Detecting Pathologies with Homology Algorithms in Magnetic Resonance Images of Brain

• Autorzy: Żelawski M.
• Języki publikacji: EN

Abstrakty

EN

This paper describes a method for detecting the presence of pathological changes in two-dimensional brain images from magnetic resonance examination. The proposed idea is based on homology theory, which makes it easily extendable to three-dimensional brain images in particular it may be applied to the computer tomography data.

Słowa kluczowe

EN

homology algorithm; brain tumor; MR imaging; medical imaging

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2009
• Tom: Vol. 18, No. 3
• Strony: 253--266
• Opis fizyczny: Bibliogr. 38 poz., il.

Twórcy

autor

Żelawski M.

• Institute of Computer Science, Jagiellonian University, Kraków, Poland,

Bibliografia

• [1] Clarke L.P., et al., MRI segmentation: methods and application, Magnetic Resonance Imaging, 13(3):343-368, 1995.
• [2] Vandermeulen D., Descombes X., Suetens P., Marchal G., Unsupervised Regularized Classification of Multi-Spectral MRI, Katholieke Universiteit Leuven, Belgium, Tech. Rep. KUL/ESAT/MI2/9608, 1996.
• [3] Dickson S., Thomas B., Using neural networks to automatically detect brain tumours in MR images, International Journal of Neural Systems, 4(l):91-99, 1997.
• [4] Worth A.J., Makris N., Caviness Jr. V.S., Kennedy D.N., Neuroanatomical Segmentation in MRI: Technological Objectives, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 11, No. 8, 1161-1187, 1997.
• [5] Zhu Y., Yan H., Computerized tumor boundary detection using a Hopfield neural network, IEEE Transactions on Medical Imaging, 16:55-67, 1997.
• [6] Clark M., Hall L., Goldgof D., Velthuizen R., Murtagh F., Silberger M., Automatic tumor segmentation using knowledge-based techniques, IEEE Transactions on Medical Imaging 117, 187-201, 1998.
• [7] Clark M., Hall L., Goldgof D., Velthuizen R., Murtagh F., Silberger M., Unsupervised Brain Tumor Segmentation Using Knowledge-Based and Fuzzy Techniques, In: Fuzzy and neuro-fuzzy systems in medicine, H.N. Teodorescu, A- Kandel, L.C. Jain, editors, Chapter 5, CRC Press., 1998
• [8] Kaczynski T., Mrozek M., Slusarek M., Homology computation by reduction of chain complexes, Computers and Math. Appl., 35(4), 59-70, 1998.
• [9] Niessen W.J., et al., Multiscale segmentation of three dimensional MR brain images, International Journal of Computer Vision, Vol. 31, no. 2/3, pp. 185-202, 1999.
• [10] Allili M., Ziou D., Extraction of topological properties of images via cubical homology, preprint, Georgia Inst. Tech., Atlanta, 2000.
• [11] Fletcher-Heath L., Hall L., Goldgof D., Murtagh F., Automatic Segmentation of Non-enhancing Brain Tumors in Magnetic Resonance Images, Artificial Intelligence in Medicine 21, 43-63, 2001.
• [12] Tadeusiewicz R., Ogiela M., Structural Methods for X-Ray Image Recognition, ESTRO Teaching Course on Imaging for Target Volume Determination in Radiotherapy. Cracow Conference, 2001.
• [13] Lassouaoui N., Hamami L., Brain image segmentation using mathematical morphology, In:. 2nd IASTED International Conference on Visualization Imaging, and Image Processing, VIIP2002, Malaga, Spain, 9-12, 2002.
• [14] Moon N., Bullitt E., van Leemput K., Gerig G., Automatic Brain and Tumor Segmentation, MICCAI 2002, LNCS 2489:372-379, 2002.
• [15] Niethammer M., Stein A.N., Kalies W.D., Pilarczyk P., Mischaikow K., Tannenbaum A., Analysis of blood vessel topology by cubical homology, Proceedings of the International Conference on Image Processing (2002), Vol. 2, 969-972.
• [16] Herlidou-Meme S., Constans J., Carsin B., Olivie D., Eliat P., Nadal-Desbarats L., Gondry C, Rumeur E.L., Idy-Peretti I., de Certaines J., MPJ texture analysis on texture test objects, normal brain and intracranial tumors, Magnetic Resonance Imaging, 21(9):989-993, 2003.
