Search

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. [ABSTRACT FROM AUTHOR]

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.

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.

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.

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.

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.

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.