nils-herrmann/clean_scirepeval huggingface dataset:
Peripapillary Atrophy Detection by Sparse Biologically Inspired Feature Manifold

Peripapillary atrophy (PPA) is an atrophy of pre-existing retina tissue. Because of its association with eye diseases such as myopia and glaucoma, PPA is an important indicator for diagnosis of these diseases. Experienced ophthalmologists are able to determine the presence of PPA using visual information from the retinal images. However, it is tedious, time consuming and subjective to examine all images especially in a screening program. This paper presents biologically inspired feature (BIF) for the automatic detection of PPA. BIF mimics the process of cortex for visual perception. In the proposed method, a focal region is segmented from the retinal image and the BIF is extracted. As BIF is an intrinsically low dimensional feature embedded in a high dimensional space, it is not suitable to measure the similarity between two BIFs directly based on the Euclidean distance. Therefore, it is necessary to obtain a suitable mapping to reduce the dimensionality. In this paper, we explore sparse transfer learning to transfer the label information from ophthalmologists to the sample distribution knowledge contained in all samples. Selective pair-wise discriminant analysis is used to define two strategies of sparse transfer learning: negative and positive sparse transfer learning. Experimental results show that negative sparse transfer learning is superior to the positive one for this task. The proposed BIF based approach achieves an accuracy of more than 90% in detecting PPA, much better than previous methods. It can be used to save the workload of ophthalmologists and thus reduce the diagnosis costs.
THALIA - An automatic hierarchical analysis system to detect drusen lesion images for amd assessment

Age-related macular degeneration (AMD) is a leading cause of permanent blindness. In its early stage AMD is characterized by drusen which are extracellelur deposits in the retina. In this paper, we present THALIA, an automatic system for the detection of drusen images for AMD assessment. First, the macular region of interest is detected using a seeded mode tracking approach. The macular region of interest is then mapped into a new representation using a hierarchicial word transform (HWI). In HWI, dense sampling is first carried out to generate structured pixels which embed local context. These structured pixels are then clustered using hierarchical k-means. The HWI image is subsequently classified using a SVM-based classifier. We have tested THALIA on a dataset of 350 images and obtained an accuracy of 95.46%. Results are promising for further validation of the THALIA system.
Automatic atherosclerotic heart disease detection in intracoronary optical coherence tomography images

Intracoronary optical coherence tomography (OCT) is a new invasive imaging system which produces high-resolution images of coronary arteries. Preliminary data suggests that the atherosclerotic disease can be detected from the intracoronary OCT images. However, manual assessment of the intracoronary OCT images is time-consuming and subjective. In this work, we present an automatic atherosclerotic disease detection system on intracoronary OCT images. In the system, a preprocessing scheme is first applied to remove speckle noise and artifacts caused by catheter. Intensity, Histograms of Oriented Gradients (HOG), and Local Binary Patterns (LBP) are then extracted to represent the OCT image. Finally a linear SVM classifier is employed to detect the unhealthy subject. Four-fold cross-validation process is conducted to evaluate the proposed system; and a dataset with 200 images from healthy subjects and 200 images from unhealthy subjects is built to evaluate the system. The mean accuracy is 0.90 and standard deviation is 0.0427, which indicates that the proposed system is accurate and stable.
Red lesion detection in retinal fundus images using Frangi-based filters

This paper presents a method to detect red lesions related to Diabetic Retinopathy (DR), namely Microaneurysms and Hemorrhages from retinal fundus images with robustness to the presence of blood vessels. Filters based on Frangi filters are used for the first time for this task. Green channel of the input image was decomposed into smaller sub images and proposed filters were applied to each sub image after initial preprocessing. Features were extracted from the filter response and used to train a Support Vector Machine classifier to predict whether a test image had lesions or not. Experiments were performed on a dataset of 143 retinal fundus and the proposed method achieved areas under the ROC curve equal to 0.97 and 0.87 for Microaneurysms and Hemorrhages respectively. Results show the effectiveness of the proposed method for detecting red lesions. This method can help significantly in automated detection of DR with fewer false positives.
A cloud-based system for automatic glaucoma screening

In recent years, there has been increasing interest in the use of automatic computer-based systems for the detection of eye diseases including glaucoma. However, these systems are usually standalone software with basic functions only, limiting their usage in a large scale. In this paper, we introduce an online cloud-based system for automatic glaucoma screening through the use of medical image-based pattern classification technologies. It is designed in a hybrid cloud pattern to offer both accessibility and enhanced security. Raw data including patient's medical condition and fundus image, and resultant medical reports are collected and distributed through the public cloud tier. In the private cloud tier, automatic analysis and assessment of colour retinal fundus images are performed. The ubiquitous anywhere access nature of the system through the cloud platform facilitates a more efficient and cost-effective means of glaucoma screening, allowing the disease to be detected earlier and enabling early intervention for more efficient intervention and disease management.
Optic disc region of interest localization in fundus image for Glaucoma detection in ARGALI

