Stochastic resonance-based tomographic transform for computed tomographic image enhancement of brain lesions

Journal of Computer Assisted Tomography

Volumn 32, Issue 6, 2008, Pages 966-974

  Rallabandi, V P Subramanyam ^a   Roy, Prasun Kumar ^a  

^a NATIONAL BRAIN RESEARCH CENTRE   (India)

Author keywords

Computed tomography; Image enhancement; Noise; Statistical thermodynamics; Stochastic resonance

Indexed keywords

COMPUTERIZED TOMOGRAPHY; MAGNETIC RESONANCE; NOISE ABATEMENT; QUANTUM THEORY; STATISTICAL MECHANICS; STOCHASTIC SYSTEMS; THERMODYNAMICS;

ACTIVE ZONE; BRAIN LESIONS; COMPUTED TOMOGRAPHIC; COMPUTED TOMOGRAPHY; COMPUTER ASSISTED; LATENT STRUCTURES; NOISE; STOCHASTIC RESONANCES; TOMOGRAPHIC IMAGES; TOMOGRAPHIC TRANSFORMS;

IMAGE ENHANCEMENT;

ARTICLE; ARTIFACT REDUCTION; ASTROCYTOMA; BRAIN DAMAGE; CASE REPORT; COMPUTER ASSISTED TOMOGRAPHY; CYSTICERCOSIS; DIAGNOSTIC ACCURACY; HUMAN; HUMAN CELL; HUMAN TISSUE; IMAGE ANALYSIS; IMAGE ENHANCEMENT; MENINGIOMA; NOISE REDUCTION; NONHUMAN; PRIORITY JOURNAL; PROCESS OPTIMIZATION; STOCHASTIC RESONANCE BASED TOMOGRAPHIC TRANSFORM; THERMODYNAMICS; TUBERCULOMA; ALGORITHM; AUTOMATED PATTERN RECOGNITION; BIOLOGICAL MODEL; BRAIN TUMOR; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; IMAGE QUALITY; METHODOLOGY; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS;

ALGORITHMS; BRAIN NEOPLASMS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; HUMANS; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; STOCHASTIC PROCESSES; TOMOGRAPHY, X-RAY COMPUTED;

EID: 62149094385     PISSN: 03638715     EISSN: None     Source Type: Journal    
DOI: 10.1097/RCT.0b013e318159c638     Document Type: Article

References (26)

1. Gammaitoni L, Hanggi P, Jung P, et al. Stochastic resonance. Rev Modern Physics. 1998;70:223-287.
2. Horsthemke W, Lefever R. Noise-induced Transitions: Applications to Physics, Chemistry and Biology. New York: Springer; 2006.
3. Andi B, Graziani S. Stochastic Resonance: Theory and Applications. Berlin: Springer; 2000.
4. Hongler M, Meneses Y, Beyeler A, et al. The resonant retina: exploiting vibration noise to optically detect edges in an image. IEEE Trans Pattern Anal Mach Intell. 2003;25:1051-1062.
5. Marks R, Thompson B, El-Sharkawi M, et al. Stochastic resonance of a threshold detector: image visualization and explanation. Proc IEEE Int Symp Syst Circ. 2002;4:521-523.
6. Mitaim S, Kosko B. Adaptive stochastic resonance. Proc IEEE. 1998;86: 2152-2183.
7. Alibegov B. Stochastic Resonance in x-ray. Astron Lett. 1996;22: 564-566.
8. Thomsen H, Merckmann P, Logager V. Enhanced CT or MRI: A choice between contrast medium induced nephropathy and nephrogenic systemic fibrosis. Acta Radiol. 2007;48:593-596.
9. Li H, Liu K, Lo S. Fractal modeling and segmentation for the enhancement of microcalcifications in digital mammograms. IEEE Trans Med Imag. 1997;16:785-798.
10. Soares J, Leandro J, Cesar R, et al. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans Med Imag. 2006;25:1214-1222.
11. Villain N, Goussard Y, Idier J, et al. Three-dimensional edge-preserving image enhancement for computed tomography. IEEE Trans Med Imag. 2003;22:1275-1287.
12. Pan X, Yu L, Kao C. Spatial-resolution enhancement in computed tomography. IEEE Trans Med Imag. 2005;24:246-253.
13. Chang D, Wu W. Image contrast enhancement based on a histogram transformation of local standard deviation. IEEE Trans Med Imag. 1998;17:518-531.
14. Przelaskowski A, Sklinda K, Bargiea P, et al. Improved early stroke detection: wavelet-based enhancement of computerized tomography exams. Comput Biol Med. 2007;37:524-533.
15. Roy PK. Stochastic resonance imaging: preliminary studies on the brain as a stochastic processor. Lect Notes Comput Sci. 2004;3316: 96-103.
16. Einstein A. Investigations on the Theory of Brownian Movement. New York: Dover; 1956.
17. Chandrasekhar S. Stochastic problems in physics. Rev Modern Physics. 1943;15:1-89. Reprinted in: Wax N, ed. Noise and Stochastic Processes. New York: Dover; 1954:3-91.
18. Deans SR. Applications of the Radon Transform. New York: Wiley; 1983.
19. Gard TC. Introduction to Stochastic Differential Equations. New York: Marcel-Dekker; 1998.
20. Beutel J, Kundel H, van Metter R. Handbook of Medical Imaging: Vo l 1 - Physics & Psychophysics. New York: SPIE-IEEE Press; 2000.
21. Michelson A. Studies in Optics. New York: Dover; 1997.
22. Schwarcz A, Ursprung Z, Berente Z, et al. In vivo brain edema classification. J Magn Reson Imaging. 2007;25:26-31.
23. Maulaz A, Jozwika B, Michel P, et al. Selecting patients for early stroke treatment with penumbra images. Cerebrovasc Dis. 2005;20: 19-24.
24. Simonotto E, Riana M, Seife C, et al. Visual perception of stochastic resonance. Phys Rev Lett. 1997;78:1186-1189.
25. Roy P. Stochastic resonance as an emerging technique for neuromodula-tion: using nonlinear dynamics to analyze EEG. J Quant Linguist. 2004;11:49-77.
26. Prigogine I, Kondepudi D. Modern Thermodynamics: From Heat Engines to Dissipative Structures. New York: Wiley; 2000.