• [17] Iftekharuddin K.M., Jia W., Marsh R., Fractal analysis of tumor in brain MR images, Machine Vision and Applications, Vol. 13, No 5-6, pp. 352-362, 2003.
• [18] Mahmoud-Ghoneim D., Toussaint G., Constans J.M., De Certaines J.D., Three dimensional texture analysis in MRI: a preliminary evaluation in gliomas, Magnetic Resonance Imaging, Vol. 21, No. 9., pp. 983-987, 2003.
• [19] Prastawa M., Bullitt E., Moon N., van Leemput K., Gerig G., Automatic brain tumor segmentation by subject specific modification of atlas priors, Academic Radiology, 10(12):1341-1348, 2003.
• [20] Sędziwy A., Detecting Pathologies in Magnetic Resonance Images of Brain, Image Processing & Communications, Vol. 9, no. 1, pp. 53-62, 2003.
• [21] Kaczynski T., Mischaikow K., Mrozek M., Computational Homology, Springer-Verlag, Appl. Math. Sci. Series Vol. 157, New York, 2004 .
• [22] Prastawa M., Bullitt E., Ho S., Gerig G., A Brain Tumor Segmentation Framework Based on Outlier Detection, Medical Image Analysis, 8(3):275-283, 2004.
• [23] Sędziwy A., Adaptive Algorithm of Detection of Pathological Changes in Brain Image Diagnostics, Automatyka AGH, 8/3, 151-159, 2004.
• [24] Brown R.A., Zlatescu M.C., Cairncross J.G., Mitchel J.R., Texture Analysis for Non-Invasive Identification of Brain Tumor Genotype from MRI, Visualization, Imaging and Image Processing, ACTA Press, 480(116):459-464, 2005.
• [25] Droske M., Meyer M., Rumpf M., Schaller C., An adaptive level set method for interactive segmentation of intracranial tumors, Neurosurgical Research 27(4), pp. 363-370, 2005.
• [26] Glotsos D., Tohka J., Ravazoula P., Cavouras D., Nikiforidis G., Automated diagnosis of brain tumours astrocytomas using probabilistic neural network clustering and support vector machines, Int. J. Neural Syst. 15(1-2):1-11, 2005.
• [27] Mischaikow K., Mrozek M., Pilarczyk P., Graph approach to the computation of the homology of continuous maps, Foundations of Computational Mathematics, 5, 199-229, 2005.
• [28] Xie K., Yang J., Zhang Z.G., Zhu Y.M., Semi-automated brain tumor and edema segmentation using MRI, European Journal of Radiology, 56:12-19, 2005.
• [29] Zook J.M., Iftekharuddin K.M., Statistical analysis of fractal-based brain tumor detection algorithms, Magn Reson Imaging, 23(5):671-8, 2005.
• [30] Żelawski M., Pattern Recognition Based on Homology Theory, Machine Graphics & Vision, 14(3):309-324, 2005.
• [31] Lau Phooi-Yee, Ozawa S., A simple method for detecting tumor in T2-weighted MRI brain images: an image-based analysis, Trans, on Information and Systems - IEICE, Vol. E89-D, No. 3, pp. 1270-1279, 2006.
• [32] Georgiadis P., Cavouras D., Kalatzis I., Daskalakis A., Kagadis G., Sifaki K., Malamas M., Nikiforidis G., Solomou E., Non-linear Least Squares Features Transformation for Improving the Performance of Probabilistic Neural Networks in Classifying Human Brain Tumors on MRI, ICCSA (3), 239-247, 2007 .
• [33] Georgiadis P., Cavouras D., Kalatzis I., Daskalakis A., Kagadis G., Sifaki K., Malamas M., Nikiforidis G., Solomou E., Improving brain tumor characterization on MRI by probabilistic neural networks and non-linear transformation of textural features, Comput Methods Programs Biomed., 89(l):24-32, 2008.
• [34] Mrozek M., Batko B., Coreduction Homology Algorithm, Discrete and Computational Geometry, accepted, 2008.
• [35] Mrozek M., Pilarczyk P., Żelazna N., Homology Algorithm Based on Acyclic Subspace, Computers &: Mathematics with Applications, Vol. 55, Issue 11, 2395-2412, 2008.
• [36] Yeh Jinn-Yi, Fu J.C., A hierarchical genetic algorithm for segmentation of multi-spectral human- brain MRI, Expert Systems with Applications, 34(2):1285-1295.
• [37] Pilarczyk P., Homology Software, http://www.math.gatech.edu/-chom.
• [38] Żelawski M., 2D Pattern Recognition Software, http://www.ii.uj.edu.pl/~zelawski/rec_hom.htm.