Glaucoma is the second leading cause of permanent blindness worldwide. Glaucoma can be diagnosed through measurement of neuro-retinal optic cup-to-disc ratio (CDR). Correctly determining the optic disc region of interest (ROI) will produce a smaller initial image which takes much lesser time taken to process compared to the entire image. The earlier ROI localization in the ARGALI system used a grid based method. The new algorithm adds a preprocessing step before analyzing the image. This step significantly improves the performance of the ROI detection. A batch of 1564 retinal images from the Singapore Eye Research Centre was used to compare the performance of the two methods. From the results, the earlier and new algorithm detects the ROI correctly for 88% and 96% of the images respectively. The results indicate potential applicability of the method for automated and objective mass screening for early detection of glaucoma.
Integrating holistic and local deep features for glaucoma classification.

Automated glaucoma detection is an important application of retinal image analysis. Compared with segmentation based approaches, image classification based approaches have a potential of better performance. However, it still remains a challenging problem for two reasons. Firstly, due to insufficient sample size, learning effective features is difficult. Secondly, the shape variations of optic disc introduce misalignment. To address these problem, a new classification based approach for glaucoma detection is proposed, in which deep convolutional networks derived from large-scale generic dataset is used to representing the visual appearance and holistic and local features are combined to mitigate the influence of misalignment. The proposed method achieves an area under the receiver operating characteristic curve of 0.8384 on the Origa dataset, which clearly demonstrates its effectiveness.
Speckle reduction in optical coherence tomography by matrix completion using bilateral random projection

Speckle noise is problematic in optical coherence tomography (OCT) and often obscures the structure details. In this paper, we propose a new method to reduce speckle noise from multiply scanned OCT slices. The proposed method registers the OCT scans using a global alignment followed by a local alignment based on global and local motion estimation. Then low rank matrix completion using bilateral random projection is utilized to estimate the noise and recover the clean image. Experimental results show that the proposed method archives average contrast to noise ratio 14.90, better than 13.78 by the state-of-the-art method used in current OCT machines. The technology can be embedded into current OCT machines to enhance the image quality.
Glaucoma classification from retina optical coherence tomography angiogram

This paper presents a new method for classification of retina into glaucoma and non-glaucoma cases based on optical coherence tomography angiogram (OCTA). The key idea here is to analyze the retinal microvasculature in the optic disc area of an enface OCTA for glaucoma classification. To facilitate this analysis, we propose a way to extract a so-called “optic disc microvasculature” region and then propose several features that will be extracted from this microvasculature region. A machine classifier is then trained using the designated features and subsequently used to classify the OCTA data. We show that our proposed approach works well on the tested dataset.
Optic cup and disk extraction from retinal fundus images for determination of cup-to-disc ratio

The ratio of the optic cup to disc (CDR) in retinal fundus images is one of the principal physiological characteristics in the diagnosis of glaucoma. Currently the CDR is manually determined which can be subjective and limits its use in mass screening. To automatically extract the disc, a variational level set method is proposed in this paper. For the cup, two methods making use of color intensity and threshold level set are evaluated. A batch of 73 retinal images from the Singapore Eye Research Centre was used to assess the performance of the determined CDR to the clinical CDR, and it was found that the threshold and variational level set methods produced 97% accuracy in the determined CDR results, an 18% improvement over the color intensity method. The results indicate potential applicability of the methods for automated and objective mass screening for early detection of glaucoma.
Automatic Optic Disc Detection in OCT Slices via Low-Rank Reconstruction

Optic disc measurements provide useful diagnostic information as they have correlations with certain eye diseases. In this paper, we provide an automatic method for detecting the optic disc in a single OCT slice. Our method is developed from the observation that the retinal pigment epithelium (RPE) which bounds the optic disc has a low-rank appearance structure that differs from areas within the disc. To detect the disc, our method acquires from the OCT image an RPE appearance model that is specific to the individual and imaging conditions, by learning a low-rank dictionary from image areas known to be part of the RPE according to priors on ocular anatomy. The edge of the RPE, where the optic disc is located, is then found by traversing the retinal layer containing the RPE, reconstructing local appearance with the low-rank model, and detecting the point at which appearance starts to deviate (i.e., increased reconstruction error). To aid in this detection, we also introduce a geometrical constraint called the distance bias that accounts for the smooth shape of the RPE. Experiments demonstrate that our method outperforms other OCT techniques in localizing the optic disc and estimating disc width. Moreover, we also show the potential usage of our method on optic disc area detection in 3-D OCT volumes.
Automatic detection of the macula in the retinal fundus image by detecting regions with low pixel intensity

