Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

Computers in Biology and Medicine

Volumn 60, Issue , 2015, Pages 8-31

  Lemaître, Guillaume ^a,b   Martí, Robert ^b   Freixenet, Jordi ^b   Vilanova, Joan C ^c   Walker, Paul M ^a   Meriaudeau, Fabrice ^a  

^a UNIVERSITÉ DE BOURGOGNE   (France)

^b UNIVERSITY OF GIRONA   (Spain)

^c HOSPITAL JOSEP TRUETA   (Spain)

Author keywords

Computer Aided Detection; Computer Aided Diagnosis; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy Imaging; Prostate cancer

Indexed keywords

DISEASES; MAGNETIC RESONANCE IMAGING; MAGNETIC RESONANCE SPECTROSCOPY; MAGNETISM; RESONANCE; SURVEYS; UROLOGY;

COMPUTER AIDED DETECTION; COMPUTER AIDED DIAGNOSIS SYSTEMS; COMPUTER-AIDED DETECTION AND DIAGNOSIS; COMPUTER-AIDED SYSTEMS; EVALUATION FRAMEWORK; MAGNETIC RESONANCE SPECTROSCOPY IMAGING; PROSTATE CANCERS; RESEARCH COMMUNITIES;

COMPUTER AIDED DIAGNOSIS;

CANCER DIAGNOSIS; CANCER SCREENING; COMPUTER AIDED DESIGN; CONTRAST ENHANCEMENT; HUMAN; IMAGE PROCESSING; MALE; NUCLEAR MAGNETIC RESONANCE IMAGING; PRIORITY JOURNAL; PROSTATE CARCINOMA; REGISTRATION; REVIEW; SIGNAL NOISE RATIO; TRANSRECTAL ULTRASONOGRAPHY; ARTIFICIAL NEURAL NETWORK; CANCER GRADING; CARCINOMA; COMPARATIVE STUDY; COMPUTER ASSISTED DIAGNOSIS; COMPUTER PROGRAM; MASS SCREENING; MEDICAL INFORMATICS; METHODOLOGY; OBSERVER VARIATION; PROCEDURES; PROSTATIC NEOPLASMS; REPRODUCIBILITY; TIME;

CARCINOMA; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; MASS SCREENING; MEDICAL INFORMATICS; NEOPLASM GRADING; NEURAL NETWORKS (COMPUTER); OBSERVER VARIATION; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; RESEARCH DESIGN; SOFTWARE; TIME FACTORS;

EID: 84923878876     PISSN: 00104825     EISSN: 18790534     Source Type: Journal    
DOI: 10.1016/j.compbiomed.2015.02.009     Document Type: Review

References (218)

