Birgitte Nielsen

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Texture Analysis

Quantification of the chromatin structure in cell nuclei by texture analysis may improve the accuracy of diagnosis and prognosis of cancer, and may also contribute towards a further understanding of the biological processes involved in carcinogenesis


In the development of a nuclear image analysis system, there are several different steps that need to be performed, such as: extracting useful features from the segmented nuclei, evaluating and selecting features, designing a classifier based on training data, and testing the designed classifier on independent test data.


A large number of features have been proposed to describe the nuclei. The most commonly used features in nuclear image analysis are morphometric, densitometric and textural features. Morphometric features measure the size and shape of the nucleus and are independent of the gray level values of the pixels representing the nucleus. Densitometric features measure the overall optical density or gray level of the nucleus. Textural features provide measures of a number of properties such as contrast, smoothness, coarseness, randomness and structural complexity within the image. In order to find nuclear features that discriminate between cases from different diagnostic and prognostic classes, a statistical evaluation of the features needs to be performed. A subset, or linear combination of the features, can be found using a feature selection procedure. This is achieved using a training data set or by using a functional feature space distance metric, with the aim of generating the smallest classification error. However, the minimum complexity principle should also motivate us to generate only a select few features. Identifying a few consistently valuable features is important as it improves classification reliability and enhances our understanding of the phenomena that we are modeling. The aim of the texture analysis research project is to develop and identify a few textural features that give reliable diagnostic and prognostic information, and to gain insight into which image chromatin structures that actually contain this type of information.


Several categories of methods exist within the field of texture analysis, even if we restrict ourselves to statistical methods, there are still many methods available. Statistical approaches are considered to be generally applicable and work well for natural textures present in images. Statistical texture methods extract local information from the pixels of the image, and describe the distribution of this information in a statistical way. The extracted statistics may range from simple first-order statistics such as mean value and standard deviation of the gray level distribution, second-order or higher-order statistics, depending on the number of pixels which define the local information. In many popular texture analysis methods, second-order or higher-order statistics on the relation between gray level values in pixel pairs or sequences of pixels are stored in matrices. Textural features are then extracted that directly describe the probability distribution within the matrix and, therefore, indirectly describe the image texture. Examples of such methods are the gray level co-occurrence matrix and the gray level run length matrix methods, which are commonly used in nuclear image analysis. The gray level co-occurrence matrix contains information on the relation between gray levels in pixel pairs, while the gray level run length matrix contains information on consecutive pixels with the same gray level, collinear in a given direction. A number of scalar texture features may be extracted from the co-occurrence or run length matrices. Many of these run length or co-occurrence features may be seen as a weighted sum of matrix element values, where the weighting applied to each element is based on a given weighting function. By varying this weighting function, different types of information about the texture can be extracted. The weighting functions fall into two general categories: weighting based on the value of the matrix element and weighting based on the position in the matrix.

This text was last modified: 14.11.2017

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