Medical image analysis with artificial neural networks

Computerized Medical Imaging and Graphics

Volumn 34, Issue 8, 2010, Pages 617-631

  Jiang, J ^a   Trundle, P ^a   Ren, J ^a  

^a UNIVERSITY OF BRADFORD   (United Kingdom)

Author keywords

Intelligent computing; Medical imaging analysis; Neural networks

Indexed keywords

ARTIFICIAL NEURAL NETWORK; COMPUTATIONAL INTELLIGENCE; FIXED STRUCTURE; GLOBAL VIEW; IMAGING ANALYSIS; LITERATURE SURVEY; MEDICAL IMAGE ANALYSIS; MEDICAL IMAGE REGISTRATION; MEDICAL IMAGE SEGMENTATION; MEDICAL IMAGES; NETWORK DEVELOPMENT; NEURAL NETWORK APPLICATION; POST PROCESSING; PRACTICAL DEVELOPMENT; PRE-PROCESSING; RESEARCH COMMUNITIES; TRAINING PROCEDURES; VISUAL CONTENT;

COMPUTER AIDED DIAGNOSIS; COMPUTER AIDED NETWORK ANALYSIS; EDGE DETECTION; IMAGE ANALYSIS; IMAGE REGISTRATION; IMAGE SEGMENTATION; INDUSTRIAL RESEARCH; INTELLIGENT COMPUTING; MEDICAL COMPUTING; MEDICAL IMAGING; MEDICAL PROBLEMS;

NEURAL NETWORKS;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANCER DIAGNOSIS; COMPUTER ASSISTED DIAGNOSIS; DIAGNOSTIC IMAGING; DIGITAL MAMMOGRAPHY; IMAGE ANALYSIS; IMAGE ENHANCEMENT; IMAGE PROCESSING; IMAGE QUALITY; LUNG DISEASE; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; SIGNAL NOISE RATIO;

DIAGNOSTIC IMAGING; IMAGE INTERPRETATION, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER); REVIEW LITERATURE AS TOPIC;

EID: 77957910572     PISSN: 08956111     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compmedimag.2010.07.003     Document Type: Article

References (80)