1. Giger M.L., Chan H.P., Boone J. Anniversary paper. history and status of CAD and quantitative image analysis: the role of Medical Physics and AAPM. Med. Phys. 2008, 35(12):5799-5820.
2. Hambrock T., Vos P.C., Hulsbergen-van de Kaa C.A., Barentsz J.O., Huisman H.J. Prostate cancer. computer-aided diagnosis with multiparametric 3-T MR imaging-effect on observer performance. Radiology 2013, 266(2):521-530.
3. Chan H.P., Sahiner B., Helvie M.A., Petrick N., Roubidoux M.A., Wilson T.E., Adler D.D., Paramagul C., Newman J.S., Sanjay-Gopal S. Improvement of radiologists[U+05F3] characterization of mammographic masses by using computer-aided diagnosis. an ROC study. Radiology 1999, 212(3):817-827.
4. Dean J.C., Ilvento C.C. Improved cancer detection using computer-aided detection with diagnostic and screening mammography. prospective study of 104 cancers. AJR Am. J. Roentgenol. 2006, 187(1):20-28.
5. Li F., Aoyama M., Shiraishi J., Abe H., Li Q., Suzuki K., Engelmann R., Sone S., Macmahon H., Doi K. Radiologists[U+05F3] performance for differentiating benign from malignant lung nodules on high-resolution CT using computer-estimated likelihood of malignancy. AJR Am. J. Roentgenol. 2004, 183(5):1209-1215.
6. Petrick N., Haider M., Summers R.M., Yeshwant S.C., Brown L., Iuliano E.M., Louie A., Choi J.R., Pickhardt P.J. CT colonography with computer-aided detection as a second reader. observer performance study. Radiology 2008, 246(1):148-156.
7. Hegde J.V., Mulkern R.V., Panych L.P., Fennessy F.M., Fedorov A., Maier S.E., Tempany C.M. Multiparametric MRI of prostate cancer. an update on state-of-the-art techniques and their performance in detecting and localizing prostate cancer. J. Magn. Reson. Imaging 2013, 37(5):1035-1054.
8. Hricak H., Williams R.D., Spring D.B., Moon K.L., Hedgcock M.W., Watson R.A., Crooks L.E. Anatomy and pathology of the male pelvis by magnetic resonance imaging. AJR Am. J. Roentgenol. 1983, 141(6):1101-1110.
9. Huch Boni R.A., Boner J.A., Lutolf U.M., Trinkler F., Pestalozzi D.M., Krestin G.P. Contrast-enhanced endorectal coil MRI in local staging of prostate carcinoma. J. Comput. Assist. Tomogr. 1995, 19(2):232-237.
10. 3) spatial resolution. Radiology 1996, 198(3):795-805.
11. J. Scheidler, R. Petsch, U. Muller-Lisse, A. Heuck, M. Reiser, Echo-planar diffusion-weighted MR imaging of the prostate, in: Proceedings of the 7th Annual Meeting of ISMRM Philadelphia, 1999, p. 1103.
12. Swanson M.G., Vigneron D.B., Tran T.K., Sailasuta N., Hurd R.E., Kurhanewicz J. Single-voxel oversampled J-resolved spectroscopy of in vivo human prostate tissue. Magn. Reson. Med. 2001, 45(6):973-980.
13. Chan I., Wells W., Mulkern R.V., Haker S., Zhang J., Zou K.H., Maier S.E., Tempany C.M. Detection of prostate cancer by integration of line-scan diffusion, T2-mapping and T2-weighted magnetic resonance imaging; a multichannel statistical classifier. Med. Phys. 2003, 30(9):2390-2398.
14. Wang S., Burtt K., Turkbey B., Choyke P., Summers R. Computer Aided-Diagnosis of prostate cancer on multiparametric MRI. a technical review of current research. BioMed. Res. Int. 2014.
15. Ferlay J., Shin H.R., Bray F., Forman D., Mathers C., Parkin D.M. Estimates of worldwide burden of cancer in 2008. GLOBOCAN 2008. Int. J. Cancer 2010, 127(12):2893-2917.
16. Siegel R., Naishadham D., Jemal A. Cancer statistics. CA Cancer J. Clin. 2013, 63(1):11-30.
17. A.C. American Cancer Society, Cancer Facts and Figures 2013, 〈〉, 2013 (accessed 01.08.2013). http://www.cancer.org/research/cancerfactsfigures.
18. A.C. American Cancer Society, Cancer Facts and Figures 2010, 〈〉, 2010 (accessed 01.08.2013). http://www.cancer.org/research/cancerfactsfigures.
