Showing papers by "Dorin Comaniciu published in 1999"

Mean shift analysis and applications

Abstract: A nonparametric estimator of density gradient, the mean shift, is employed in the joint, spatial-range (value) domain of gray level and color images for discontinuity preserving filtering and image segmentation. Properties of the mean shift are reviewed and its convergence on lattices is proven. The proposed filtering method associates with each pixel in the image the closest local mode in the density distribution of the joint domain. Segmentation into a piecewise constant structure requires only one more step, fusion of the regions associated with nearby modes. The proposed technique has two parameters controlling the resolution in the spatial and range domains. Since convergence is guaranteed, the technique does not require the intervention of the user to stop the filtering at the desired image quality. Several examples, for gray and color images, show the versatility of the method and compare favorably with results described in the literature for the same images.

Distribution Free Decomposition of Multivariate Data

Abstract: We present a practical approach to nonparametric cluster analysis of large data sets The number of clusters and the cluster centres are automatically derived by mode seeking with the mean shift procedure on a reduced set of points randomly selected from the data The cluster boundaries are delineated using a k-nearest neighbour technique The proposed algorithm is stable and efficient, a 10,000 point data set being decomposed in only a few seconds Complex clustering examples and applications are discussed, and convergence of the gradient ascent mean shift procedure is demonstrated for arbitrary distribution and cardinality of the data

Image-guided decision support system for pathology

Abstract: We present a content-based image retrieval sys- tem that supports decision making in clinical pathology. The image-guided decision support system locates, retrieves, and displays cases which exhibit morphological profiles con- sistent to the case in question. It uses an image database containing 261 digitized specimens which belong to three classes of lymphoproliferative disorders and a class of heal- thy leukocytes. The reliability of the central module, the fast color segmenter, makes possible unsupervised on-line anal- ysis of the query image and extraction of the features of interest: shape, area, and texture of the nucleus. The nuclear shape is characterized through similarity invariant Fourier descriptors, while the texture analysis is based on a mul- tiresolution simultaneous autoregressive model. The system performance was assessed through ten-fold cross-validated classification and compared with that of a human expert. To facilitate a natural man-machine interface, speech recogni- tion and voice feedback are integrated. Client-server com- munication is multithreaded, Internet-based, and provides ac- cess to supporting clinical records and video databases.

Retrieval performance improvement through low rank corrections

Abstract: Whenever a feature extracted from an image has a unimodal distribution, information about its covariance matrix can be exploited for content based retrieval using as dissimilarity measure, the Bhattacharyya distance. To reduce the amount of computations and the size of logical database entry, we approximate the Bhattacharyya distance, taking into account that most of the energy in the feature space is often restricted to a low dimensional subspace. The theory was tested for a database of 1188 textures derived from VisTex with the local texture being represented by a 15 dimensional MRSAR feature vector. The retrieval performance improved significantly, relative to the traditional Mahalanobis distance based approach, in spite of using only one or two dimensions in the approximation.

Decision support system for multiuser remote microscopy in telepathology

Abstract: Recent advances in networking, robotics and computer technology allow real-time diagnosis, consultation, and education by using images obtained through remote microscopy. This paper presents a new approach in telepathology, the image guided decision support (IGDS) system, which integrates components for both remote microscope control and decision support. Using the micro-controller component the physician can command a robotic microscope from a distance, obtain high-quality images to be used in the diagnosis, and authorize other users to visualize the same images. The image understanding-based decision support component of the system locates, retrieves and displays cases which exhibit morphological profiles consistent to the case in question and suggests the most likely diagnosis based on majority logic. The IGDS system has a natural man-machine interface containing engines for speech recognition and voice feedback.