Prioritization of brain MRI volumes using medical image perception model and tumor region segmentation

Computers in Biology and Medicine

Volumn 43, Issue 10, 2013, Pages 1471-1483

  Mehmood, Irfan ^a   Ejaz, Naveed ^a   Sajjad, Muhammad ^a   Baik, Sung Wook ^a  

^a Sejong University   (South Korea)

Author keywords

Image ranking; Medical image perception; MRI; Tumor detection and N cut segmentation

Indexed keywords

DIAGNOSTIC IMAGING; IMAGE RANKINGS; MEDICAL IMAGE PERCEPTION; QUANTITATIVE RESULT; REGION SEGMENTATION; SCIENTIFIC VALUES; TUMOR DETECTION; VISUAL PERCEPTION;

IMAGE SEGMENTATION; TUMORS;

MAGNETIC RESONANCE IMAGING;

AREA UNDER THE CURVE; ARTICLE; BRAIN CANCER; BRAIN SIZE; DIAGNOSTIC ACCURACY; FALSE NEGATIVE RESULT; FALSE POSITIVE RESULT; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; MEDICAL IMAGE PERCEPTION; MEDICAL SCHOOL; MOVEMENT PERCEPTION; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; RADIOLOGIST; VISUAL INFORMATION;

IMAGE RANKING; MEDICAL IMAGE PERCEPTION; MRI; TUMOR DETECTION AND N-CUT SEGMENTATION;

ARTIFACTS; BRAIN; BRAIN NEOPLASMS; DATABASES, FACTUAL; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; ROC CURVE;

EID: 84882951068     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2013.07.001     Document Type: Article

References (48)