19. Giovannucci E., Liu Y., Platz E.A., Stampfer M.J., Willett W.C. Risk factors for prostate cancer incidence and progression in the health professionals follow-up study. Int. J. Cancer 2007, 121(7):1571-1578.
20. Steinberg G.D., Carter B.S., Beaty T.H., Childs B., Walsh P.C. Family history and the risk of prostate cancer. Prostate 1990, 17(4):337-347.
21. Freedman M.L., Haiman C.A., Patterson N., McDonald G.J., Tandon A., Waliszewska A., Penney K., Steen R.G., Ardlie K., John E.M., Oakley-Girvan I., Whittemore A.S., Cooney K.A., Ingles S.A., Altshuler D., Henderson B.E., Reich D. Admixture mapping identifies 8q24 as a prostate cancer risk locus in African-American men. Proc. Natl. Acad. Sci. U.S.A. 2006, 103(38):14068-14073.
22. Amundadottir L.T., Sulem P., Gudmundsson J., Helgason A., Baker A., Agnarsson B.A., Sigurdsson A., Benediktsdottir K.R., Cazier J.B., Sainz J., Jakobsdottir M., Kostic J., Magnusdottir D.N., Ghosh S., Agnarsson K., Birgisdottir B., Le Roux L., Olafsdottir A., Blondal T., Andresdottir M., Gretarsdottir O.S., Bergthorsson J.T., Gudbjartsson D., Gylfason A., Thorleifsson G., Manolescu A., Kristjansson K., Geirsson G., Isaksson H., Douglas J., Johansson J.E., Balter K., Wiklund F., Montie J.E., Yu X., Suarez B.K., Ober C., Cooney K.A., Gronberg H., Catalona W.J., Einarsson G.V., Barkardottir R.B., Gulcher J.R., Kong A., Thorsteinsdottir U., Stefansson K. A common variant associated with prostate cancer in European and African populations. Nat. Genet. 2006, 38(6):652-658.
23. Agalliu I., Gern R., Leanza S., Burk R.D. Associations of high-grade prostate cancer with BRCA1 and BRCA2 founder mutations. Clin. Cancer Res. 2009, 15(3):1112-1120.
24. Hoffman R.M., Gilliland F.D., Eley J.W., Harlan L.C., Stephenson R.A., Stanford J.L., Albertson P.C., Hamilton A.S., Hunt W.C., Potosky A.L. Racial and ethnic differences in advanced-stage prostate cancer. the Prostate Cancer Outcomes Study. J. Natl. Cancer Inst. 2001, 93(5):388-395.
25. Ma R.W., Chapman K. A systematic review of the effect of diet in prostate cancer prevention and treatment. J. Hum. Nutr. Diet. 2009, 22(3):187-199.
26. Alexander D.D., Mink P.J., Cushing C.A., Sceurman B. A review and meta-analysis of prospective studies of red and processed meat intake and prostate cancer. Nutr. J. 2010, 9:50.
27. Rodriguez C., Freedland S.J., Deka A., Jacobs E.J., McCullough M.L., Patel A.V., Thun M.J., Calle E.E. Body mass index, weight change, and risk of prostate cancer in the Cancer Prevention Study II Nutrition Cohort. Cancer Epidemiol. Biomark. Prev. 2007, 16(1):63-69.
28. Strum S., Pogliano D. What every doctor who treats male patients should know. PCRI Insights 2005, 8(May (2)).
29. Lu-Yao G.L., Albertsen P.C., Moore D.F., Shih W., Lin Y., DiPaola R.S., Barry M.J., Zietman A., O'Leary M., Walker-Corkery E., Yao S.L. Outcomes of localized prostate cancer following conservative management. J. Am. Med. Assoc. 2009, 302(11):1202-1209.
30. Oster G., Lamerato L., Glass A.G., Richert-Boe K.E., Lopez A., Chung K., Richhariya A., Dodge T., Wolff G.G., Balakumaran A., Edelsberg J. Natural history of skeletal-related events in patients with breast, lung, or prostate cancer and metastases to bone. a 15-year study in two large US health systems. Support Care Cancer 2013, 21(12):3279-3286.
31. Ye L., Kynaston H.G., Jiang W.G. Bone metastasis in prostate cancer: molecular and cellular mechanisms (Review). Int. J. Mol. Med. 2007, 20(1):103-111.
32. Carrol C.L., Sommer F.G., McNeal J.E., Stamey T.A. The abnormal prostate. MR imaging at 1.5T with histopathologic correlation. Radiology 1987, 163(2):521-525.
33. McNeal J.E., Redwine E.A., Freiha F.S., Stamey T.A. Zonal distribution of prostatic adenocarcinoma. Correlation with histologic pattern and direction of spread. Am. J. Surg. Pathol. 1988, 12(12):897-906.
