work very well upon unhealthy multimodal images with severe diseases. Additionally, the descriptors demand high dimensionality to adequately represent the features of interest. The higher the dimensionality, the greater the consumption of resources (e.g. memory space). To this end, this paper introduces a novel registration algorithm coined low-dimensional step pattern analysis (LoSPA), tailored to achieve low dimensionality while providing sufficient distinctiveness to effectively align unhealthy multimodal image pairs. The algorithm locates hypotheses of robust corner features based on connecting edges from the edge maps, mainly formed by vascular junctions. This method is insensitive to intensity changes, and produces uniformly distributed features and high repeatability across the image domain. The algorithm continues with describing the corner features in a rotation invariant manner using step patterns. These customized step patterns are robust to non-linear intensity changes, which are well-suited for multimodal retinal image registration. Apart from its low dimensionality, the LoSPA algorithm achieves about two-fold higher success rate in multimodal registration on the dataset of severe retinal diseases when compared to the top score among state-of-the-art algorithms.
Enhancement of optic cup detection from ARGALI using key points

We propose a method for improving the accuracy of the optic cup detected from the ARGALI system. This method makes use of key points from the branching points of large vessels, the analysis of intensity variation and kinks from small vessels to obtain an enhanced optic cup. Measures used to assess the detection of the optic cup showed an 11% and 40% improvement in the mean average overlap and relative area difference respectively over the previous method. The difference in the CDR error was also shown to be reduced to less than 0.1CDR units. The improved optic cup detection is more consistent with the clinical ground truth, facilitating its use in ARGALI for mass screening for glaucoma for early detection to save sight.
Automated in vivo 3D high-definition optical coherence tomography skin analysis system.

The in vivo assessment and visualization of skin structures can be performed through the use of high resolution optical coherence tomography imaging, also known as HD-OCT. However, the manual assessment of such images can be exhaustive and time consuming. In this paper, we present an analysis system to automatically identify and quantify the skin characteristics such as the topography of the surface of the skin and thickness of the epidermis in HD-OCT images. Comparison of this system with manual clinical measurements demonstrated its potential for automatic objective skin analysis and diseases diagnosis. To our knowledge, this is the first report of an automated system to process and analyse HD-OCT skin images.
Level-set based automatic cup-to-disc ratio determination using retinal fundus images in ARGALI

Glaucoma is a leading cause of permanent blindness. However, disease progression can be limited if detected early. The optic cup-to-disc ratio (CDR) is one of the main clinical indicators of glaucoma, and is currently determined manually, limiting its potential in mass screening. In this paper, we propose an automatic CDR determination method using a variational level-set approach to segment the optic disc and cup from retinal fundus images. The method is a core component of ARGALI, a system for automated glaucoma risk assessment. Threshold analysis is used in pre-processing to estimate the initial contour. Due to the presence of retinal vasculature traversing the disc and cup boundaries which can cause inaccuracies in the detected contours, an ellipse-fitting post-processing step is also introduced. The method was tested on 104 images from the Singapore Malay Eye Study, and it was found the results produced a clinically acceptable variation of up to 0.2 CDR units from the manually graded samples, with potential use in mass screening.
Fusion of pixel and texture features to detect pathological myopia

Myopia is a growing concern in many societies. In extremely high myopia, pathological myopia, which can cause visual loss, can occur. Pathological myopia is also accompanied by various visually perceivable symptoms on the retina, such as peripapillary atrophy. PAMELA is an automatic system for the detection of pathological myopia through the presence of peripapillary atrophy. In this paper, we describe two modules in the PAMELA system based on texture analysis and gray level analysis. A decision engine is then used to fuse the two individual results to obtain an overall analysis. From the results run on a sample batch of images from the Singapore Eye Research Institute, a sensitivity of 0.9 and a specificity of 0.94 with a total accuracy of up to 92.5% is obtained. The promising results indicate good potential for further development of PAMELA as a tool for mass screening for the detection of pathological myopia. This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.
Extract-and-match geometric corner and step pattern approach for registration of fluoroscopic X-ray sequences

