Quantitative Comparison of Color Asymmetry Features for Automatic Melanoma Detection.

Asymmetry assessment is an important step towards melanoma detection. This paper compares some of the color asymmetry features proposed in the literature which have been used to automatically detect melanoma from color images. A total of nine features were evaluated based on their accuracy in predicting lesion asymmetry on a dataset of 277 images. In addition, the accuracies of these features in differentiating melanoma from benign lesions were compared. Results show that simple features based on the brightness difference between the two halves of the lesion performed the best in predicting asymmetry and subsequently melanoma.Clinical relevance- The proposed work will assist researchers in choosing better performing color asymmetry features thereby improving the accuracy of automatic melanoma detection. The resulting system will reduce the workload of clinicians by screening out obviously benign cases and referring only the suspicious cases to them.

Efficient Deep Learning-based Wound-bed Segmentation For Mobile Applications.

This paper proposes a deep learning image segmentation method for the purpose of segmenting wound-bed regions from the background. Our contributions include proposing a fast and efficient convolutional neural networks (CNN)-based segmentation network that has much smaller number of parameters than U-Net (only 18.1% that of U-Net, and hence the trained model has much smaller file size as well). In addition, the training time of our proposed segmentation network (for the base model) is only about 40.2% of that needed to train a U-Net. Furthermore, our proposed base model also achieved better performance compared to that of the U-Net in terms of both pixel accuracy and intersection-over-union segmentation evaluation metrics. We also showed that because of the small footprint of our efficient CNN-based segmentation model, it could be deployed to run in real-time on portable and mobile devices such as an iPad.

Role of the Choroid in Automated Age-related Macular Degeneration Detection from Optical Coherence Tomography Images.

Automatic detection of age-related macular degeneration (AMD) from optical coherence tomography (OCT) images is often performed using the retinal layers only and choroid is excluded from the analysis. This is because symptoms of AMD manifest in the choroid only in the later stages and clinical literature is divided over the role of the choroid in detecting earlier stages of AMD. However, more recent clinical research suggests that choroid is affected at a much earlier stage. In the proposed work, we experimentally verify the effect of including the choroid in detecting AMD from OCT images at an intermediate stage. We propose a deep learning framework for AMD detection and compare its accuracies with and without including the choroid. Results suggest that including the choroid improves the AMD detection accuracy. In addition, the proposed method achieves an accuracy of 96.78% which is comparable to the state-of-the-art works.

Geometric corner extraction in retinal fundus images.

This paper presents a novel approach of finding corner features between retinal fundus images. Such images are relatively textureless and comprising uneven shades which render state-of-the-art approaches e.g., SIFT to be ineffective. Many of the detected features have low repeatability (<; 10%), especially when the viewing angle difference in the corresponding images is large. Our approach is based on the finding of blood vessels using a robust line fitting algorithm, and locating corner features based on the bends and intersections between the blood vessels. These corner features have proven to be superior to the state-of-the-art feature extraction methods (i.e. SIFT, SURF, Harris, Good Features To Track (GFTT) and FAST) with regard to repeatability and stability in our experiment. Overall in average, the approach has close to 10% more repeatable detected features than the second best in two corresponding retinal images in the experiment.

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.

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.

Integrating research, clinical practice and translation: the Singapore experience.

We introduce the experiences of the Singapore ocular imaging team, iMED, in integrating image processing and computer-aided diagnosis research with clinical practice and knowledge, towards the development of ocular image processing technologies for clinical usage with potential impact. In this paper, we outline key areas of research with their corresponding image modalities, as well as providing a systematic introduction of the datasets used for validation.

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.

Validity of a new optic disc grading software for use in clinical and epidemiological research.