34. Stamey T.A., Donaldson A.N., Yemoto C.E., McNeal J.E., Sozen S., Gill H. Histological and clinical findings in 896 consecutive prostates treated only with radical retropubic prostatectomy. epidemiologic significance of annual changes. J. Urol. 1998, 160(6 Pt 2):2412-2417.
35. Cohen R.J., Shannon B.A., Phillips M., Moorin R.E., Wheeler T.M., Garrett K.L. Central zone carcinoma of the prostate gland. a distinct tumor type with poor prognostic features. J. Urol. 2008, 179(5):1762-1767.
36. Chou R., Croswell J.M., Dana T., Bougatsos C., Blazina I., Fu R., Gleitsmann K., Koenig H.C., Lam C., Maltz A., Rugge J.B., Lin K. Screening for prostate cancer. a review of the evidence for the U.S. Preventive Services Task Force. Ann. Intern. Med. 2011, 155(11):762-771.
37. Andriole G.L., Crawford E.D., Grubb R.L., Buys S.S., Chia D., Church T.R., Fouad M.N., Gelmann E.P., Kvale P.A., Reding D.J., Weissfeld J.L., Yokochi L.A., O'Brien B., Clapp J.D., Rathmell J.M., Riley T.L., Hayes R.B., Kramer B.S., Izmirlian G., Miller A.B., Pinsky P.F., Prorok P.C., Gohagan J.K., Berg C.D. Mortality results from a randomized Prostate-cancer screening trial. New Engl. J. Med. 2009, 360(13):1310-1319.
38. Schröder F.H., Hugosson J., Roobol M.J., Tammela T.L., Ciatto S., Nelen V., Kwiatkowski M., Lujan M., Lilja H., Zappa M., Denis L.J., Recker F., Páez A., Määttänen L., Bangma C.H., Aus G., Carlsson S., Villers A., Rebillard X., van der Kwast T., Kujala P.M., Blijenberg B.G., Stenman U.-H., Huber A., Taari K., Hakama M., Moss S.M., de Koning H.J., Auvinen A. Prostate-cancer mortality at 11 years of follow-up. New Engl. J. Med. 2012, 366(11):981-990.
39. Hugosson J., Carlsson S., Aus G., Bergdahl S., Khatami A., Lodding P., Pihl C.G., Stranne J., Holmberg E., Lilja H. Mortality results from the Göteborg randomised population-based prostate-cancer screening trial. Lancet Oncol. 2010, 11(8):725-732.
40. Bourdoumis A., Papatsoris A.G., Chrisofos M., Efstathiou E., Skolarikos A., Deliveliotis C. The novel prostate cancer antigen 3 (PCA3) biomarker. Int. Braz. J. Urol. 2010, 36(6):665-668.
41. Morgan R., Boxall A., Bhatt A., Bailey M., Hindley R., Langley S., Whitaker H.C., Neal D.E., Ismail M., Whitaker H., Annels N., Michael A., Pandha H. Engrailed-2 (EN2). a tumor specific urinary biomarker for the early diagnosis of prostate cancer. Clin. Cancer Res. 2011, 17(5):1090-1098.
42. Brenner J., Chinnaiyan A., Tomlins S. ETS fusion genes in prostate cancer. Prostate Cancer, Protein Reviews 2013, vol. 16:139-183. Springer, New York.
43. Hoeks C.M., Barentsz J.O., Hambrock T., Yakar D., Somford D.M., Heijmink S.W., Scheenen T.W., Vos P.C., Huisman H., van Oort I.M., Witjes J.A., Heerschap A., Futterer J.J. Prostate cancer. multiparametric MR imaging for detection, localization, and staging. Radiology 2011, 261(1):46-66.
44. Moore C.M., Ridout A., Emberton M. The role of MRI in active surveillance of prostate cancer. Curr. Opin. Urol. 2013, 23(3):261-267.
45. Noguchi M., Stamey T.A., McNeal J.E., Yemoto C.M. Relationship between systematic biopsies and histological features of 222 radical prostatectomy specimens. lack of prediction of tumor significance for men with nonpalpable prostate cancer. J. Urol. 2001, 166(1):104-109.
46. Haas G.P., Delongchamps N.B., Jones R.F., Chandan V., Serio A.M., Vickers A.J., Jumbelic M., Threatte G., Korets R., Lilja H., de la Roza G. Needle biopsies on autopsy prostates. sensitivity of cancer detection based on true prevalence. J. Natl. Cancer Inst. 2007, 99(19):1484-1489.
47. Taira A.V., Merrick G.S., Galbreath R.W., Andreini H., Taubenslag W., Curtis R., Butler W.M., Adamovich E., Wallner K.E. Performance of transperineal template-guided mapping biopsy in detecting prostate cancer in the initial and repeat biopsy setting. Prostate Cancer Prostatic Dis. 2010, 13(1):71-77.