1. Ge J., Sahiner B., Hadjiiski L.M., et al. Computer aided detection of clusters of microcalcifications on full field digital mammograms. Medical Physics 2006, 33(8):2975-2988.
2. Lo S.C.B., Chan H.P., Lin J.S., Li H., Freedman M.T., Mun S.K. Artificial convolution neural network for medical image pattern recognition. Neural Networks 1995, 8(7/8):1201-11214.
3. Nagel R.H., Nishikawa R.M., Papaioannou J., Doi K. Analysis of methods for reducing false positives in the automated detection of clustered microcalcifications in mammograms. Medical Physics 1998, 25(8):1502-1506.
4. Papadopoulossa D.I., Fotiadisb A., Likasb An automatic microcalcification detection system based on a hybrid neural network classifier. Artificial Intelligence in Medicine 2002, 25:149-167.
5. Christoyiani E., Dermatas G., Kokkinakis Fast detection of masses in computer aided mammography. IEEE Signal Processing Magazine 2000, 17(1):54-64.
6. Patrocinio A.C., Schiabel H., Benatti R.H., Goes C.E., Nunes F.L.S. Investigation of clustered microcalcification features for an automated classifier as part of a mammography CAD scheme. Proceedings of the 22nd annual EMBS international conference 2000, 1203-1205.
7. Setiono R. Extracting rules from pruned neural networks for breast cancer diagnosis. Artificial Intelligence in Medicine 1996, 8(1):37-51.
8. Xu X.W., Doi K., Kobayashi T., MacMahon H., Giger M.L. Development of an improved CAD scheme for automated detection of lung nodules in digital chest images. Medical Physics 1997, 24(9):1395-1403.
9. Zhou Z.H., Jiang Y., Yang Y.B., Chen S.F. Lung cancer cell identification based on artificial neural network ensembles. Artificial Intelligence in Medicine 2002, 24(1):25-36.
10. Keserci B., Yoshida H. Computerized detection of pulmonary nodules in chest radiographs based on morphological features and wavelet snake model. Medical Image Analysis 2002, 6(4):431-447.
11. Qian W., Zhukov T., Song D.S., Tockman M.S. Computerized analysis of cellular features and biomarkers for cytologic diagnosis of early lung cancer. Analytical and Quantitative Cytology and Histology 2007, 29(2):103-111.
12. Coppini G., Diciotti S., Falchini M., Villari N., Valli G. Neural networks for computer-aided diagnosis: detection of lung nodules in chest radiograms. IEEE Transactions on Information Technology in Biomedicine 2003, 7(4):344-357.
13. Mohamed E.I., Maiolo C., Linder R., Poppl S.J., De Lorenzo A. Artificial neural network analysis: a novel application for predicting site-specific bone mineral density. Acta Diabetologica 2003, 40(Suppl. 1):S19-S22.
14. Scott J.A. The lung scan and the abnormal chest X-ray: difficult diagnoses. Nuclear Medicine Communications 2004, 25(11):1137-1141.
15. Kobashi S., Kamiura N., Hata Y., Miyawaki F. Volume-quantization-based neural network approach to 3D MR angiography image segmentation. Image and Vision Computing 2001, 19(4):185-193.
16. Middleton R.I., Damper Segmentation of magnetic resonance images using a combination of neural networks and active contour models. Medical Engineering & Physics 2004, 26(1):71-86.
17. Lin J.S. Segmentation of medical images through a penalized fuzzy Hopfield network with moments preservation. Journal of The Chinese Institute of Engineers 2000, 23(5):633-643.
18. Lin K.C.R., Yang M.S., Liu H.C., Lirng J.F., Wang P.N. Generalized Kohonen's competitive learning algorithms for ophthalmological MR image segmentation. Magnetic Resonance Imaging 2003, 21(8):863-870.
19. Dokur Z., Olmez T. Segmentation of MR and CT images by using a quantiser neural network. Neural Computing & Applications 2003, 11(3-4):168-177.
20. Stalidis G., Maglaveras N., Efstratiadis S.N., Dimitriadis A.S., Pappas C. Model-based processing scheme for quantitative 4-D cardiac MRI analysis. IEEE Transaction on Information Technology in Biomedicine 2002, 6(1):59-72.
21. Chang C.L., Ching Y.T. Fuzzy Hopfield neural network with fixed weight for medical image segmentation. Optical Engineering 2002, 41(2):351-358.
22. Shen S., Sandham W., Granat M., Sterr A. MRI fuzzy segmentation of brain tissue using neighborhood attraction with neural-network optimization. IEEE Transactions on Information Technology in Biomedicine 2005, 9(3):459-467.
23. Chang C.Y., Chung P.C. Two-layer competitive based Hopfield neural network for medical image edge detection. Optical Engineering 2000, 39(3):695-703.
24. Chang C.Y., Chung P.C. Medical image segmentation using a contextual-constraint-based Hopfield neural cube. Image and Vision Computing 2001, 19(9-10):669-678.
25. Chang C.Y. Contextual-based Hopfield neural network for medical image edge detection. Optical Engineering 2006, 45(3). Art. No. 037006.
26. Matsopoulosa G.K., Mouravlianskya N.A., Asvestasa P.A., Delibasisa K.K., Koulouliasb V. Thoracic non-rigid registration combining self-organizing maps and radial basis functions. Medical Image Analysis 2005, 9(3):237-254.
