A method is presented for automated segmentation of vessels in two-dimensional color images of the retina. This method can be used in computer analyses of retinal images, e.g., in automated screening for diabetic retinopathy. The system is based on extraction of image ridges, which coincide approximately with vessel centerlines. The ridges are used to compose primitives in the form of line elements. With the line elements an image is partitioned into patches by assigning each image pixel to the closest line element. Every line element constitutes a local coordinate frame for its corresponding patch. For every pixel, feature vectors are computed that make use of properties of the patches and the line elements. The feature vectors are classified using a kNN-classifier and sequential forward feature selection. The algorithm was tested on a database consisting of 40 manually labeled images. The method achieves an area under the receiver operating characteristic curve of 0.952. The method is compared with two recently published rule-based methods of Hoover et al. and Jiang et al. . The results show that our method is significantly better than the two rule-based methods (p<0.01). The accuracy of our method is 0.944 versus 0.947 for a second observer.

/pdf/ridge-based-vessel-segmentation-in-color-images-of-the-1i9un3yqbx.pdf

Ridge-based vessel segmentation in color images of the retina

Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning

Describes an automated method to locate and outline blood vessels in images of the ocular fundus. Such a tool should prove useful to eye care specialists for purposes of patient screening, treatment evaluation, and clinical study. The authors' method differs from previously known methods in that it uses local and global vessel features cooperatively to segment the vessel network. The authors evaluate their method using hand-labeled ground truth segmentations of 20 images. A plot of the operating characteristic shows that the authors' method reduces false positives by as much as 15 times over basic thresholding of a matched filter response (MFR), at up to a 75% true positive rate. For a baseline, they also compared the ground truth against a second hand-labeling, yielding a 90% true positive and a 4% false positive detection rate, on average. These numbers suggest there is still room for a 15% true positive rate improvement, with the same false positive rate, over the authors' method. They are making all their images and hand labelings publicly available for interested researchers to use in evaluating related methods.

/pdf/locating-blood-vessels-in-retinal-images-by-piecewise-4lgaualy1f.pdf

Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response

We present here SIMPLIcity (semantics-sensitive integrated matching for picture libraries), an image retrieval system, which uses semantics classification methods, a wavelet-based approach for feature extraction, and integrated region matching based upon image segmentation. An image is represented by a set of regions, roughly corresponding to objects, which are characterized by color, texture, shape, and location. The system classifies images into semantic categories. Potentially, the categorization enhances retrieval by permitting semantically-adaptive searching methods and narrowing down the searching range in a database. A measure for the overall similarity between images is developed using a region-matching scheme that integrates properties of all the regions in the images. The application of SIMPLIcity to several databases has demonstrated that our system performs significantly better and faster than existing ones. The system is fairly robust to image alterations.

/pdf/simplicity-semantics-sensitive-integrated-matching-for-3bfwe17qcj.pdf

SIMPLIcity: semantics-sensitive integrated matching for picture libraries

Human motion analysis is receiving increasing attention from computer vision researchers. This interest is motivated by a wide spectrum of applications, such as athletic performance analysis, surveillance, man-machine interfaces, content-based image storage and retrieval, and video conferencing. The paper gives an overview of the various tasks involved in motion analysis of the human body. The authors focus on three major areas related to interpreting human motion: 1) motion analysis involving human body parts, 2) tracking of human motion using single or multiple cameras, and 3) recognizing human activities from image sequences. Motion analysis of human body parts involves the low-level segmentation of the human body into segments connected by joints, and recovers the 3D structure of the human body using its 2D projections over a sequence of images. Tracking human motion using a single or multiple camera focuses on higher-level processing, in which moving humans are tracked without identifying specific parts of the body structure. After successfully matching the moving human image from one frame to another in image sequences, understanding the human movements or activities comes naturally, which leads to a discussion of recognizing human activities. The review is illustrated by examples.

Human motion analysis: a review

Blood vessels usually have poor local contrast, and the application of existing edge detection algorithms yield results which are not satisfactory. An operator for feature extraction based on the optical and spatial properties of objects to be recognized is introduced. The gray-level profile of the cross section of a blood vessel is approximated by a Gaussian-shaped curve. The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images. Twelve different templates that are used to search for vessel segments along all possible directions are constructed. Various issues related to the implementation of these matched filters are discussed. The results are compared to those obtained with other methods. >

/pdf/detection-of-blood-vessels-in-retinal-images-using-two-2lxvrugvz5.pdf

Detection of blood vessels in retinal images using two-dimensional matched filters

Medical imaging is shifting from film to electronic images. The STARE (structured analysis of the retina) system is a sophisticated image management system that will automatically diagnose images, compare images, measure key features in images, annotate image contents, and search for images similar in content. The authors concentrate on automated diagnosis. The images are annotated by segmentation of objects of interest, classification of the extracted objects, and reasoning about the image contents. The inferencing is accomplished with Bayesian networks that learn from image examples of each disease. This effort at image understanding in fundus images anticipates the future use of medical images. As these capabilities mature, the authors expect that ophthalmologists and physicians in other fields that rely in images will use a system like STARE to reduce repetitive work, to provide assistance to physicians in difficult diagnoses or with unfamiliar diseases, and to manage images in large image databases.

Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images

An architecture for multiple perspective interactive video

Similarity between images is used for storage and retrieval in image databases. In the literature, several similarity measures have been proposed that may be broadly categorized as: (1) metric based, (2) set-theoretic based, and (3) decision-theoretic based measures. In each category, measures based on crisp logic as well as fuzzy logic are available, In some applications such as image databases, measures based on fuzzy logic would appear to be naturally better suited, although so far no comprehensive experimental study has been undertaken. In this paper, we report results of some of the experiments designed to compare various similarity measures for application to image databases. We are currently working with texture images and intend to work with face images in the near future. As a first step for comparison, the similarity matrices for each of the similarity measure is computed over a set of selected textures and are presented as visual images. Comparative analysis of these images reveals the relative characteristics of each of these measures. Further experiments are needed to study their sensitivity to small changes in images such as illumination, magnification, orientation etc. We describe these experiments (sensitivity analysis, transition analysis etc.) that are currently in progress. >

Similarity measures for image databases

Crucial to the transformation of computers into intelligent multimedia systems is the ability to access, manipulate and manage visual information. In particular, video data is becoming a prevalent source of such information. Our Multiple Perspective Interactive Video (MPI-Video), project integrates a variety of visual computing operations with modeling and interaction techniques, to extract, synthesize and manage dynamic representations of scenes observed from multiple perspectives. Automatic and semi-automatic analysis of video data from multiple cameras is performed. This analysis is used to build a three-dimensional model of the environment monitored by the cameras. MPI- Video has applications in a variety of areas including the development of immersive video for telepresence systems, traffic monitoring and control and the analysis of physical performances, e.g. sports and dance. This video presents an overview of MPI- Video along with demonstrations of two MPI-Video prototypes developed in the Visual Computing Lab. Further details on MPI- Video are described by Jain and Wakimoto in [Jain and Wakimoto, 1995] and in our technical report [Kelly et al., 1995]

/pdf/multiple-perspective-interactive-video-10hwdn6tah.pdf

Shankar Chatterjee

Papers

Detection of blood vessels in retinal images using two-dimensional matched filters

Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images

An architecture for multiple perspective interactive video

Similarity measures for image databases

Multiple perspective interactive video