48. Delongchamps N.B., Peyromaure M., Schull A., Beuvon F., Bouazza N., Flam T., Zerbib M., Muradyan N., Legman P., Cornud F. Prebiopsy magnetic resonance imaging and prostate cancer detection. comparison of random and targeted biopsies. J. Urol. 2013, 189(2):493-499.
49. Turkbey B., Choyke P.L. Multiparametric MRI and prostate cancer diagnosis and risk stratification. Curr. Opin. Urol. 2012, 22(4):310-315.
50. Barentsz J.O., Richenberg J., Clements R., Choyke P., Verma S., Villeirs G., Rouviere O., Logager V., Futterer J.J. ESUR prostate MR guidelines 2012. Eur. Radiol. 2012, 22(4):746-757.
51. Hricak H., Dooms G.C., McNeal J.E., Mark A.S., Marotti M., Avallone A., Pelzer M., Proctor E.C., Tanagho E.A. MR imaging of the prostate gland. normal anatomy. AJR Am. J. Roentgenol. 1987, 148:51-58.
52. Akin O., Sala E., Moskowitz C.S., Kuroiwa K., Ishill N.M., Pucar D., Scardino P.T., Hricak H. Transition zone prostate cancers. features, detection, localization, and staging at endorectal MR imaging. Radiology 2006, 239(3):784-792.
53. Wang L., Mazaheri Y., Zhang J., Ishill N.M., Kuroiwa K., Hricak H. Assessment of biologic aggressiveness of prostate cancer. correlation of MR signal intensity with Gleason grade after radical prostatectomy. Radiology 2008, 246(1):168-176.
54. Kirkham A.P., Emberton M., Allen C. How good is MRI at detecting and characterising cancer within the prostate?. Eur. Urol. 2006, 50(6):1163-1174.
55. Quint L.E., Van Erp J.S., Bland P.H., Mandell S.H., Del Buono E.A., Grossman H.B., Glazer G.M., Gikas P.W. Carcinoma of the prostate. MR images obtained with body coils do not accurately reflect tumor volume. AJR Am. J. Roentgenol. 1991, 156(3):511-516.
56. Cruz M., Tsuda K., Narumi Y., Kuroiwa Y., Nose T., Kojima Y., Okuyama A., Takahashi S., Aozasa K., Barentsz J.O., Nakamura H. Characterization of low-intensity lesions in the peripheral zone of prostate on pre-biopsy endorectal coil MR imaging. Eur. Radiol. 2002, 12(2):357-365.
57. Liu W., Turkbey B., Senegas J., Remmele S., Xu S., Kruecker J., Bernardo M., Wood B.J., Pinto P.A., Choyke P.L. Accelerated T2 mapping for characterization of prostate cancer. Magn. Reson. Med. 2011, 65(5):1400-1406.
58. Liney G.P., Lowry M., Turnbull L.W., Manton D.J., Knowles A.J., Blackband S.J., Horsman A. Proton MR T2 maps correlate with the citrate concentration in the prostate. NMR Biomed. 1996, 9(2):59-64.
59. Liney G.P., Turnbull L.W., Lowry M., Turnbull L.S., Knowles A.J., Horsman A. In vivo quantification of citrate concentration and water T2 relaxation time of the pathologic prostate gland using 1H MRS and MRI. Magn. Reson. Imaging 1997, 15(10):1177-1186.
60. Liney G.P., Knowles A.J., Manton D.J., Turnbull L.W., Blackband S.J., Horsman A. Comparison of conventional single echo and multi-echo sequences with a fast spin-echo sequence for quantitative T2 mapping. application to the prostate. J. Magn. Reson. Imaging 1996, 6(4):603-607.
61. Gibbs P., Tozer D.J., Liney G.P., Turnbull L.W. Comparison of quantitative T2 mapping and diffusion-weighted imaging in the normal and pathologic prostate. Magn. Reson. Med. 2001, 46(6):1054-1058.
62. Verma S., Turkbey B., Muradyan N., Rajesh A., Cornud F., Haider M.A., Choyke P.L., Harisinghani M. Overview of dynamic contrast-enhanced MRI in prostate cancer diagnosis and management. AJR Am. J. Roentgenol. 2012, 198(6):1277-1288.
63. Gribbestad I., Gjesdal K., Nilsen G., Lundgren S., Hjelstuen M., Jackson A. An introduction to dynamic contrast-enhanced MRI in oncology. Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Oncology, Medical Radiology 2005, 1-22. Springer, Berlin, Heidelberg. A. Jackson, D. Buckley, G. Parker (Eds.).