27. Fritzke B. A growing neural gas network learns topologies. Advances in neural information processing systems, vol. 7 1995, MIT Press, Cambridge, MA, pp. 625-632.
28. Shang L.F., Lv J.C., Yi Z. Rigid medical image registration using PCA neural network. Neurocomputing 2006, 69(13-15):1717-1722.
29. Coppini G., Diciotti S., Valli G. Matching of medical images by self-organizing neural networks. Pattern Recognition Letters 2004, 25(3):341-352.
30. Fatemizadeh E., Lucas C., Soltanian-Zadeh H. Automatic landmark extraction from image data using modified growing neural gas network. IEEE Transactions on Information Technology in Biomedicine 2003, 7(2):77-85.
31. Di Bona S., Salvetti O. Neural method for three-dimensional image matching. Journal of Electronic Imaging 2002, 11(4):497-506.
32. Jiang J. Image compression with neural networks-a survey, image communication. Journal of EURASIP 1999, 14(9).
33. Panagiotidis N.G., Kalogeras D., Kollias S.D., Stafylopatis A. Neural network-assisted effective lossy compression of medical images. Proceedings of the IEEE 1996, 84(10):1474-1487.
34. Karlik B. Medical image compression by using vector quantization neural network (VQNN). Neural Network World 2006, 16(4):341-348.
35. Meyer-Base K., Jancke A., Wismuller S., Foo T., Martinetz Medical image compression using topology-preserving neural networks. Engineering Applications of Artificial Intelligence 2005, 18(4):383-392.
36. Jaiswal R.R., Gaikwad A.N. Neural network assisted effective lossy compression of medical images. IETE Technical Review 2006, 23(2):119-126.
37. Lo S.C.B., Li H., Freedman M.T. Optimization of wavelet decomposition for image compression and feature preservation. IEEE Transactions on Medical Imaging 2003, 22(9):1141-1151.
38. Suzuki K., Horiba I., Sugie N. Efficient approximation of neural filters for removing quantum noise from images. IEEE Transactions on Signal Processing 2002, 50(7):1787-1799.
39. Suzuki K., Abe H., MacMahon H., Doi K. Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN). IEEE Transactions on Medical Imaging 2006, 25(4).
40. Suzuki K., Horiba I., Ikegaya K., Nanki M. Recognition of coronary arterial stenosis using neural network on DSA system. Systems and Computers, Japan 1995, 26(8):66-74.
41. Suzuki K. Neural edge enhancer for supervised edge enhancement from noisy images. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003, 25(December (12)):1582-1596.
42. Hainc L., Kukal J. Role of robust processing in ANN de-noising of 2D image. Neural Network World 2006, 16(2):163-176.
43. Chen G.K., Chiueh T.D., Chen J.H. Active cancellation system of acoustic noise in MR imaging. IEEE Transactions on Biomedical Engineering 1999, 46(2):186-191.
44. Wu H.H.P. Patient information extraction in digitized X-ray imagery. Image and Vision Computing 2004, 22(3):215-226.
45. Chiu J.-S., Wang Y.-F., Su Y.-C., et al. Artificial neural network to predict skeletal metastasis in patients with prostate cancer. Journal of Medical Systems 2009, 33(April (2)):91-100.
46. Goodband J.H., Haas O.C.L., Mills J.A. A comparison of neural network approaches for on-line prediction in IGRT. Medical Physics 2008, 35(March (3)):1113-1122.
47. Kim J., Horwitz B. Investigating the neural basis for fMRI-based functional connectivity in a blocked design: application to interregional correlations and psycho-physiological interactions. Magnetic Resonance Imaging 2008, 26(5):583-593.
48. Marrelec G., Kim J., Doyon J., Horwitz B. Large-scale neural model validation of partial correlation analysis for effective connectivity investigation in functional MRI. Human Brain Mapping 2009, 30(3):941-950.
49. Carrington S.J., Bailey A.J. Are there theory of mind regions in the brain? A review of the neuroimaging literature. Human Brain Mapping 2008, 30(8):2313-2335.
50. Li K., Guo L., Nie J., Liu T. Review of methods for functional brain connectivity detection using fMRI. Computerized Medical Imaging and Graphics 2009, 33(2):131-139.
51. Farlow S.J. The GMDH algorithm. Self-organizing methods in modeling: GMDH type algorithms 1984, 1-24. Marcel Dekker, New York. S.J. Farlow (Ed.).
52. Ivakhnenko A.C. Polynomial theory of complex systems. IEEE Transactions on Systems, Man and Cybernetics 1971, (October):364-378.
53. Kondo C., Kondo T., Ueno J. Three-dimensional medical image analysis of the heart by the revised GMDH-type neural network self-selecting optimum neural network architecture. Artificial Life and Robotics 2009, 14(2):123-128.
54. Kondo T., Ueno J. Multi-layered GMDH-type neural network self-selecting optimum neural network architecture and its application to 3-dimensional medical image recognition of blood vessels. International Journal of Innovative Computing, Information and Control ICIC International 2008, 4(1):175-187.
55. Zhang J., Ge Y., Ong S.H., et al. Rapid surface registration of 3D volumes using a neural network approach. Image and Vision Computing 2008, 26(2):201-210.
