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Abstract: This paper describes an automatic method for classification and segmentation of different intracardiac masses in tumor echocardiograms. Identification of mass type is highly desirable, since to different treatment options for cardiac tumors (surgical resection) and thrombi (effective anticoagulant treatment) are possible. Correct diagnosis of the character of intracardiac mass in a living patient is a true challenge for a cardiologist; therefore, an objective image analysis method may be useful in heart diseases diagnosis. Image texture analysis is used to distinguish various types of masses. The presented methods assume that image texture encodes important histological features of masses and, therefore, texture numerical parameters enable the discrimination and segmentation of a mass. The recently developed technique based on the network of synchronized oscillators is proposed for the image segmentation. This technique is based on a ‘temporary correlation’ theory, which attempts to explain scene recognition as it would be performed by a human brain. This theory assumes that different groups of neural cells encode different properties of homogeneous image regions (e.g. shape, color, texture). Monitoring of temporal activity of cell groups leads to scene segmentation. A network of synchronized oscillators was successfully used for segmentation of Brodatz textures and medical textured images. The advantage of this network is its ability to detect texture boundaries. It can be also manufactured as a VLSI chip, for a very fast image segmentation. The accuracy of locating of analyzed tissues in the image should be assessed to evaluate a segmentation technique. The new evaluation method based on measurement of physical textured test objects was proposed. Firstly, a series of object images was obtained by the use of different devices (scanner, digital camera and TV camera). Secondly, the images were segmented using oscillator network and feedforward artificial neural network. Thirdly, geometrical test object parameters were estimated and compared to its true values. The experiment was repeated also for ultrasound images, which represented rectangular cross-section of synthetic sponge submerged in water. In addition, classification and segmentation of selected benign tumor echocardiograms were performed. Oscillator network was used with network weights defined for both whole texture region and texture boundary detection for the tumor segmentation. The latter method provides much faster segmentation with the similar accuracy. The obtained segmentation results were discussed and compared to the artificial neural network classifier. Finally, it was demonstrated that the network of synchronized oscillators is a reliable tool for the segmentation of the selected intracardiac masses, since it gives a relatively accurate location of analyzed tissues. The advantage of the proposed method is its resistance to changes of the visual information in the analyzed image and to noise and artifacts, often present in echocardiograms. [Copyright &y& Elsevier]

Abstract: Modern medical diagnosis utilizes techniques of visualization of human internal organs (CT, MRI) or of its metabolism (PET). However, evaluation of acquired images made by human experts is usually subjective and qualitative only. Quantitative analysis of MR data, including tissue classification and segmentation, is necessary to perform e.g. attenuation compensation, motion detection, and correction of partial volume effect in PET images, acquired with PET/MR scanners. This article presents briefly a MaZda software package, which supports 2D and 3D medical image analysis aiming at quantification of image texture. MaZda implements procedures for evaluation, selection and extraction of highly discriminative texture attributes combined with various classification, visualization and segmentation tools. Examples of MaZda application in medical studies are also provided. [Copyright &y& Elsevier]

Abstract: MaZda, a software package for 2D and 3D image texture analysis is presented. It provides a complete path for quantitative analysis of image textures, including computation of texture features, procedures for feature selection and extraction, algorithms for data classification, various data visualization and image segmentation tools. Initially, MaZda was aimed at analysis of magnetic resonance image textures. However, it revealed its effectiveness in analysis of other types of textured images, including X-ray and camera images. The software was utilized by numerous researchers in diverse applications. It was proven to be an efficient and reliable tool for quantitative image analysis, even in more accurate and objective medical diagnosis. MaZda was also successfully used in food industry to assess food product quality. MaZda can be downloaded for public use from the Institute of Electronics, Technical University of Lodz webpage. [Copyright &y& Elsevier]