Brain atrophy progress detection in MR images

• Autorzy: Kuczyński K. , Stęgierski R. , Siczek M.
• Języki publikacji: EN

Abstrakty

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).

Słowa kluczowe

EN

brain atrophy detection; fractal dimension; MRI; medical image classification

PL

wymiar fraktalny; obrazowanie metodą rezonansu magnetycznego; klasyfikacja medyczna obrazu

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2010
• Tom: Vol. 16
• Strony: 187--192
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

Kuczyński K.

• Institute of Computer Science, Maria Curie–Skłodowska University in Lublin, Pl. Marii Curie–Skłodowskiej 1, 20-031 Lublin, Poland

autor

Stęgierski R.

autor

Siczek M.

Bibliografia

• [1] BOESEN K., REHM K., SCHAPERA K., STOLTZNER S., WOODS R., LÜDERSC E., ROTTENBERG D., Quantitative comparison of four brain extraction algorithms, NeuroImage, Vol. 22, Issue 3, July 2004, pp. 1255–1261.
• [2] CZARNECKA A., SĄSIADEK M.J., HUDYMA E., KWAŚNICKA H., PARADOWSKI M., Computer-Interactive Methods of Brain Cortical Evaluation, In E. Pietka, J. Kawa (Eds.): Information Tech. in Biomedicine, ASC 47, Springer-Verlag Berlin Heidelberg 2008, pp. 173–178.
• [3] KUCZYŃSKI K., BUCZKO O., MIKOŁAJCZAK P., Fractal-dimension-based classification of radiological images, Polish Journal of Environmental Studies, Vol. 17, No. 3B, 2008, pp. 198–202.
• [4] KUCZYŃSKI K., MIKOŁAJCZAK P., Magnetic Resonance Image Classification Using Fractal Analysis, In E. Pietka, J. Kawa (Eds.): Information Tech. in Biomedicine, ASC 47, Springer-Verlag Berlin Heidelberg 2008, pp. 173–178.
• [5] KUCZYŃSKI K., MIKOŁAJCZAK P., Medical image registration – a study of accuracy, performance and applicability of the procedure, Polish Journal of Environmental Studies, Vol. 16, No. 4A, 2007, pp. 144-147.
• [6] MAJUMDAR S., PRASAD R., The fractal dimension of cerebral surfaces using magnetic resonance imaging, Comput. Phys., Nov/Dec 1988, pp. 69–73.
• [7] MATTES D., HAYNOR D. R., VESSELLE H., LEWELLEN T.K., EUBANK W., PET-CT image registration in the chest using free-form deformations, IEEE Trans. on Medical Imaging, Vol. 22(1), January 2003, pp. 120–128.
• [8] mBDR Home Page: http://mbdr.nbirn.net/, March 2009.
• [9] National Library of Medicine Insight Segmentation and Registration Toolkit (ITK) Documentation: http://www.itk.org/Doxygen/html/, March 2009.
• [10] SARKAR N., CHAUDHURI B.B., An effcient differential box-counting approach to compute fractal dimension of image, IEEE Trans. Systems, Man. Cybernet. Vol. 24 (1), 1994, pp. 115–120.
• [11] SMITH S.M., Fast robust automated brain extraction, Human Brain Mapping, Vol. 17(3), November 2002, pp. 143–155.
• [12] SMITH S.M., ZHANG Y., JENKINSON M., CHEN J., MATTHEWS P.M., FEDERICO A., DE STEFANO N., Accurate, robust and automated longitudinal and cross-sectional brain change analysis, NeuroImage, Vol. 17(1), 2002, pp. 479–489.
• [13] SMITH S.M., JENKINSON M., WOOLRICH M.W., BECKMANN C.F., BEHRENS T.E.J., JOHANSEN-BERG H., BANNISTER P.R., DE LUCA M., DROBNJAK I., FLITNEY D.E., NIAZY R., SAUNDERS J., VICKERS J., ZHANG Y., DE STEFANO N., BRADY J.M., MATTHEWS P.M., Advances in functional and structural MR image analysis and implementation as FSL, NeuroImage, Vol. 23(S1), 2004, pp. 208–219.
• [14] VIOLA P. WELLS W.M., Alignment by maximization of mutual information, IJCV Vol. 24(2), 1997, pp. 137–154.
• [15] WOOLRICH M.W., JBABDI S., PATENAUDE B., CHAPPELL M., MAKNI S., BEHRENS T., BECKMANN C., JENKINSON M., SMITH S.M., Bayesian analysis of neuroimaging data in FSL, NeuroImage, Vol. 45, 2009, pp. 173–186.
• [16] World Health Organization (WHO), International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10), Chapter V Mental and behavioural disorders, F00 – F07, 2007.
• [17] ZHANG Y., BRADY M., SMITH S., Segmentation of brain MR images through a hidden Markov random field model and the expectation maximization algorithm, IEEE Trans. on Medical Imaging, Vol. 20(1), 2001, pp. 45–57.