Showing papers by "Subhasis Chaudhuri published in 1989"
TL;DR: The concept of matched filter detection of signals is used to detect piecewise linear segments of blood vessels in these images and the results are compared to those obtained with other methods.
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. >
1,692 citations
Proceedings Article•
08 Nov 1989
TL;DR: For the final step in interpreting the image, the backpropagation neural network is found to be able to learn to diagnose a set of diseases from the type of information in the coded description of the image.
Abstract: Interpretation of images of the ocular fundus by the STARE (STructured Analysis of the REtina) system requires many steps, including image enhancement, object segmentation, object identification, and scene analysis. We describe how these steps are performed and linked, and we demonstrate some success with the STARE system in each of these steps. We are currently able to segment the blood vessels, optic nerve, fovea, bright lesions, and dark lesions automatically. We describe the methods for these tasks and the development underway to complete the production of a database of objects that forms a coded description of the image. For the final step in interpreting the image, we found the backpropagation neural network to be able to learn to diagnose a set of diseases from the type of information in the coded description of the image.
32 citations
04 Jun 1989
TL;DR: The total-least-squares (TLS) method is very appropriate when the observation and the data matrices are both perturbed by random noise, and the solution is equivalent to maximum-likelihood estimation.
Abstract: If the correspondence between two sets of points representing the coordinates of different points of an object undergoing rotational motion and deformation is known, the parameters can be estimated using different least-squares estimators. The total-least-squares (TLS) method is very appropriate when the observation and the data matrices are both perturbed by random noise. For Gaussian-distributed noise, the TLS solution is equivalent to maximum-likelihood estimation. The mean-square error in TLS is always smaller than in an ordinary least-squares (LS) estimator. The scope is analyzed of TLS in estimating the generalized motion parameters, as is the feasibility of decomposing the generalized motion parameters in terms of rotation and deformation parameters. The performance of TLS is compared to that of the LS estimator. >
21 citations