Early Prediction of Wound Healing Outcome Based on Chronic Wound Registry Database.

Chronic wounds cause a number of unnecessary amputations due to a delay in proper treatment. To expedite timely treatment, this paper presents an algorithm which uses a logistic regression classifier to predict whether the wound will heal or not within a specified time. The prediction is made at three time-points: one month, three months, and six months from the first visit of the patient to the healthcare facility. This prediction is made using a systematically collected chronic wound registry and is based entirely on data collected during patients' first visit. The algorithm achieves an area under the receiver operating characteristic curve (AUC) of 0.75, 0.72, and 0.71 for the prediction at the three time-points, respectively.Clinical relevance- Using the proposed prediction model, the clinicians will have an early estimate of the time taken to heal thereby providing appropriate treatments. We hope this will ensure timely treatments and reduce the number of unnecessary amputations.

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.

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.

Techniques and Applications in Skin OCT Analysis.

The skin is the largest organ of our body. Skin disease abnormalities which occur within the skin layers are difficult to examine visually and often require biopsies to make a confirmation on a suspected condition. Such invasive methods are not well-accepted by children and women due to the possibility of scarring. Optical coherence tomography (OCT) is a non-invasive technique enabling in vivo examination of sub-surface skin tissue without the need for excision of tissue. However, one of the challenges in OCT imaging is the interpretation and analysis of OCT images. In this review, we discuss the various methodologies in skin layer segmentation and how it could potentially improve the management of skin diseases. We also present a review of works which use advanced machine learning techniques to achieve layers segmentation and detection of skin diseases. Lastly, current challenges in analysis and applications are also discussed.

Dissolving Triamcinolone-Embedded Microneedles for the Treatment of Keloids: A Single-Blinded Intra-Individual Controlled Clinical Trial.

INTRODUCTION: Keloids are a prevalent chronic skin disorder with significant psychosocial morbidity. Intralesional corticosteroid injections are the first-line treatment but are painful and require repeated injections by medical professionals. Dissolving microneedles are a novel method of cutaneous drug delivery that induces minimal/no pain and can be self-administered. The objective of the study was to evaluate the efficacy and safety of triamcinolone-embedded dissolving microneedles in treatment of keloids. METHODS: This was a single-blind, intra-individual controlled two-phase clinical trial of 8-week duration each. Two keloids per subject were selected for (1) once-daily 2-min application with microneedles for 4 weeks, followed by no treatment for the next 4 weeks, or (2) non-intervention as control. Primary outcome was change in keloid volume as assessed by a high-resolution 3D scanner. RESULTS: There was significant reduction in keloid volume compared with controls after 4 weeks of treatment. This reduction was greater with a higher dosage of triamcinolone used. CONCLUSIONS: Once-daily application of dissolving triamcinolone-embedded microneedles significantly reduced the volume of keloids. The treatment was safe, can be self-administered and can serve as an alternative for patients unsuitable for conventional treatments. TRIAL REGISTRATION: Trial Registry: Health Science Authority (Singapore) Clinical Trials Register Registration number: 2015/00440.

Three-dimensional graph-based skin layer segmentation in optical coherence tomography images for roughness estimation.

Automatic skin layer segmentation in optical coherence tomography (OCT) images is important for a topographic assessment of skin or skin disease detection. However, existing methods cannot deal with the problem of shadowing in OCT images due to the presence of hair, scales, etc. In this work, we propose a method to segment the topmost layer of the skin (or the skin surface) using 3D graphs with a novel cost function to deal with shadowing in OCT images. 3D graph cuts use context information across B-scans when segmenting the skin surface, which improves the segmentation as compared to segmenting each B-scan separately. The proposed method reduces the segmentation error by more than 20% as compared to the best performing related work. The method has been applied to roughness estimation and shows a high correlation with a manual assessment. Promising results demonstrate the usefulness of the proposed method for skin layer segmentation and roughness estimation in both normal OCT images and OCT images with shadowing.

Detecting retinal microaneurysms and hemorrhages with robustness to the presence of blood vessels.

BACKGROUND AND OBJECTIVES: Diabetic Retinopathy is the leading cause of blindness in developed countries in the age group 20-74 years. It is characterized by lesions on the retina and this paper focuses on detecting two of these lesions, Microaneurysms and Hemorrhages, which are also known as red lesions. This paper attempts to deal with two problems in detecting red lesions from retinal fundus images: (1) false detections on blood vessels; and (2) different size of red lesions. METHODS: To deal with false detections on blood vessels, novel filters have been proposed which can distinguish between red lesions and blood vessels. This distinction is based on the fact that vessels are elongated while red lesions are usually circular blob-like structures. The second problem of the different size of lesions is dealt with by applying the proposed filters on patches of different sizes instead of filtering the full image. These patches are obtained by dividing the original image using a grid whose size determines the patch size. Different grid sizes were used and lesion detection results for these grid sizes were combined using Multiple Kernel Learning. RESULTS: Experiments on a dataset of 143 images showed that proposed filters detected Microaneurysms and Hemorrhages successfully even when these lesions were close to blood vessels. In addition, using Multiple Kernel Learning improved the results when compared to using a grid of one size only. The areas under receiver operating characteristic curve were found to be 0.97 and 0.92 for Microaneurysms and Hemorrhages respectively which are better than the existing related works. CONCLUSIONS: Proposed filters are robust to the presence of blood vessels and surpass related works in detecting red lesions from retinal fundus images. Improved lesion detection using the proposed approach can help in automatic detection of Diabetic Retinopathy.

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 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.

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 survey on computer aided diagnosis for ocular diseases.

BACKGROUND: Computer Aided Diagnosis (CAD), which can automate the detection process for ocular diseases, has attracted extensive attention from clinicians and researchers alike. It not only alleviates the burden on the clinicians by providing objective opinion with valuable insights, but also offers early detection and easy access for patients. METHOD: We review ocular CAD methodologies for various data types. For each data type, we investigate the databases and the algorithms to detect different ocular diseases. Their advantages and shortcomings are analyzed and discussed. RESULT: We have studied three types of data (i.e., clinical, genetic and imaging) that have been commonly used in existing methods for CAD. The recent developments in methods used in CAD of ocular diseases (such as Diabetic Retinopathy, Glaucoma, Age-related Macular Degeneration and Pathological Myopia) are investigated and summarized comprehensively. CONCLUSION: While CAD for ocular diseases has shown considerable progress over the past years, the clinical importance of fully automatic CAD systems which are able to embed clinical knowledge and integrate heterogeneous data sources still show great potential for future breakthrough.

Automatic nuclear cataract grading using image gradients.

This paper deals with automatic grading of nuclear cataract (NC) from slit-lamp images in order to reduce the efforts in traditional manual grading. Existing works on this topic have mostly used brightness and color of the eye lens for the task but not the visibility of lens parts. The main contribution of this paper is in utilizing the visibility cue by proposing gray level image gradient-based features for automatic grading of NC. Gradients are important for the task because in a healthy eye, clear visibility of lens parts leads to distinct edges in the lens region, but these edges fade as severity of cataract increases. Experiments performed on a large dataset of over 5000 slit-lamp images reveal that the proposed features perform better than the state-of-the-art features in terms of both speed and accuracy. Moreover, fusion of the proposed features with the prior ones gives results better than any of the two used alone.