Wyniki wyszukiwania - BazTech

1

Texture-based identification of dystrophy phase. Indicating the most suitable features for therapy testing

Duda D.

Journal of Medical Informatics & Technologies

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2018

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Vol. 27

29--40

EN

In this study, texture analysis (TA) is applied for characterization of dystrophic muscles visualized on T2-weighted Magnetic Resonance (MR) images. The study proposes a strategy for indicating the textural features that are the most appropriate for testing the therapies of Duchenne muscular dystrophy (DMD). The strategy considers that muscle texture evolves not only along with the disease progression but also with the individual’s development. First, a Monte Carlo (MC) procedure is used to assess the relative importance of each feature in identifying the phases of growth in healthy controls. The features considered as age-dependent at a given acceptance threshold are excluded from further analyses. It is assumed that their application in therapies’ evaluation may entail an incorrect assessment of dystrophy response to treatment. Next, the remaining features are used in differentiation among dystrophy phases. At this step, an MC-based feature selection is applied to find an optimal subset of features. Experiments are repeated at several acceptance thresholds for age-dependent features. Different solutions are finally compared with two classifiers: Neural Network (NN) and Support Vector Machines (SVM). The study is based on the Golden Retriever Muscular Dystrophy (GRMD) model. In total, 39 features provided by 8 TA methods (statistical, filter- and model-based) are tested.

2

Automatic segmentation of brain tumors using tensor analysis and multimodal MRI

Jackowski K., Manhães-Savio A., Cyganek B.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

165--172

EN

Glioma detection and classification is an critical step to diagnose and select the correct treatment for the brain tumours. There has been advances in glioma research and Magnetic Resonance Imaging (MRI) is the most accurate non-invasive medical tool to localize and analyse brain cancer.The scientiﬁc global community has been organizing challenges of open data analysis to push forward automatic algorithms to tackle this task. In this paper we analyse part of such challenge data, the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS), with novel algorithms using partial learning to test an active learning methodology and tensor-based image modelling methods to deal with the fusion of the multimodal MRI data into one space. A Random Forest classifier is used for pixel classification. Our results show an error rates of 0.011 up to 0.057 for intra-subject classification. These results are promising compared to other studies. We plan to extend this method to use more than 3 MRI modalities and present a full active learning approach.

3

Analysis and comparison of symmetry based lossless and perceptually lossless algorithms for volumetric compression of medical images

Chandrika B. K., Aparna P., Sumam D. S.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

147--154

EN

Modern medical imaging techniques produce huge volume of data from stack of images generated in a single examination. To compress them several volumetric compression techniques have been proposed. Performance of these compression schemes can be improved further by considering the anatomical symmetry present in medical images and incorporating the characteristics of human visual system. In this paper a volumetric medical image compression algorithm is presented in which perceptual model is integrated with a symmetry based lossless scheme. Symmetry based lossless and perceptually lossless algorithms were evaluated on a set of three dimensional medical images. Experimental results show that symmetry based perceptually lossless coder gives an average of 8.47% improvement in bit per pixel without any perceivable degradation in visual quality against the lossless scheme.

4

Brain atrophy progress detection in MR images

Kuczyński K., Stęgierski R., Siczek M.

Journal of Medical Informatics & Technologies

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2010

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Vol. 16

187--192

EN

Alzheimer's, Parkinson's and other dementive diseases currently pose an important social problem. High brain atrophy level is one of the most important symptoms of these disorders, but it also may result from normal ageing processes. The purpose of the presented research is to design methods that support detection of dementia symptoms in radiological images. The proposed framework consists of image registration procedure, brain extraction and tissue segmentation and the exact analysis of image series (fractal and volumetric properties).

5

Registration and normalization of MRI/PET images

Rumiński J., Suchowirski M.

Journal of Medical Informatics & Technologies

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2005

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Vol. 9

159--166

EN

Parametric imaging is more and more popular in dynamic brain studies. It enables to quantitatively or semi-quantitatively estimate physiological state and processes in brain. Parametric images represent spatial distribution of parameter values calculated for chosen mathematical model of the process or object. This work compares different methods of geometrical transformations for image registration and normalization. Appropriate method for image registration and normalization (in reference to atlases) is extremely important for common visualization of structural and parametric images in MRI and PET studies. Rigid and elastic geometrical transformations are implemented and compared. Additionally Delaunay triangulation and image morphing methods are used. Manual and proposed automatic registration and normalization methods are presented and compared based on MRI/PET and Talairach atlas images. Concluding, the proposed automatic image normalization method is accurate and using the combination of Delaunay and morphing methods can produce even better results.

6

Limitations of DSC-MRI for quantitative brain perfusion

Rumiński J., Bobek-Billewicz B.

Journal of Medical Informatics & Technologies

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2004

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Vol. 8

MM49--58

EN

Synthesis of quantitative parametric images in DSC-MRI is presented. Critical review of major limitations of the DSC-MRI method is discussed. It includes investigation of measurement procedures/conditions as well as parametric image synthesis methodology. Simulations, as well as phantom studies were used to verify theoretical limitations of the DSC-MRI. Especially, estimation of the contrast (Gd-DTPA) concentration by EPI measurements, the role of a phantom and its pipes orientation, influence of a bolus dispersion, bolus arrival time, and other signal parameters on an image quality. As a conclusion testing software package is proposed.

7

Computer system for diagnostic, intervention planning and surgery of brain

Cegielski M., Wołowiec Ł., Grzelak P.

Journal of Medical Informatics & Technologies

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2004

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Vol. 8

MM69--76

EN

The paper presents the computer system for 3D visualization of medical images. The framework of the system and algorithms used for segmentation and visualisation are described. Models of geometric and volumetric visualisation are compared. Additionally, plans for the future system development are stated.