This paper proposes a method to detect the macula in the retinal fundus image automatically. The method makes use of the optic disc height obtained from the ARGALI to define the region of interest. Regions of dark spots are then detected by finding the coordinates with the lowest pixel intensity and determining the average pixel neighbourhood intensities. These regions are ranked to determine the region containing the macula. This algorithm was tested on 162 images, and an accuracy of 98.8% was achieved. The results are promising for further development and use of this method in AMD studies and physiology localization.
Automatic detection of pathological myopia using variational level set

Pathological myopia, the seventh leading cause of legal blindness in United States, is a condition caused by pathological axial elongation and eyes that deviates from the normal distribution curve of axial length, resulting in impaired vision. Studies have shown that ocular risks associated with myopia should not be underestimated, and there is a public health need to prevent the onset or progression of myopia. Peripapillary atrophy (PPA) is one of the clinical indicators for pathological myopia. In this paper, we introduce a novel method, to detect pathological myopia via peripapaillary atrophy feature by means of variational level set. This method is a core algorithm of our system, PAMELA, an automated system for the detection of pathological myopia. The proposed method has been tested on 40 images from Singapore Cohort study Of the Risk factors for Myopia (SCORM), producing a 95% accuracy of correct assessment, and a sensitivity and specificity of 0.9 and 1 respectively. The results highlight the potential of PAMELA as a possible clinical tool for objective mass screening of pathological myopia.
Model-based optic nerve head segmentation on retinal fundus images

The optic nerve head (optic disc) plays an important role in the diagnosis of retinal diseases. Automatic localization and segmentation of the optic disc is critical towards a good computer-aided diagnosis (CAD) system. In this paper, we propose a method that combines edge detection, the Circular Hough Transform and a statistical deformable model to detect the optic disc from retinal fundus images. The algorithm was evaluated against a data set of 325 digital color fundus images, which includes both normal images and images with various pathologies. The result shows that the average error in area overlap is 11.3% and the average absolute area error is 10.8%, which outperforms existing methods. The result indicates a high correlation with ground truth segmentation and thus demonstrates a good potential for this system to be integrated with other retinal CAD systems.
Skin surface topographic assessment using in vivo high-definition optical coherence tomography

Skin surface topography and wrinkles are important biophysical features in both dermatological and cosmeceutical practice and research. Current skin surface topography evaluation techniques, such as replica-based methods, have limitations which may result in errors and inaccurate measurement. High-Definition Optical Coherence Tomography (HD-OCT) is a recently-developed non-invasive skin imaging modality used clinically for visualizing skin structures. In this present work, we developed an automatic evaluation system to quantify the skin surface topography and wrinkles in HD-OCT images. Comparison of this system with subjective evaluations of skin surface roughness was carried out, and there was a good correlation between the results of these two methods of evaluation.
Photometric correction of retinal images by polynomial interpolation

This paper presents a photometric restoration technique that automatically corrects shading within retinal images taken with a fundus camera. The proposed technique is based on the observation that the background of retinal images usually shows flat reflectance variations due to its high similarity in color and texture. It estimates shading through an iterative polynomial interpolation procedure that first estimates a shading image through a horizontal interpolation process and then improves the shading estimation by a vertical interpolation process. Once the shading image is estimated, a reflectance image can accordingly be determined based on the luminance of the retina image under study. Experiments on 161 retinal images of different qualities show promising results.
An automatic diagnosis system of nuclear cataract using slit-lamp images

An automatic diagnosis system of nuclear cataract is presented in this paper. Nuclear cataract is graded according to the severity of opacity using slit-lamp lens images. Anatomical structure in the lens image is detected using a modified active shape model (ASM). Based on the anatomical landmark, local features are extracted according to clinical grading protocol. Support vector machine (SVM) regression is employed to train a grading model for grade prediction. The system is tested using clinical images and clinical ground truth. More than five thousands slit-lamp images were tested. The success rate of feature extraction is 95% and the mean grading difference is 0.36. The automatic diagnosis system can help to improve the grading objectivity and save the workload of ophthalmologists.
Image based diagnosis of cortical cataract.