This paper presents a method to extract-and-match robust corner features based on connecting edges from the edge maps, mainly formed by coronary vascular junctions in fluoroscopic x-ray sequence images. Such images are challenging due to the aperture problem. To overcome this, existing approaches attempt to extract vessels for registration. However, they are ineffective in poor quality images. Our approach describes the extracted robust corner features in a rotation invariant manner using step patterns, followed by matching them effectively. Experimental results show that our approach performs very well (above 80%) in a dataset of poor quality fluoroscopic x-ray image sequences without extensive processing such as segmentation or learning.
Automatic pterygium detection on cornea images to enhance computer-aided cortical cataract grading system

In this paper, we present a new method to detect pterygiums using cornea images. Due to the similarity of appearances and spatial locations between pterygiums and cortical cataracts, pterygiums are often falsely detected as cortical cataracts on retroillumination images by a computer-aided grading system. The proposed method can be used to filter out the pterygium which improves the accuracy of cortical cataract grading system. This work has three major contributions. First, we propose a new pupil segmentation method for visible wavelength images. Second, an automatic detection method of pterygiums is proposed. Third, we develop an enhanced compute-aided cortical cataract grading system that excludes pterygiums. The proposed method is tested using clinical data and the experimental results demonstrate that the proposed method can improve the existing automatic cortical cataract grading system.
Automatic opacity detection in retro-illumination images for cortical cataract diagnosis

Computer aided analysis of medical images, a unique type of non-text media, can facilitate clinical diagnosis. As an example, an automatic opacity detection approach is proposed in this paper to grade cortical cataract more objectively. The automatic pupil detection is performed by detecting the strongest edges on the convex hull and ellipse fitting using nonlinear least square method. The cortical opacity is detected by radial edge detection and post-processing. The automatic grades are assigned following Wisconsin cataract grading protocol. The accuracy of pupil detection is 98.2%. The mean error of opacity area detection is 7 percent compared with the result of human grader. And 86.3% accurate grades of cortical cataract are achieved. This is the first time that the spoke-like feature is utilized in the automatic detection of cortical cataract to separate from other opacity types. The encouraging results show that it is probable to apply the proposed approach to clinical diagnosis later.
Computer aided diagnosis of nuclear cataract

An approach to automatically diagnose nuclear cataract based on the slit-lamp image is proposed in this paper. Model-based approach is investigated to detect robust lens structure. Based on the detected lens structure, the mean intensity, the color information on the posterior subcapsular reflex and visual axis profile are extracted as the grading features. Support vector machine (SVM) regression model is further trained to predict the grades of nuclear cataract automatically. The proposed approach was tested using 900 images and the mean grading error is 0.36. The encouraging results show that it is promising to apply the proposed approach to clinical diagnosis.
Learn to recognize pathological myopia in fundus images using bag-of-feature and sparse learning approach

Pathological myopia is a leading cause of visual impairment, and can lead to blindness in children if left undetected. We present a bag-of-feature and sparse learning based framework to automatically recognize pathological myopia in retinal fundus images and discover the most related visual features corresponding to the retinal changes in pathological myopia. In the learning phase, the codebook for the bag-of-feature model and the classification model are first learnt, and the top related visual features are discovered via sparse learning concurrently. In the testing phase, for a given retinal fundus image, local features are first extracted and then quantized with the learned codebook to obtain the global feature. Finally, the classification model is used to determine the presence of pathological myopia. Our results on a population based study dataset of 2258 images achieve a 0.964 ± 0.007 AUC value and 90.6±1.0% balanced accuracy at a 85.0% specificity. The results are promising for further development and validation of this framework.
ACHIKO-I retinal fundus image database and its evaluation on cup-to-disc ratio measurement