64. Padhani A.R. Dynamic contrast-enhanced MRI in clinical oncology. current status and future directions. J. Magn. Reson. Imaging 2002, 16(4):407-422.
65. P. Tofts, T1-weighted DCE imaging concepts: modelling, acquisition and analysis, in: Magneton Flash, Siemens, 2010.
66. Rosenkrantz A.B., Sabach A., Babb J.S., Matza B.W., Taneja S.S., Deng F.M. Prostate cancer. comparison of dynamic contrast-enhanced MRI techniques for localization of peripheral zone tumor. AJR Am. J. Roentgenol. 2013, 201(3):W471-W478.
67. Jager G.J., Ruijter E.T., van de Kaa C.A., de la Rosette J.J., Oosterhof G.O., Thornbury J.R., Ruijs S.H., Barentsz J.O. Dynamic TurboFLASH subtraction technique for contrast-enhanced MR imaging of the prostate. correlation with histopathologic results. Radiology 1997, 203(3):645-652.
68. Kim J.K., Hong S.S., Choi Y.J., Park S.H., Ahn H., Kim C.S., Cho K.S. Wash-in rate on the basis of dynamic contrast-enhanced MRI. usefulness for prostate cancer detection and localization. J. Magn. Reson. Imaging 2005, 22(5):639-646.
69. Schlemmer H.P., Merkle J., Grobholz R., Jaeger T., Michel M.S., Werner A., Rabe J., van Kaick G. Can pre-operative contrast-enhanced dynamic MR imaging for prostate cancer predict microvessel density in prostatectomy specimens?. Eur. Radiol. 2004, 14(2):309-317.
70. Zelhof B., Lowry M., Rodrigues G., Kraus S., Turnbull L. Description of magnetic resonance imaging-derived enhancement variables in pathologically confirmed prostate cancer and normal peripheral zone regions. BJU Int. 2009, 104(5):621-627.
71. van Niekerk C.G., Witjes J.A., Barentsz J.O., van der Laak J.A., Hulsbergen-van de Kaa C.A. Microvascularity in transition zone prostate tumors resembles normal prostatic tissue. Prostate 2013, 73(5):467-475.
72. Le Bihan D., Breton E., Lallemand D., Aubin M.L., Vignaud J., Laval-Jeantet M. Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 1988, 168(2):497-505.
73. Koh D.M., Collins D.J. Diffusion-weighted MRI in the body. applications and challenges in oncology. AJR Am. J. Roentgenol. 2007, 188(6):1622-1635.
74. Huisman T.A. Diffusion-weighted imaging. basic concepts and application in cerebral stroke and head trauma. Eur. Radiol. 2003, 13(10):2283-2297.
75. Le Bihan D., Breton E., Lallemand D., Grenier P., Cabanis E., Laval-Jeantet M. MR imaging of intravoxel incoherent motions. application to diffusion and perfusion in neurologic disorders. Radiology 1986, 161(2):401-407.
76. Shimofusa R., Fujimoto H., Akamata H., Motoori K., Yamamoto S., Ueda T., Ito H. Diffusion-weighted imaging of prostate cancer. J. Comput. Assist. Tomogr. 2005, 29(2):149-153.
77. A.R. Padhani, Integrating multiparametric prostate MRI into clinical practice, Cancer Imaging 11 Spec No A (2011) 27-37.
78. Choi Y.J., Kim J.K., Kim N., Kim K.W., Choi E.K., Cho K.S. Functional MR imaging of prostate cancer. Radiographics 2007, 27:63-75.
79. Doo K.W., Sung D.J., Park B.J., Kim M.J., Cho S.B., Oh Y.W., Ko Y.H., Yang K.S. Detectability of low and intermediate or high risk prostate cancer with combined T2-weighted and diffusion-weighted MRI. Eur. Radiol. 2012, 22(8):1812-1819.
80. Hambrock T., Somford D.M., Huisman H.J., van Oort I.M., Witjes J.A., Hulsbergen-van de Kaa C.A., Scheenen T., Barentsz J.O. Relationship between apparent diffusion coefficients at 3.0-T MR imaging and Gleason grade in peripheral zone prostate cancer. Radiology 2011, 259(2):453-461.
81. Itou Y., Nakanishi K., Narumi Y., Nishizawa Y., Tsukuma H. Clinical utility of apparent diffusion coefficient (ADC) values in patients with prostate cancer. can ADC values contribute to assess the aggressiveness of prostate cancer?. J. Magn. Reson. Imaging 2011, 33(1):167-172.
