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This paper is focused on the analysis of biomedical images, including textured ones. A segmentation method, based on network of synchronized oscillators is presented. Oscillator networks can be considered as a special case of the CNN. Its oscillatory dynamics allows encoding the different features of objects forming the visual scene, thus makes these network suitable for medium level image processing,...
The objective of this paper is to evaluate performance of the level set approach applied to segmentation and tracking of noisy 3D images of computer-simulated blood-vessel phantoms and artificial vascular trees. Of particular interest was the segmentation of thin vessels, with diameter smaller that voxel size. Flood fill technique was also explored, for comparison. Quantitative measures of segmentation...
The issue of vessel tracking in a novel, simultaneous ToF-SWI (time-of flight, susceptibility weighted imaging) 3D images is considered. Properties of the ToF-SWI images are discussed briefly. Results of arterial tree tracking by means of multiscale filtering, flod-fill and level set methods are presented and compared. Human brain and phantom MR images are used in the study. Topics of future research...
This paper presents the 32times32 CMOS VLSI chip of synchronised oscillators network designed for fast segmentation and labeling of binary images. The network chip architecture and its functional blocs were briefly described. The hardware realisation of oscillator network provides much faster image segmentation when compared to computer simulation techniques. Segmentation results of sample biomedical...
Recent development of three-dimensional imaging techniques with application in medical science demands a development of appropriate 3D image analysis techniques. This paper presents a segmentation method based on three-dimensional network of synchronized oscillators applied for 3D MR liver images. Principles of oscillator network operation were described. The network was tested on sample 3D artificial...
In this paper we present classification of the thermal images in order to discriminate healthy and pathological cases during breast cancer screening. Different image features and approaches for data reduction and classification have been used. The most promised method was based on wavelet transformation and nonlinear neural network classifier.
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