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

Classification Of Cervical Cell Nuclei Using Morphological Segmentation And Texture Feature Extraction

This paper presents preliminary results for the classification of Pap smear cell nuclei, using Gray Level Co-occurrence Matrix (GLCM) textual features. We outline a method of nuclear segmentation using fast morphological gray-scale transforms. For each segmented nucleus, features derived from a modified form of the GLCM are extracted over several angle and distance measures. Linear Discriminant Analysis is preformed on these features to reduce the dimensionality of the feature space, and a classifier with hyper quadric decision surface is implemented to classify a small set of normal and abnormal cell nuclei. Using 2 features, we achieve a misclassification rate of 3.3% on a data set of 61 cells

Gradient Watersheds in Morphological Scale-Space

this paper