Malgorzata Przytulska

Morphological Spectra as Tools for Texture Analysis

There are presented formal properties of morphological spectra as a novel tool for analysis of textures. It is described a multi-level structure of the system of morphological spectra and the method of calculation of spectral components. Formal properties of morphological spectra: symmetries, ability to describe parallel shifts and rotations of analyzed images are presented. Several comments concerning practical aspects of using morphological spectra to analysis of textures are also given.

Biomedical Structures Representation by Morphological Spectra

It is considered the problem of representation of irregular structures, typical in biomedical images, by morphological spectra. Some basic properties of morphological spectra have been reminded. Then several possible methods of morphological spectra presentation are illustrated by a numerical example. The problem of representation of selected classes of irregular structures is considered and illustrated by the examples of vertically elongated, compact and branching structures representation. It is shown that for each class of irregular structures a hierarchy of spectral components indicating their role in representation of the class can be statistically established.

Pattern recognition based on similarity in linear semi-ordered spaces

In the paper an approach to pattern recognition based on a notion of similarity in linear semi-ordered (Kantorovitsch) space is presented. It is compared with other approaches based on the metric distance and on angular dilation measures in observation spaces. Basic assumptions of the Kantorovitsch space are shortly presented. It is shown that finite reference sets for pattern recognition take on in Kantorovitsch space formal structures presented by connectivity graphs which facilitate finding the reference vectors for pattern recognition.

A Comparative Analysis of SPECT Images of the Left and Right Cerebral Hemispheres in Patients with Diagnosed Epileptic Symptoms

The aim of his work was examination of asymmetries in activity of the left and right cerebral hemispheres as well as localization and contouring of the regions of reduced or increased activity on the basis of single photon emission computer tomography (SPECT) images. Advantage of this technique lies in possibility of brain activity map acquisition at the time of radiotracer injection during seizures though the image registration is done one hour after seizure. The SPECT imaging method makes possible a more accurate spatial localization of seizure source than analysis of EEG signals. Simultaneous EEG signal registration allows to qualify exactly the moment of seizure onset when radiotracer injection could be done to register an unequivocal image. The mean and standard deviation of normalized intensities inside the contoured areas of images were calculated.

Biomedical Image Segmentation Based on Aggregated Morphological Spectra

It is described a method of biomedical images segmentation by discrimination of textures based on their morphological spectra. Basic notions concerning morphological spectra are given. Their properties making possible to characterize basic morphological structures independently on spatial orientation or shifts of the analyzed specimens are described. It is shown that spectral components can be chosen and used in aggregated form so as to make discrimination of textures invariant with respect to scale changing and to basic geometrical image transformations. Analysis of two types of biomedical images: aorta tissue and pancreas tissue, based on comparison of histograms of selected spectral components values illustrate the methods presented in the paper.

Direct Filtering and Enhancement of Biomedical Images Based on Morphological Spectra

In the paper a method of filtering of biomedical images aimed at their enhancement for direct visual examination or for automatic segmentation of regions covered by typical textures is presented. For this purpose morphological spectra (being a modification of the systems of orthogonal 2D Walsh functions) are used. Filtering consists in assigning relative weights coefficients to spectral components representing typical morphological micro-structures. However, direct filtering makes possible elimination of calculation of the components of morphological spectra, because filtered values of image elements are given as linear combinations of the values of the original image in fixed basic windows. The method of calculation of the transformation coefficients in details is described. Application of the method is illustrated by an example of cerebral SPECT image examination.

Morphologic-Statistical Approach to Detection of Lesions in Liver Tissue in Fish

The problem of light microscope images enhancement by filtering for recognition pathologic liver tissues in fish is considered in the paper. The problem follows from the necessity of monitoring the sea water pollutions caused by mercury compounds and their influence on living organisms. It is proposed to use image filtering based on morphological spectra to enhance visibility of liver lesions in the images in order to extract morphologic-statistical parameters useful in automatic tissues classification into normal and pathologic classes. It is shown that selected components of the 4th range morphologic spectra (MS4) are the most suitable to discriminate normal and pathologic liver tissues. The selected spectral components are characterized by their estimated mean values, standard deviations and kurtoses. The so-obtained morphologic-statistical parameters have been used to construct the learning sets for two types of image classifiers: based on the nearest mean and k nearest neighbors rules. It is shown that preliminary image filtering by morphological spectra-based filters improves spatial distribution of the recognized normal and pathologic objects in the parameter space.

Quantitative Examination of Liver Tissue Ultrasound Elastograms

Methods of computer-aided statistical analysis of ultrasound elastograms are presented. An approach consisting in initial segmentation of elastograms visualizing low-elasticity segments distribution in the tissue of an examined biological organ and in statistical analysis of this distribution is described. Satisfactory correlation between the values of some statistics and medical specialists’ description of human liver elastograms was observed. The ways of continuation of the works aimed at improvement of the elastograms-based diagnostic methods are suggested.

Biomedical Image Segmentation Based on Morphological Spectra

It is presented a method of segmentation of biomedical ultrasound images by using morphological spectra. The last are a suitable tool for discrimination of textures. In the paper using morphological spectra to analysis of ultrasound liver elastograms is described. It is shown that adequately selected morphological spectral components provide additional information about the spatial distribution of liver tissue elasticity which may enhance the medical diagnosis of liver diseases. Some future works aimed at improvement of this method are suggested.

Partial Similarity Based Retrieval of Images in Distributed Databases

The proper balance between a symmetric and asymmetric cell division is crucial for the neural stem cell maintenance both in vitro and in vivo. These conditions are provided by specific regions of the brain called neural stem cell niches and in vitro occur in neurospheres or adherent clones. A method and a tool for cell culture growth monitoring applied in the investigation of the clonally growth of HUCB-NSC (Human Umbilical Cord Blood derived Neural Stem Cells) line, as an in vitro model of the neural stem cell niche, is proposed.