Lixin Duan

A survey on computer aided diagnosis for ocular diseases

BackgroundComputer 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.MethodWe 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.ResultWe 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.ConclusionWhile 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.

Optic Cup Segmentation for Glaucoma Detection Using Low-Rank Superpixel Representation

We present an unsupervised approach to segment optic cups in fundus images for glaucoma detection without using any additional training images. Our approach follows the superpixel framework and domain prior recently proposed in, where the superpixel classification task is formulated as a low-rank representation (LRR) problem with an efficient closed-form solution. Moreover, we also develop an adaptive strategy for automatically choosing the only parameter in LRR and obtaining the final result for each image. Evaluated on the popular ORIGA dataset, the results show that our approach achieves better performance compared with existing techniques.

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.

Speckle Reduction in Optical Coherence Tomography by Image Registration and Matrix Completion

Speckle noise is problematic in optical coherence tomography (OCT). With the fast scan rate, swept source OCT scans the same position in the retina for multiple times rapidly and computes an average image from the multiple scans for speckle reduction. However, the eye movement poses some challenges. In this paper, we propose a new method for speckle reduction from multiply-scanned OCT slices. The proposed method applies a preliminary speckle reduction on the OCT slices and then registers them using a global alignment followed by a local alignment based on fast iterative diamond search. After that, low rank matrix completion using bilateral random projection is utilized to iteratively estimate the noise and recover the underlying clean image. Experimental results show that the proposed method achieves average contrast to noise ratio 15.65, better than 13.78 by the baseline method used currently in swept source OCT devices. The technology can be embedded into current OCT machines to enhance the image quality for subsequent analysis.

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

BACKGROUND AND OBJECTIVESnDiabetic 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.nnnMETHODSnTo 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.nnnRESULTSnExperiments 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.nnnCONCLUSIONSnProposed 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.

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.

Axial Alignment for Anterior Segment Swept Source Optical Coherence Tomography via Robust Low-Rank Tensor Recovery

We present a one-step approach based on low-rank tensor recovery for axial alignment in 360-degree anterior chamber optical coherence tomography. Achieving translational alignment and rotation correction of cross-sections simultaneously, this technique obtains a better anterior segment topographical representation and improves quantitative measurement accuracy and reproducibility of disease related parameters. Through its use of global information, the proposed method is more robust compared to using only individual or paired slices, and less sensitive to noise and motion artifacts. In angle closure analysis on 30 patient eyes, the preliminary results indicate that the proposed axial alignment method can not only facilitate manual qualitative analysis with more distinct landmark representation and much less human labor, but also can improve the accuracy of automatic quantitative assessment by 2.9 %, which demonstrates that the proposed approach is promising for a wide range of clinical applications.

Incorporating Privileged Genetic Information for Fundus Image Based Glaucoma Detection

Visual features extracted from retinal fundus images have been increasingly used for glaucoma detection, as those images are generally easy to acquire. In recent years, genetic researchers have found that some single nucleic polymorphisms (SNPs) play important roles in the manifestation of glaucoma and also show superiority over fundus images for glaucoma detection. In this work, we propose to use the SNPs to form the so-called privileged information and deal with a practical problem where both fundus images and privileged genetic information exist for the training subjects, while the test objects only have fundus images. To solve this problem, we present an effective approach based on the learning using privileged information (LUPI) paradigm to train a predictive model for the image visual features. Extensive experiments demonstrate the usefulness of our approach in incorporating genetic information for fundus image based glaucoma detection.

Local patch reconstruction framework for optic cup localization in glaucoma detection

Optic cup localization/segmentation has attracted much attention from medical imaging researchers, since it is the primary image component clinically used for identifying glaucoma, which is a leading cause of blindness. In this work, we present an optic cup localization framework based on local patch reconstruction, motivated by the great success achieved by reconstruction approaches in many computer vision applications recently. Two types of local patches, i.e. grids and superpixels are used to show the variety, generalization ability and robustness of the proposed framework. Tested on the ORIGA clinical dataset, which comprises of 325 fundus images from a population-based study, both implementations under the proposed frameworks achieved higher accuracy than the state-of-the-art techniques.

Breast Tumor Detection in Ultrasound Images Using Deep Learning.

Detecting tumor regions in breast ultrasound images has always been an interesting topic. Due to the complex structure of breasts and the existence of noise in the ultrasound images, traditional handcraft feature based methods usually cannot achieve satisfactory results. With the recent advance of deep learning, the performance of object detection has been boosted to a great extent, especially for general object detection. In this paper, we aim to systematically evaluate the performance of several existing state-of-the-art object detection methods for breast tumor detection. To achieve that, we have collected a new dataset consisting of 579 benign and 464 malignant lesion cases with the corresponding ultrasound images manually annotated by experienced clinicians. Comprehensive experimental results clearly show that the recently proposed convolutional neural network based method, Single Shot MultiBox Detector (SSD), outperforms other methods in terms of both precision and recall.