Retinal fundus image is a widely used image modality to diagnose ocular diseases such as glaucoma, age-related macular degeneration and diabetic retinopathy. There are a number of fundus image databases available online. However, most of them are used for blood vessel extraction and optic disc localization, and hardly any have optic cup annotation. In this paper, we present a fundus image database with accurate annotation of both the optic disc and optic cup. Besides, there are multiple images for the same subject, taking under different conditions, allowing repeatability study of automated segmentation algorithms. Currently, ACHIKO-I contains 179 images and is divided into two subsets for different purposes. The database is used to evaluate our work o n cup-to-disc ratio (CDR) measurement, which also demonstrates how the database can be used and assessed.
Early age-related macular degeneration detection by focal biologically inspired feature

Age-related macular degeneration (AMD) is a leading cause of vision loss. The presence of drusen are often associated to AMD. Drusen are tiny yellowish-white extracellular buildup present around the macular region of the retina. Clinically, ophthalmologists examine the area around the macula to determine the presence and severity of drusen. However, manual identification and recognition of drusen is subjective, time consuming and expensive. To reduce manual workload and facilitate large-scale early AMD screening, it is essential to detect drusen automatically. In this paper, we propose to use biologically inspired features (BIF) for the purpose of AMD detection. The optic disc and macula are detected to determine a focal region around macula for feature extraction. The extracted features are then classified using support vector machines (SVM). Our experimental results, tested on 350 images, demonstrate that the biologically inspired features from the focal region is effective for drusen detection with a sensitivity of 86.3% and specificity of 91.9%. The results of our proposed approach can be used to reduce workload of ophthalmologists and diagnosis cost.
Corneal graft detection for Descemet's Stripping Automated Endothelial Keratoplasty using Optical Coherence Tomography

The cornea is the window of the eye and when it is severely damaged or diseased, vision is impaired. Descemet's Stripping Automated Endothelial Keratoplasty (DSAEK) is a surgical procedure to replace the malfunctioned Descemet's membrane with a healthy one in order to restore the patient's sight. After the operation, ophthalmologists need to monitor the grafted membrane to check for signs of detachment, rejection, etc. and take appropriate actions before graft failure occurs. In this paper, we introduce the COrneaL GrAft Thickness Evaluation (COLGATE) System that we developed for ophthalmologists for the evaluation of the transplanted corneal graft. We discuss the various components in our system and methods we developed. Experiments are conducted and the results are m1 = 7.5% and m2 = 7.2%.
Automatic volume classification in intravascular optical coherence tomography images

Intravascular Optical Coherence Tomography (IVOCT) is an emerging modality which could produce high resolution images, and would help cardiologists study coronary artery disease. Current IVOCT studies are mainly focus on classification or identification at frame level, while overlook the information provided by the entire pullbacks. In this work, we propose a volume based classification system which identifies the disease pullbacks automatically. Here, volume is defined as a set of consecutive frames within IVOCT pullback. In the proposed system, we first apply graph search based lumen segmentation method to detect lumen boundary and region-of-interest (ROI). After that, two texture features, Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) are extracted from each frame. Finally, we represent each volume using bag-of-words, and apply linear SVM for classification. A dataset with 9 healthy volumes and 32 unhealthy volumes is constructed to evaluate the system. In our experiment, each volume includes 20 consecutive IVOCT frames. Leave-one-patient-out cross-validation is employed for evaluation. Sensitivity, specificity and mean accuracy are computed to quantitatively evaluate the proposed system. The mean classification accuracy of 0.95 is reported, demonstrating the effectiveness of the proposed system.
Automatic detection of the macula in retinal fundus images using seeded mode tracking approach

The macula is the part of the eye responsible for central high acuity vision. Detection of the macula is an important task in retinal image processing as a landmark for subsequent disease assessment, such as for age-related macula degeneration. In this paper, we have presented an approach to automatically determine the macula centre in retinal fundus images. First contextual information on the image is combined with a statistical model to obtain an approximate macula region of interest localization. Subsequently, we propose the use of a seeded mode tracking technique to locate the macula centre. The proposed approach is tested on a large dataset composed of 482 normal images and 162 glaucoma images from the ORIGA database and an additional 96 AMD images. The results show a ROI detection of 97.5%, and 90.5% correct detection of the macula within 1/3DD from a manual reference, which outperforms other current methods. The results are promising for the use of the proposed approach to locate the macula for the detection of macula diseases from retinal images.