An automatic approach to detect cortical opacities and grade the severity of cortical cataract from retro-illumination images is proposed. The spoke-like feature of cortical opacity is employed to separate from other opacity type. The proposed algorithms were tested by images from a community study. The success rate of region of interest (ROI) detection is 98.2% for 611 images. For 466 images tested, the mean error of opacity area detection is 3.15% compared with human grader and 85.6% of exact cortical cataract grading is obtained. The experimental results show that the proposed approach is promising in clinical diagnosis.
An ensembling approach for optic cup detection based on spatial heuristic analysis in retinal fundus images

Optic cup detection remains a challenging task in retinal image analysis, and is of particular importance for glaucoma evaluation, where disease severity is often assessed by the size of the optic cup. In this paper, we propose spatial heuristic ensembling (SHE), an approach which aims to fuse the advantages of each method based on the specific performance in each defined sector. In this way, we generate an ensembled optic cup which is obtained from the optimal combination of the component methods. We conduct experiments on the ORIGA data set of 650 retinal images and show that the ensemble approach performs better than the individual segmentations, reducing the relative overlap error, and CDR errors by as much as 0.04 CDR units. The results are promising for the continued development of such an approach for improving optic cup segmentation.
Automatic retinal interest evaluation system (ARIES)

In recent years, there has been increasing interest in the use of automatic computer-based systems for the detection of eye diseases such as glaucoma, age-related macular degeneration and diabetic retinopathy. However, in practice, retinal image quality is a big concern as automatic systems without consideration of degraded image quality will likely generate unreliable results. In this paper, an automatic retinal image quality assessment system (ARIES) is introduced to assess both image quality of the whole image and focal regions of interest. ARIES achieves 99.54% accuracy in distinguishing fundus images from other types of images through a retinal image identification step in a dataset of 35342 images. The system employs high level image quality measures (HIQM) to perform image quality assessment, and achieves areas under curve (AUCs) of 0.958 and 0.987 for whole image and optic disk region respectively in a testing dataset of 370 images. ARIES acts as a form of automatic quality control which ensures good quality images are used for processing, and can also be used to alert operators of poor quality images at the time of acquisition.
Automatic image classification in intravascular optical coherence tomography images

Vulnerable plaque detection to identify plaque is important in coronary heart disease diagnosis. Currently, it is conducted through manual reading of intravascular optical coherence tomography (IVOCT) images by an interventional cardiologist. However, human reading and understanding is highly subjective. An objective and automated assessment of plaque status is highly needed. This paper proposes a method for automatic image classification in IVOCT images based on different lesion types. In the proposed method, we first use detail-preserving anisotropic diffusion to remove speckle noise in IVOCT images. It removes the noise without losing details. Then, the IVOCT images are transformed to polar coordinates for feature extraction. In particular, Fisher vector and other texture features including local binary pattern and histogram of oriented gradients are studied. Finally, a support vector machine classifier is obtained to classify the IVOCT images into five groups: Normal (normal), FP (fibrous plaque), FA (fibroatheroma), PR (plaque rupture), and FC (fibrocalcific plaque). These five groups are obtained according to lesion characteristics. We evaluate the proposed method in a dataset of 1,000 images with five groups. Experimental results show that the proposed method achieves an average accuracy of 90% in image classification. The proposed automatic IVOCT image classification method can be used to save time and cost of cardiologist.
Self-assessment for optic disc segmentation

Optic disc segmentation from retinal fundus image is a fundamental but important step in many applications such as automated glaucoma diagnosis. Very often, one method might work well on many images but fail on some other images and it is difficult to have a single method or model to cover all scenarios. Therefore, it is important to combine results from several methods to minimize the risk of failure. For this purpose, this paper computes confidence scores for three methods and combine their results for an optimal one. The experimental results show that the combined result from three methods is better than the results by any individual method. It reduces the mean overlapping error by 7.4% relatively compared with best individual method. Simultaneously, the number of failed cases with large overlapping errors is also greatly reduced. This is important to enhance the clinical deployment of the automated disc segmentation.
Glaucoma detection based on deep convolutional neural network.

Glaucoma is a chronic and irreversible eye disease, which leads to deterioration in vision and quality of life. In this paper, we develop a deep learning (DL) architecture with convolutional neural network for automated glaucoma diagnosis. Deep learning systems, such as convolutional neural networks (CNNs), can infer a hierarchical representation of images to discriminate between glaucoma and non-glaucoma patterns for diagnostic decisions. The proposed DL architecture contains six learned layers: four convolutional layers and two fully-connected layers. Dropout and data augmentation strategies are adopted to further boost the performance of glaucoma diagnosis. Extensive experiments are performed on the ORIGA and SCES datasets. The results show area under curve (AUC) of the receiver operating characteristic curve in glaucoma detection at 0.831 and 0.887 in the two databases, much better than state-of-the-art algorithms. The method could be used for glaucoma detection.