Norman P. Katz

Bio: Norman P. Katz is an academic researcher from Sony Broadcast & Professional Research Laboratories. The author has contributed to research in topics: Edge detection & Icon. The author has an hindex of 3, co-authored 4 publications receiving 1912 citations.

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

Abstract: 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. >

System for managing multimedia discs, tracks and files on a standalone computer

Abstract: A computer system is provided that allows the content of a user's collection of fixed and removable media to be managed without regard to a specific type of media. The computer system performs equally well with files on floppy discs, removable hard-disks such as Iomega Zip, CDDA discs, CD-ROM discs, DVD-ROM discs and recordable or re-writeable variants of CD-ROMs and DVD-ROMs. The computer system of the present invention includes a computer program that allows a user to: insert his/her own collection of discs into a changer device and automatically inventory the changer to create a database or “cache” of the titles, directories and file names on all discs in the changer, describe and categorize both audio and CD-ROM discs, sort the discs based on different criteria, load a specific disc into the changer's drive to access files or play audio or video tracks, display discs and the multimedia files on the changer either textually or graphically, create playlists and collections of specific files or tracks as a reference for locating them later or incorporating them into presentations, use a built-in player to play a sequence of multimedia clips or tracks, while automatically loading CDs containing these clips and tracks as needed, view certain multimedia discs such as CD-Extra discs as both a set of audio tracks and a directory tree of files, maintain information associated with CDs in a database after the CDs are removed from the disc changer device, and allow the user to browse through his/her CD collection even after the discs have been removed from the disc changer device.

Ridge-based vessel segmentation in color images of the retina

Abstract: 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.

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

Abstract: 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.

Display screen or portion thereof with graphical user interface

Abstract: Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification

Abstract: We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and two-dimensional Gabor wavelet transform responses taken at multiple scales. The Gabor wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as Gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE (Staal et al.,2004) and STARE (Hoover et al.,2000) databases of manually labeled images. On the DRIVE database, it achieves an area under the receiver operating characteristic curve of 0.9614, being slightly superior than that presented by state-of-the-art approaches. We are making our implementation available as open source MATLAB scripts for researchers interested in implementation details, evaluation, or development of methods