Scale-Space Texture Analysis

In this paper we propose a technique for classifying images by modelling features extracted at different scales. Specifically, we use texture measures derived from Pap smear cell nuclei images using grey level Co-occurrence Matrix (GLCM). For a texture feature extracted from GLCM at a number of distances we hypothesis that by modelling the feature as a continuous function of scale we can obtain information as to the shape of this function and hence improve its discriminatory power. This hypothesis is compared to the traditional method of selecting a given number of the best single distance measure. It is found on the limited data set available, that the classification accuracy can be improved by modelling the texture feature in this way

Chromatin segmentation

A method of segmenting chromatin particles in a nucleus of a cell by locating regional minima in an image, computing a zone of influence (ZOI) around each regional minimum, and segmenting a single chromatin blob within each ZOI using a region growing procedure. The method can be used as the basis of a method of qualitatively characterizing the distribution of nuclear chromatin by computing features for individual chromatin particles. Chromatin features can be synthesized from the features of individual particles and particle features can be synthesized into nucleus features and slide features. The method is useful for detecting malignancy associated changes and changes during neoplasia. The method may also be used more generally to assess chromatin patterns in living cells during the cell life cycle. This makes it possible to measure alternations in the evolving patterns that result from pathological or environmental influences

Classification In Scale-Space: Applications To Texture

In this paper we propose a technique for classifying images by modeling features extracted at different scales. Specifically, we use texture measures derived from Pap Smear cell nuclei images using a Grey Level Co-occurrence Matrix (GLCM). For a texture feature extracted from the GLCM at a number of distances we hypothesise that by modeling the feature as a continuous function of scale we can obtain information as to the shape of this function and hence improve its discriminatory power. This hypothesis is compared to the traditional method of selecting a given number of the best single distance measures. It is found, on the limited data set available, that the classification accuracy can be improved by modeling the texture features in this way

Generalised Hilbert transforms for the estimation of growth direction in coral cores

Measuring coral growth rate is essential for monitoring coral reef health, and part of the process involves directional analysis of x-ray images of coral sections. The monogenic signal is useful for this application as it represents an image in terms of intrinsically-1D feature type (phase), strength (amplitude) and orientation. At certain locations the monogenic signal may give orientation errors, however these can be resolved using higher order generalised Hilbert transforms. We follow this approach, but combine components using a double angle representation as well as the sign of the phase. The improved algorithm is then applied to estimation of growth direction in coral x-ray images. An objective estimation of major growth axis, growth band location, and off axis extension compensation is now possible, and shows the usefulness of 2D analytic signal based image analysis

PARTIAL ORDERINGS AND SCALE-SPACES WITH MONOTONICITY OF FEATURES

Abstract Signal simplification, as measured through a reduction in signal features is an important aspect of scale-space theories. Partial ordering can be seen as a formal characterization of the notion of a simpler version of a signal. The partial orderings implied by some scale-spaces which obey monotonicity of features are examined with examples drawn from 1-D Gaussian scale-space and Multiscale Dilation Scale-Space. We hope to provide a unifying framework for such scale-spaces Keywords: Scale-space theory, posets, mathematical morphology. 1

Co-operative evolution of a neural classifier and feature subset

. This paper describes a novel feature selection algorithm which utilizes a genetic algorithm to select a feature subset in conjunction with the weights for a three-layer feedforward network classifier. On the "ionosphere " data set from UC Irvine, this approach produces results comparable to those reported for other algorithms on the same data, but using fewer input features and a simpler neural network architecture. These results indicate that tailoring a neural network classifier to a specific subset of features has the potential to produce a classifier with low classification error, good generalizability, and relatively low computational overhead. Keywords Genetic algorithm; neural network; classification; ionosphere 1 Introduction Feature selection is the process of selecting an optimum subset of features from the enormous set of potentially useful features available in a given problem domain [2]. The "optimum subset of features" which is the aim of the feature extraction algori..

Folding induced self-dual filters

In this paper we present a method for constructing self-dual grey-scale image operators from arbitrary morphological operators defined on what we call fold-space. We call this class of self-dual operators folding induced self-dual filters (FISFs). We show examples of their application to noise filtering