Color image simplification by morphological hierarchical segmentation and color clustering

Morphological hierarchical segmentation of color images may be achieved in a straightforward way by measuring the persistence of regional minima from color gradients and using these measurements as a criterion to select markers for the watershed from markers framework. Since color has an implicit role in the selection of markers, the segmentation process may provide a bad combination of distinct colored regions, and this may lead to a distorted image simplification. This paper proposes a new method to color image simplification in which the importance of color is raised because color information is added to the marker selection process. Such method provides finer control over the final number of regions (n) and the resulting number of colors (c). A color clustering method splits the regional minima in to c minima sets, each of which has a representative color. The most prominent regional minima from each minima set are selected to form the markers for the segmentation framework. In the final segmentation, the color assigned to a region is given by the representative color bound to the marker that points to the region. It leads to an image whose segmented regions are quantized to fewer distinct colors.</p

Hierarchical morphological segmentation for image sequence coding

This paper deals with a hierarchical morphological segmentation algorithm for image sequence coding. Mathematical morphology is very attractive for this purpose because it efficiently deals with geometrical features such as size, shape, contrast, or connectivity that can be considered as segmentation-oriented features. The algorithm follows a top-down procedure. It first takes into account the global information and produces a coarse segmentation, that is, with a small number of regions. Then, the segmentation quality is improved by introducing regions corresponding to more local information. The algorithm, considering sequences as being functions on a 3-D space, directly segments 3-D regions. A 3-D approach is used to get a segmentation that is stable in time and to directly solve the region correspondence problem. Each segmentation stage relies on four basic steps: simplification, marker extraction, decision, and quality estimation. The simplification removes information from the sequence to make it easier to segment. Morphological filters based on partial reconstruction are proven to be very efficient for this purpose, especially in the case of sequences. The marker extraction identifies the presence of homogeneous 3-D regions. It is based on constrained flat region labeling and morphological contrast extraction. The goal of the decision is to precisely locate the contours of regions detected by the marker extraction. This decision is performed by a modified watershed algorithm. Finally, the quality estimation concentrates on the coding residue, all the information about the 3-D regions that have not been properly segmented and therefore coded. The procedure allows the introduction of the texture and contour coding schemes within the segmentation algorithm. The coding residue is transmitted to the next segmentation stage to improve the segmentation and coding quality. Finally, segmentation and coding examples are presented to show the validity and interest of the coding approach.Peer ReviewedPostprint (published version

Achieving the Way for Automated Segmentation of Nuclei in Cancer Tissue Images through Morphology-Based Approach: a Quantitative Evaluation

In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours’ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations

Evolution of field early-type galaxies: The view from GOODS CDFS

We explore the evolution of field early-type galaxies in a sample extracted from the ACS images of the southern GOODS field. The galaxies are selected by means of a nonparametric analysis, followed by visual inspection of the candidates with a concentrated surface brightness distribution. We furthermore exclude from the final sample those galaxies that are not consistent with an evolution into the Kormendy relation between surface brightness and size that is observed for z = 0 ellipticals. The final set, which comprises 249 galaxies with a median redshift z(m) = 0.71, represents a sample of early-type systems not selected with respect to color, with similar scaling relations as those of bona fide elliptical galaxies. The distribution of number counts versus apparent magnitude rejects a constant number density with cosmic time and suggests a substantial decrease with redshift: n proportional to (1 + z)(-2.5). The majority of the galaxies (78%) feature passively evolving old stellar populations. One-third of those in the upper half of the redshift distribution have blue colors, in contrast to only 10% in the lower redshift subsample. An adaptive binning of the color maps using an optimal Voronoi tessellation is performed to explore the internal color distribution. We find that the red and blue early-type galaxies in our sample have distinct behavior with respect to the color gradients, so that most blue galaxies feature blue cores whereas most of the red early-types are passively evolving stellar populations with red cores, i.e., similar systems to local early-type galaxies. Furthermore, the color gradients and scatter do not evolve with redshift and are compatible with the observations at z 0, assuming a radial dependence of the metallicity within each galaxy. Significant gradients in the stellar age are readily ruled out. This work emphasizes the need for a careful sample selection, as we found that most of those galaxies that were visually classified as candidate early types-but then rejected based on the Kormendy relation-feature blue colors characteristic of recent star formation