Non-linear least squares features transformation for improving the performance of probabilistic neural networks in classifying human brain tumors on MRI

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4707 LNCS, Issue PART 3, 2007, Pages 239-247

  Georgiadis, Pantelis ^a   Cavouras, Dionisis ^b   Kalatzis, Ioannis ^b   Daskalakis, Antonis ^a   Kagadis, George ^a   Sifaki, Koralia ^c   Malamas, Menelaos ^c   Nikiforidis, George ^a   Solomou, Ekaterini ^a  

^a UNIVERSITY OF PATRAS   (Greece)

^b UNIVERSITY OF WEST ATTICA   (Greece)

^c General Airforce Hospital   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

BRAIN; COMPUTER SOFTWARE; MAGNETIC RESONANCE IMAGING; NEURAL NETWORKS; TUMORS;

LEAST SQUARES FEATURES TRANSFORMATION; METASTASES; PROBABILISTIC NEURAL NETWORKS; SOFTWARE SYSTEMS;

FEATURE EXTRACTION;

EID: 38149119642     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74484-9_21     Document Type: Conference Paper

References (17)

1. Shen, S., Sandham, W., Granat, M., Sterr, A.: MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Trans Inf Technol Biomed. 9, 459-467 (2005)
2. Soltanian-Zadeh, H., Peck, D.J., Windham, J.P., Mikkelsen, T.: Brain tumor segmentation and characterization by pattern analysis of multispectral NMR images. NMR Biomed. 11, 201-208 (1998)
3. Lerski, R.A., Straughan, K., Schad, L.R., Boyce, D., Bluml, S., Zuna, I.: MR image texture analysis-an approach to tissue characterization. Magn Reson Imaging 11, 873-887 (1993)
4. Schad, L.R., Bluml, S., Zuna, I.: MR tissue characterization of intracranial tumors by means of texture analysis. Magn Reson Imaging 11, 889-896 (1993)
5. Herlidou-Meme, S., Constans, J.M., Carsin, B., Olivie, 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. Magn Reson Imaging 21, 989993 (2003)
6. 1H-MRS metabolic patterns for distinguishing between meningiomas and other brain tumors. Magnetic Resonance Imaging 21, 663-672 (2003)
7. 1H brain tumor spectra: A multicenter study. Magn Reson Med. 49, 29-36 (2003)
8. 1H MR spectra. J Magn Reson. 170, 164-175 (2004)
9. Lukas, L., Devos, A., Suykens, J.A., Vanhamme, L., Howe, F.A., Majos, C., Moreno-Torres, A., Van der Graaf, M., Tate, A.R., Arus, C., Van Huffel, S.: Brain tumor classification based on long echo proton MRS signals. Artif Intell Med. 31, 73-89 (2004)
10. Menze, B.H., Lichy, M.P., Bachert, P., Keim, B.M., Schlemmer, H.P., Hamprecht, F.A.: Optimal classification of long echo time in vivo magnetic resonance spectra in the detection of recurrent brain tumors. NMR Biomed. 19, 599-609 (2006)
11. Simonetti, A.W., Melssen, W.J.: Combination of feature-reduced MR spectroscopic and MR imaging data for improved brain tumor classification. NMR Biomed. 18, 34-43 (2005)
12. Devos, A., Simonetti, A.W., van der Graaf, M., Lukas, L., Suykens, J.A., Vanhamme, L., Buydens, L.M., Heerschap, A., Van Huffel, S.: The use of multivariate MR imaging intensities versus metabolic data from MR spectroscopic imaging for brain tumour classification. J Magn Reson. 173, 218-228 (2005)
13. Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans Syst Man Cybern. SMC 3, 610-621 (1973)
14. Galloway, M.M.: Texture analysis using grey level run lengths. Comp. Graph, and. Image Proc. 4, 172-179 (1975)
15. Theodoridis, S., Koutroumbas, K.: Pattern recognition. Academic Press, New York (1999)
16. Specht, D.F.: Probabilistic neural networks. Neural Networks 3, 109-118 (1990)
17. Ahmed, N., Rao, R.: Orthogonal transforms for digital signal processing. Springer, Heidelberg (1975)