56. Markaki V.E., Asvestas P.A., Matsopoulos G.K. Application of Kohonen network for automatic point correspondence in 2D medical images. Computers in Biology and Medicine 2009, 39(7):630-635.
57. Fu J.C., Chen C.C., Chai J.W., et al. Image segmentation by EM-based adaptive pulse coupled neural networks in brain magnetic resonance imaging. Computerized Medical Imaging and Graphics 2010, 34(4):308-320.
58. Suzuki K., Horiba I., Sugie N., Nanki M. Extraction of left ventricular contours from left ventriculograms by means of a neural edge detector. IEEE Transactions on Medical Imaging 2004, 23(3):330-339.
59. Lisboa P.J., Taktak A.F.G. The use of artificial neural networks in decision support in cancer: a systematic review. Neural Networks 2006, 19(4):408-415.
60. Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Computerized Medical Imaging and Graphics 2007, 31(4-5):198-211.
61. Cheng H.D, Cai X., Chen X., Hu L., Lou X. Computer-aided detection and classification of microcalcifications in mammograms: a survey. Pattern Recognition 2003, 36(12):2967-2991.
62. Halkiotis S., Botsis T., Rangoussi M. Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks. Signal Processing 2007, 87(7):1559-1568.
63. Verma B., McLeod P., Klevansky A. A novel soft cluster neural network for the classification of suspicious areas in digital mammograms. Pattern Recognition 2009, 42(9):1845-1852.
64. Cheng H.D., Shi X.J., Min R., Hu L.M., Cai X.P., Du H.N. Approaches for automated detection and classification of masses in mammograms. Pattern Recognition 2006, 39(4):646-668.
65. Guo D., Qiu T., Bian J., et al. A computer-aided diagnostic system to discriminate SPIO-enhanced magnetic resonance hepatocellular carcinoma by a neural network classifier. Computerized Medical Imaging and Graphics 2009, 33(8):588-592.
66. Yamashita K., Yoshiura T., Arimura H., et al. performance evaluation of radiologists with artificial neural network for differential diagnosis of intra-axial cerebral tumors on MR images. American Journal of Neuroradiology 2008, 29(June-July):1153-1158.
67. Döhler F., Mormann F., Weber B., et al. A cellular neural network based method for classification of magnetic resonance images: towards an automated detection of hippocampal sclerosis. Journal of Neuroscience Methods 2008, 170(2):324-331.
68. Bather T.F., Jensen L.R., Sitter B., et al. MR-determined metabolic phenotype of breast cancer in prediction of lymphatic spread, grade, and hormone status. Breast Cancer Research and Treatment 2007, 104(2):181-189.
69. Guenther F.H, Ghosh S.S., Tourville J.A. Neural modeling and imaging of the cortical interactions underlying syllable production. Brain and Language 2006, 96(3):280-301.
70. Cavanna A.E., Trimble M.R. The precuneus: a review of its functional anatomy and behavioural correlates. Brain 2006, 129(3):564-583.
71. Zhang X., Kanematsu M., Fujita H., et al. Application of an artificial neural network to the computer-aided differentiation of focal liver disease in MR imaging. Radiological Physics and Technology 2009, 2(2):175-182.
72. Tägil K., Marving J., Lomsky M., et al. Use of neural networks to improve quality control of interpretations in myocardial perfusion imaging. International Journal of Cardiovascular Imaging 2008, 24(8):841-848.
73. Pan G., Yan G., Song X., Qiu X. BP neural network classification for bleeding detection in wireless capsule endoscopy. Journal of Medical Engineering and Technology 2009, 33(7):575-581.
74. Chen S.-T., Hsiao Y.-H., Huang Y.-L., et al. Comparative analysis of logistic regression, support vector machine and artificial neural network for the differential diagnosis of benign and malignant solid breast tumors by the use of three-dimensional power Doppler imaging. Korean Journal of Radiology 2009, 10(5):464-471.
75. Dokur Z. A unified framework for image compression and segmentation by using an incremental neural network. Expert Systems and Applications 2008, 34(1):611-619.
76. Cerveri P., Forlani C., Borghese N.A., Ferrigno G. Distortion correction for X-ray image intensifiers: local unwarping polynomials and RBF neural networks. Medical Physics 2002, 29(8):1759-1771.
77. Hsu J.H., Tseng C.S. Application of three-dimensional orthogonal neural network to craniomaxillary reconstruction. Computerized Medical Imaging and Graphics 2001, 25(6):477-482.
78. Kallergi M. Computer-aided diagnosis of mammographic microcalcification clusters. Medical Physics 2004, 31(2):314-326.
79. Glass J.O., Reddick W.E., Goloubeva O., Yo V., Steen R.G. Hybrid artificial neural network segmentation of precise and accurate inversion recovery (PAIR) images from normal human brain. Magnetic Resonance Imaging 2000, 18(10):1245-1253.
80. Reddick W.E., Mulhern R.K., Elkin T.D., Glass J.O., Merchant T.E., Langston J.W. A hybrid neural network analysis of subtle brain volume differences in children surviving brain tumors. Magnetic Resonance Imaging 1998, 16(4):413-421.