1. Porter K.R., McCarthy B.J., Freels S., Kim Y., Davis F.G. Prevalence estimates for primary brain tumors in the United States by age, gender, behavior, and histology. Neuro-oncology 2010, 12:520-527.
2. Canadian cancer society, . http://www.cancer.ca/en/cancer-information/cancer-101/canadian-cancer-statistics-publication/?region=nb.
3. BBC Research, Emerging Markets for Advanced Medical Technologies, 2010, . http://www.bccresearch.com/market-research/healthcare/advanced-medical-technologies-hlc082a.htm.
4. Sze G., Milano E., Johnson C., Heier L. Detection of brain metastases: comparison of contrast-enhanced MR with unenhanced MR and enhanced CT. Am. J. Neurorad. 1990, 11:785-791.
5. Castano R., Wagstaff K., Song L., Anderson R. Validating rover image prioritizations. The Interplanetary Network Progress Report 2005, 42:160. MIT Libraries, CSAIL Technical Reports.
6. Ejaz N., Mehmood I., Wook Baik S. Efficient visual attention based framework for extracting key frames from videos. Signal Process. Image Commun. 2013, 28:34-44.
7. Ejaz N., Mehmood I., Baik S.W. MRT letter: Visual attention driven framework for hysteroscopy video abstraction. Microsc. Res. Tech. 2013, 76:559-563.
8. Manning D., Gale A., Krupinski E. Perception research in medical imaging. Br. J. Rad. 2005, 78:683-685.
9. Pizer S.M., ter Haar Romeny B.M. Fundamental properties of medical image perception. J. Dig. Imag. 1991, 4:1-20.
10. Kundel H., Nodine C. A visual concept shapes image perception. Radiology 1983, 146:363-368.
11. Krupinski E.A., Tillack A.A., Richter L., Henderson J.T., Bhattacharyya A.K., Scott K.M., Graham A.R., Descour M.R., Davis J.R., Weinstein R.S. Eye-movement study and human performance using telepathology virtual slides: implications for medical education and differences with experience. Hum. Pathol. 2006, 37:1543.
12. Krupinski E.A. The importance of perception research in medical imaging. Radiat. Med. 2000, 18:329-334.
13. R. Castano, R.C. Anderson, T. Estlin, D. DeCoste, F. Fisher, D. Gaines, D. Mazzoni, M. Judd, Rover traverse science for increased mission science return, in: Proceedings for the 2003 IEEE Aerospace Conference, vol. 8, 2003, pp. 3629-3636.
14. H. Wolf, D. Deng, How interesting is this? Finding interest hotspots and ranking images using an MPEG-7 visual attention model, Annual Colloquium of the Spatial Information Research Centre, 17(2005) 67-76.
15. Shi J., Malik J. Normalized cuts and image segmentation, pattern analysis and machine intelligence. IEEE Trans. 2000, 22:888-905.
16. Fischl B., Sereno M.I., Tootell R.B., Dale A.M. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 1999, 8:272-284.
17. Lawson J.A., Vogrin S., Bleasel A.F., Cook M.J., Bye A.M. Cerebral and cerebellar volume reduction in children with intractable epilepsy. Epilepsia 2005, 41:1456-1462.
18. Shattuck D.W., Sandor-Leahy S.R., Schaper K.A., Rottenberg D.A., Leahy R.M. Magnetic resonance image tissue classification using a partial volume model. NeuroImage 2001, 13:856-876.
19. Shan Z.Y., Yue G.H., Liu J.Z. Automated histogram-based brain segmentation in T1-weighted three-dimensional magnetic resonance head images. NeuroImage 2002, 17:1587.
20. Yoon U.-C., Kim J.-S., Kim J.-S., Kim I.-Y., Kim S.I. Adaptable fuzzy c-means for improved classification as a preprocessing procedure of brain parcellation. J. Dig. Imag. 2001, 14:238-240.
21. Lemieux L., Hagemann G., Krakow K., Woermann F.G. Fast, accurate, and reproducible automatic segmentation of the brain in T1-weighted volume MRI data. Magnet. Reson. Med. 1999, 42:127-135.
22. Boesen K., Rehm K., Schaper K., Stoltzner S., Woods R., Lüders E., Rottenberg D. Quantitative comparison of four brain extraction algorithms. NeuroImage 2004, 22:1255-1261.
23. Manzoor U., Nefti S. iDetect: content based monitoring of complex networks using mobile agents. Appl. Soft Comput. 2012, 12:1607-1619.
24. Manzoor U., Nefti S., Rezgui Y. Categorization of malicious behaviors using cognitive agents-CMBCA. Data Knowl. Eng. 2013, 85:40-56.
25. Mehmood I., Baik R., Baik S.W. Automatic Segmentation of Region of Interests in MR Images Using Saliency Information and Active Contours, in: IT Convergence and Security 2012 2013, 537-544. Springer.
26. Attneave F. Some informational aspects of visual perception. Psychol. Rev. 1954, 61:183.
27. Coren S., Porac C., Theodor L.H. The effects of perceptual set on the shape and apparent depth of subjective contours. Attent. Percept. Psychophys. 1986, 39:327-333.
28. Blakemore C., Over R. Curvature detectors in human vision. Perception 1974, 3:3-7.
29. Dobbins A., Zucker S.W., Cynader M.S. Endstopped neurons in the visual cortex as a substrate for calculating curvature. Nature 1987, 329:438-441.
30. Von der Heydt R., Peterhans E., Baumgartner G. Illusory contours and cortical neuron responses. Ann. Inst. Oceanogr. 1982, 58:297.
31. Wilson H.R., Wilkinson F., Asaad W. Concentric orientation summation in human form vision. Vision Res. 1997, 37:2325-2330.
32. Wolfe J.M., Yee A., Friedman-Hill S.R. Curvature is a basic feature for visual search tasks. Perception 1992, 21:465.
33. Massanes F., Brankov J.G. Motion perception in medical imaging, in: SPIE Medical Imaging. Internat. Soc. Opt. Photon. 2011, 7966:10.
34. Ehrenstein W. Basics of seeing motion. Arquivos brasileiros de oftalmologia 2003, 66:44-52.
35. B.D. Lucas, T. Kanade, An Iterative Image Registration Technique With an Application to Stereo Vision, in: Proceedings of the 7th International Joint Conference on Artificial Intelligence, 1981.
36. Huang L., Pashler H. A Boolean map theory of visual attention. Psychol. Rev. 2007, 114:599.
37. L. Xu, W. Li, D. Schuurmans, Fast normalized cut with linear constraints, in: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on IEEE, 2009, pp. 2866-2873.
38. Eriksson A., Olsson C., Kahl F. Normalized cuts revisited: a reformulation for segmentation with linear grouping constraints. J. Math. Imag.Vision 2011, 39:45-61.
39. Yu S.X., Shi J. Segmentation given partial grouping constraints, pattern analysis and machine intelligence. IEEE Trans. 2004, 26:173-183.
40. Kim H., Kim W., Lee M., Song E., Loh J.J. Tumor volume and uterine body invasion assessed by MRI for prediction of outcome in cervical carcinoma treated with concurrent chemotherapy and radiotherapy. Jpn. J. Clin. Oncol. 2007, 37:858-866.
41. Brainweb, BrainWeb: Simulated Brain Database, . http://brainweb.bic.mni.mcgill.ca/cgi/brainweb2.
42. M.D. Keith A. Johnson, J. Alex Becker., The Whole Brain Atlas, in, pp. Harvard Medical School, Brain MRI Database, Online: . http://www.med.harvard.edu/aanlib/.
43. Department of Radiology, Pakistan Institute of Medical Sciences, Islamabad, Pakistan, Online: . http://www.pims.gov.pk/radiology.htm.
44. J. Harel, C. Koch, P. Perona, Graph-based visual saliency, Adv. Neural Inf. Process. Syst. 19 (2006) 545-552.
45. Margolin R., Zelnik-Manor L., Tal A. Saliency for image manipulation. Visual Comput. 2012, 1-12.
46. T. Judd, F. Durand and A. Torralba, A Benchmark of Computational Models of Saliency to Predict Human Fixations, MIT Libraries, CSAIL Technical Reports, 2012.
47. Fawcett T. An introduction to ROC analysis. Pattern Recognit. Lett. 2006, 27:861-874.
48. E. Levina, P. Bickel, The earth mover's distance is the Mallows distance: some insights from statistics, in: Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on IEEE, 2001, pp. 251-256.