Showing papers on "Range segmentation published in 1983"

Segmentation of cell nuclei in tissue section analysis.

Abstract: Image segmentation is a critical step in digital picture analysis, especially for that of tissue sections. As the morphology of the cell nuclei provides important biological information, their segmentation is of particular interest. The known segmentation methods are not adequate for segmenting cell nuclei of tissue sections; the reason for this lies in the optical properties of their images. We have developed new gradient methods of segmentation of previously presegmented images by taking these properties into account and by using the approximately circular shape of the cell nuclei as a priori information. In our first technique, the segment method, the images of the nuclei are divided into eight segments, special gradient filters being defined for each segment. This has enabled us to improve the gradient image. After searching for local maxima, the contours of nuclei can be found. In the second method, the method of transformation into the polar coordinate system (PCS), the a priori information serves t...

Image Segmentation By Edge Tracing

Abstract: This paper describes an image segmentation technique that has detection and classification applications in autonomous image analysis systems and compares it with current edge and region-based segmentation techniques. The technique, referred to as directed edge tracing, uses both edge magnitude and direction information to reduce segmentation problems commonly associated with segmenters based on edge thresholding. The edge tracing is carried out by examining pixels in a neighborhood of high probability boundary pixels, making the method locally adaptive to contrast changes along a boundary. No a priori knowledge of the number of segments expected or of the level of contrast between segments is required.

Integrating non-semantic knowledge into image segmentation processes

Abstract: This dissertation develops several techniques for automatically segmenting images into regions. The basic approach involves the integration of different types of non-semantic knowledge into the segmentation process such that the knowledge can be used when and where it is useful. These processes are intended to produce initial segmentations of complex images which are faithful with respect to fine image detail, balanced by a computational need to limit the segmentations to a fairly small number of regions. Natural scenes often contain intensity gradients, shadows, highlights, texture, and small objects with fine geometric structure, all of which make the calculation and evaluation of reasonable segmentations for natural scenes extremely difficult. The approach taken by this dissertation is to integrate specialized knowledge into the segmentation process for each kind of image event that can be shown to adversely affect the performance of the process. At the center of our segmentation system is an algorithm which labels pixels in localized subimages with the feature histogram cluster to which they correspond, followed by a relaxation labeling process. However, this algorithm has a tendency to undersegment by failing to find clusters corresponding to small objects; it may also oversegment by splitting intensity gradients into multiple clusters, by finding clusters for "mixed pixel" regions, and by finding clusters corresponding to microtexture elements. In addition, the relaxation process often destroys fine structure in the image. Finally, the artificial subimage partitions introduce the problem of inconsistent cluster sets and the need to recombine the segmentations of the separate subsimages into a consistent whole. This dissertation addresses each of these problems by adding and deleting clusters based on image space information, by merging regions, and by defining different compatibility coefficients in the relaxation so as to preserve fine structures. The result is a segmentation algorithm which is more reliable over a broader range of images than the simple clustering algorithm. Solutions to the same segmentation problems were examined via the integration of different segmentation algorithms (including edge, region, and thresholding algorithms) to produce a consistent segmentation. . . . (Author's abstract exceeds stipulated maximum length. Discontinued here with permission of author.) UMI

Iterative Process For Colour Image Segmentation Using A Convexity Criterion

Abstract: In image processing, the segmentation is a very important step, mainly because of its influence on the feature evaluation. During the segmentation process, problems occur either due to possible ambiguities in the pixel labelling or due to frequent contacts between different components. This paper describes an iterative method, designed to segment convex poorly contrasted or touching objects which is based on the association of two notions, one related to the object colour and the other to its shape, in terms of convexity. Starting from an initial connected set of points, pixels are iteratively aggregated to that set using a colour distance. The processing stops when convexity of the iterated set is reached. Application to a particular class of convex objects, such as blood and bone marrow cells is presented.