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Texture characterization for hepatic tumor recognition in multiphase CT

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
A new approach to texture characterization from dynamic CT scans of the liver is presented. Images with the same slice position and corresponding to three typical acquisition phases are analyzed simultaneously. Thereby texture evolution during the propagation of contrast product is taken into account. The method is applied to recognizing hepatie primary tumors. Experiments with various sets of texture parameters and two classification methods show that simultaneous analysis of texture parameters derived from three subsequent acquisition moments improves the classification accuracy.
Twórcy
autor
  • Faculty of Computer Science, Białystok Technical University, ul. Wiejska 45A, 15-351 Białystok, Poland, dordu@ii.pb.bialystok.pl
Bibliografia
  • 1. Bruno A., Collorec R., Bézy-Wendling J., Reuzé P., Rolland Y.: Texture analysis in medical imaging, In: Roux C., Coatrieux J. L. (Eds.): Contemporary Perspectives in Three-dimensional Biomedical Imaging, IOS Press 1997, 133-164.
  • 2. Haralick R. M.: Statistical and structural approaches to texture, Proc. IEEE 1979, 67, 786-804.
  • 3. Haralick R. M., Shanmugam K., Dinstein I.: Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics 1973, 3, 610-621.
  • 4. Galloway M. M.: Texture analysis using gray level run lengths. Computer Graphics and Image Processing 1975, 4, 172-179.
  • 5. Chen C., Daponte J. S., Fox M. D.: Fractal feature analysis and classification in medical imaging, IEEE Transactions on Medical Imaging 1989, 8, 133-142.
  • 6. Cross G. R., Jain A. K.: Markov random fields texture models. IEEE Transactions on Pattern Analysis and Machine Intelligence 1985, 5(1), 25-39.
  • 7. Clausi D. A., Jernigan M. E.: Designing gabor filters for optimal texture separability. Pattern Recognition 2000, 33, 1836-1849.
  • 8. Mallat S. G.: A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1989, 11(7), 674-693.
  • 9. Haralick R., Sternberg S. R., Zhuang X.: Image analysis using mathematical morphology. IEEE Transactions on Pattern Analysis and Machine Intelligence 1989, 9(4), 532-550.
  • 10. Herlidou-Meme S., Constans J. M., Carsin B., Olivié D., Eliat P. A., Nadal-Desbarats L., Gondry C., Le Rumeur E., Idy-Peretti I., de Certaines J.D.: MRI texture analysis on texture test objects, normal brain and intracranial tumors. Magnetic Resonance Imaging 2003, 21, 989-993.
  • 11. Joo S., Yang Y. S., Moon W. K., Kim H. C.: Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features. IEEE Transactions on Medical Imaging 2004, 23(10), 1292-1300.
  • 12. Chappard D., Chennebault A., Moreau M., Legrand E., Audran, M., Basle M. F.: Texture analysis of X-ray radiograms is a more reliable descriptor of bone loss than mineral content in a rat model of localized disuse induced by the Clostridium botulinum toxin. Bone 2001, 28(1), 72-79.
  • 13. dos Santos Filho E., Yoshizawa M., Tanaka, A., Saijo, Y., Yambe T., Nitta, S.: Toward a neuro-fuzzy system for automatic segmentation and characterization of intravascular ultrasound images. SICE Annual Conference 2003, 2, 1586-1589.
  • 14. Gletsos M., Mougiakakou S. G., Matsopoulos G. K., Nikita K. S., Nikita A. S., Kelekis D.: A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a Neural Network classifier, IEEE Transactions on Information Technology in Biomedicine 2003, 7(3), 153-162.
  • 15. Mir A. H., Hanmandlu M., Tandon S. N.: Texture analysis of CT-images, IEEE Engineering in Medicine and Biology 1995, 5, 781-786.
  • 16. Chen E. L., Chung P. C., Chen C. L., Tsai H. M., Chang C. I.: An automatic diagnostic system for CT liver image classification. IEEE Transactions on Biomedical Engineering 1998, 45(6), 783-794.
  • 17. Husain S. A., Shigeru E.: Use of neutral networks for feature based recognition of liver region on CT images, Proc. of the IEEE Signal Processing Society Workshop 2000, 2, 831-840.
  • 18. Sariyanni C. P. A., Asvestas P., Matsopoulos G. K., Nikita K. S., Nikita A. S. Kelekis D.: A fractal analysis of CT liver images for the discrimination of hepatic lesions: A comparative study, Proc. Of the 23rd Annual EMBS International Conference 2001, 1557-1560.
  • 19. Valavanis I., Mougiakakou S. G., Nikita K. S., Nikita A.: Computer aided diagnosis of CT focal liver lesions by an ensemble of neural network and statistical classifiers, Proc. of the IEEE International Joint Conference on Neural Networks 2004, 3, 1929-1934.
  • 20. Krętowski M., Bezy-Wendling J., Duda D.: Classification of hepatic metastasis in enhanced CT images by dipolar decision tree, Proc. of 19th GRETSI 2003, 327-330.
  • 21. Duda D., Krętowski M., Bézy-Wendling J.: Texture-based classification of hepatic primary tumors in multiphase CT. Proc. of 7th MICCAI, LNCS Springer-Verlag 2004, 3217, 1050-1051.
  • 22. Duda D., Krętowski M., Bézy-Wendling J.: Texture analysis in medical image classification. Statistics and Clinical Practice, Lecture Notes of ICB Seminars 2005, 70, 83-89.
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BPZ1-0030-0027
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