BACKGROUND: To determine the reliability and agreement of a new optic disc grading software program for use in clinical, epidemiological research. DESIGN: Reliability and agreement study. SAMPLES: 328 monoscopic and 85 stereoscopic optic disc images. METHODS: Optic disc parameters were measured using a new optic disc grading software (Singapore Optic Disc Assessment) that is based on polynomial curve-fitting algorithm. Two graders independently graded 328 monoscopic images to determine intergrader reliability. One grader regraded the images after 1 month to determine intragrader reliability. In addition, 85 stereo optic disc images were separately selected, and vertical cup-to-disc ratios were measured using both the new software and standardized Wisconsin manual stereo-grading method by the same grader 1 month apart. Intraclass correlation coefficient (ICC) and Bland-Altman plot analyses were performed. MAIN OUTCOME MEASURES: Optic disc parameters. RESULTS: The intragrader and intergrader reliability for optic disc measurements using Singapore Optic Disc Assessment was high (ICC ranging from 0.82 to 0.94). The mean differences (95% limits of agreement) for intergrader vertical cup-to-disc ratio measurements were 0.00 (-0.12 to 0.13) and 0.03 (-0.15 to 0.09), respectively. The vertical cup-to-disc ratio agreement between the software and Wisconsin grading method was extremely close (ICC = 0.94). The mean difference (95% limits of agreement) of vertical cup-to-disc ratio measurement between the two methods was 0.03 (-0.09 to 0.16). CONCLUSIONS: Intragrader and intergrader reliability using Singapore Optic Disc Assessment was excellent. This software was highly comparable with standardized stereo-grading method. Singapore Optic Disc Assessment is useful for grading digital optic disc images in clinical, population-based studies.

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.

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.

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.

Focal biologically inspired feature for glaucoma type classification.

Glaucoma is an optic nerve disease resulting in loss of vision. There are two common types of glaucoma: open angle glaucoma and angle closure glaucoma. Glaucoma type classification is important in glaucoma diagnosis. Ophthalmologists examine the iridocorneal angle between iris and cornea to determine the glaucoma type. However, manual classification/grading of the iridocorneal angle images is subjective and time consuming. To save workload and facilitate large-scale clinical use, it is essential to determine glaucoma type automatically. In this paper, we propose to use focal biologically inspired feature for the classification. The iris surface is located to determine the focal region. The association between focal biologically inspired feature and angle grades is built. The experimental results show that the proposed method can correctly classify 85.2% images from open angle glaucoma and 84.3% images from angle closure glaucoma. The accuracy could be improved close to 90% with more images included in the training. The results show that the focal biologically inspired feature is effective for automatic glaucoma type classification. It can be used to reduce workload of ophthalmologists and diagnosis cost.

Focal edge association to glaucoma diagnosis.

Glaucoma is an optic nerve disease resulting in the loss of vision. There are two common types of glaucoma: open angle glaucoma and angle closure glaucoma. Glaucoma type classification is important in glaucoma diagnosis. Clinically, ophthalmologists examine the iridocorneal angle between iris and cornea to determine the glaucoma type as well as the degree of closure. However, manual grading of the iridocorneal angle images is subjective and often time consuming. In this paper, we propose focal edge for automated iridocorneal angle grading. The iris surface is located to determine focal region and focal edges. The association between focal edges and angle grades is built through machine learning. A modified grading system with three grades is adopted. The experimental results show that the proposed method can correctly classify 87.3% open angle and 88.4% closed angle. Moreover, it can correctly classify 75.0% grade 1 and 77.4% grade 0 for angle closure cases.

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.

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%.

ORIGA(-light): an online retinal fundus image database for glaucoma analysis and research.

Retinal fundus image is an important modality to document the health of the retina and is widely used to diagnose ocular diseases such as glaucoma, diabetic retinopathy and age-related macular degeneration. However, the enormous amount of retinal data obtained nowadays mostly stored locally; and the valuable embedded clinical knowledge is not efficiently exploited. In this paper we present an online depository, ORIGA(-light), which aims to share clinical groundtruth retinal images with the public; provide open access for researchers to benchmark their computer-aided segmentation algorithms. An in-house image segmentation and grading tool is developed to facilitate the construction of ORIGA(-light). A quantified objective benchmarking method is proposed, focusing on optic disc and cup segmentation and Cup-to-Disc Ratio (CDR). Currently, ORIGA(-light) contains 650 retinal images annotated by trained professionals from Singapore Eye Research Institute. A wide collection of image signs, critical for glaucoma diagnosis, are annotated. We will update the system continuously with more clinical ground-truth images. ORIGA(-light) is available for online access upon request.