2013, Number 1
<< Back Next >>
Revista Cubana de Informática Médica 2013; 5 (1)
Segmentation of magnetic resonance images of the brain based on generalized regression neural networks
Monne CY, Monne RD
Language: Spanish
References: 17
Page: 1-13
PDF size: 221.41 Kb.
ABSTRACT
The analysis of structural changes in the brain through Magnetic Resonance Images may provide useful information for the diagnosis and clinical management of patients with dementia. While the degree of sophistication achieved by the MRI equipment is high, the quantification of structures and tissues has not been completely solved. The segmentations that these equipment provide nowadays, fail on those structures where the edges are not clearly defined. This paper presents a method for automatic segmentation of magnetic resonance images of the brain, based on the use of generalized regression neural networks using genetic algorithms for adjusting parameters. The network is trained from a single image and classifies rest of them whenever magnetic resonance images were acquired with the same protocol. A method of measuring the progressive atrophy and possible changes compared to a therapeutic effect should be essentially automatic and therefore independent of the radiologist.
REFERENCES
[1] Davis P, Gray A, Albert M et al. The Consortium to Establish a Registry for Alzheimer's Disease. Neurology. 1992; 42:1676-1680.
[2] Fox N, Freeborough P. Brain Atrophy Progression Measured from Registered Serial MRI: Validation and Application to Alzheimer Disease. J. Mag. Res. Imag. 1997; 7:1069-1075.
[3] Manes F. Resonancia magnética nuclear en la Enfermedad de Alzheimer. Rev. Neurol. Arg. 2000;25: 29-37.
[4] Raya SP. Low level segmentation of 3D resonance brain images. IEEE Trans. Med. Imaging; 1990; 9: 2.
[5] Chen S, Wei-chun L, Chen C. Medical image understanding system based on Dempster-Shafer reasoning. SPIE Biomedical Imag. Processing II. San Jose, California; 1991.
[6] Clarke LP et al. Comparison of Supervised Pattern Recognition Techniques and Unsupervised Methods for MRI Segmentation. Proc. SPIE Medical Imaging VI Conference, Vol. 1652, SPIE Press, Bellingham, WA: 668-677; 1992.
[7] Vannier MW, Brunsden BS, Hildebolt CF, Falk D, Cheverud JM, Figiel GS, Perman WH, Kohn A, Robb RA, YoffieRL. Brain surface cortical sulcal lengths: quantification with three- dimensional MR imaging. Radiology. 1991; 180:479-484.
[8] Gerig G, Martin J, Kikinis R. Unsupervised tissue type segmentation of 3D dual-echo MR head data. Image Vision Computer. 1992; 10: 349–360.
[9] Alfano B, Brunetti B et al. Unsupervised, automated segmentation of the normal brain using multispectral relaxometric magnetic resonance approach. Magn. Reson. Med. 1992; 37: 84-93.
[10] Bartlett T, Vannier M, Mc Keel D, Gado M, Hildebolt C, Walkup R. Interactive segmentation of cerebral gray matter, white matter, and CSF: photographic and MR images.Comp. Med. Imag. Graphics. 1994 18(6): 449-460.
[11] Abras G, Ballarin V, González M. Aplicación de Reconocimiento de Patrones en la Clasificación de Tejido Cerebral. Actas del Congreso Argentino de Bioingeniería. Tafí del Valle. Septiembre 2001. (Publicadas en CD).
[12] Parzen E. On estimation of a probability density function and mode. Annals of Mathematical Statistics. 1962; 33: 1065-1076.
[13] Specht D. A General Regression Neural Network. IEEE Transactions on Neural Networks. 1991;2 (6): 588-596.
[14] Holland JH. Outline for a logical theory of adaptive systems. J. Assoc. Comput, Mach. 1962; 3: 297-314.
[15] Holland JH. Adaptation in Natural and Artificial Systems. Ann Arbor, MI: Univ. of Michigan Press; 1975.
[16] De Jong KA. On using genetic algorithms to search program spaces. Proc. 2nd Int. Conf. on Genetic Algorithms and Their Applications. Hillsdale, NJ: Lawrence Erlabaum: 210-216; 1987.
[17] Goldberg DE. Genetic Algorithms in Search, Optimization & Machine Learning. Addison-Wesley; 1989.