82. Peng Y., Jiang Y., Yang C., Brown J., Antic T., Sethi I., Schmid-Tannwald C., Giger M., Eggener S., Oto A. Quantitative analysis of multiparametric prostate MR images. differentiation between prostate cancer and normal tissue and correlation with Gleason score-a computer-aided diagnosis development study. Radiology 2013, 267(1):787-796.
83. Awwad H.M., Geisel J., Obeid R. The role of choline in prostate cancer. Clin. Biochem. 2012, 45(18):1548-1553.
84. Costello L.C., Franklin R.B. The clinical relevance of the metabolism of prostate cancer; zinc and tumor suppression: connecting the dots. Mol. Cancer 2006, 5:17.
85. Giskeodegard G.F., Bertilsson H., Selnaes K.M., Wright A.J., Bathen T.F., Viset T., Halgunset J., Angelsen A., Gribbestad I.S., Tessem M.B. Spermine and citrate as metabolic biomarkers for assessing prostate cancer aggressiveness. PLoS ONE 2013, 8(4):e62375.
86. van der Graaf M., Schipper R.G., Oosterhof G.O., Schalken J.A., Verhofstad A.A., Heerschap A. Proton MR spectroscopy of prostatic tissue focused on the detection of spermine, a possible biomarker of malignant behavior in prostate cancer. MAGMA 2000, 10(3):153-159.
87. S. Parfait, Classification de spectres et recherche de biomarqueurs en spectroscopie par résonqnce magnétique nulcléaire du proton dans les tumeurs prostatiques (Ph.D. thesis), Université de Bourgogne, 2010.
88. Verma S., Rajesh A., Futterer J.J., Turkbey B., Scheenen T.W., Pang Y., Choyke P.L., Kurhanewicz J. Prostate MRI and 3D MR spectroscopy. how we do it. AJR Am. J. Roentgenol. 2010, 194(6):1414-1426.
89. Scheidler J., Hricak H., Vigneron D.B., Yu K.K., Sokolov D.L., Huang L.R., Zaloudek C.J., Nelson S.J., Carroll P.R., Kurhanewicz J. Prostate cancer. localization with three-dimensional proton MR spectroscopic imaging-clinicopathologic study. Radiology 1999, 213(2):473-480.
90. Kaji Y., Kurhanewicz J., Hricak H., Sokolov D.L., Huang L.R., Nelson S.J., Vigneron D.B. Localizing prostate cancer in the presence of postbiopsy changes on MR images. role of proton MR spectroscopic imaging. Radiology 1998, 206(3):785-790.
91. Vilanova J.C., Comet J., Barceló-Vidal C., Barceló J., López-Bonet E., Maroto A., Arzoz M., Moreno À., Areal J. Peripheral zone prostate cancer in patients with elevated PSA levels and low free-to-total PSA ratio. detection with MR imaging and MR spectroscopy. Radiology 2009, 253(1):135-143.
92. P. Walker, G. Crehange, S. Parfait, A. Cochet, P. Maignon, L. Cormier, F. Brunotte, Absolute quantification in 1H MRSI of the prostate at 3T, in: ISMRM Annual Meeting 2010, 2010.
93. G. Lemaître, Absolute quantification at 3T (Master's thesis), Université de Bourgogne, Heriot-Watt University, Universitat de Girona, 2011.
94. Nowak R. Wavelet-based Rician noise removal for magnetic resonance imaging. IEEE Trans. Image Process. 1999, 8(10):1408-1419.
95. Manjon J.V., Carbonell-Caballero J., Lull J.J., Garcia-Marti G., Marti-Bonmati L., Robles M. MRI denoising using non-local means. Med. Image Anal. 2008, 12(4):514-523.
96. Buades A., Coll B., Morel J. A review of image denoising algorithms, with a new one. Simul 2005, 4:490-530.
97. Mohan J., Krishnaveni V., Guo Y. A survey on the magnetic resonance image denoising methods. Biomed. Signal Process. Control 2014, 9(0):56-69.
98. S. Ozer, M. Haider, D.L. Langer, T.H. Van der Kwast, A. Evans, M. Wernick, J. Trachtenberg, I. Yetik, Prostate cancer localization with multispectral MRI based on Relevance Vector Machines, in: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009, ISBI '09, 2009, pp. 73-76.
99. Ozer S., Langer D.L., Liu X., Haider M.A., van der Kwast T.H., Evans A.J., Yang Y., Wernick M.N., Yetik I.S. Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI. Med. Phys. 2010, 37(4):1873-1883.
100. A. Pizurica, Image denoising using wavelets and spatial context modeling (Ph.D. thesis), Universiteit Gent, 2002.
101. D. Ampeliotis, A. Anonakoudi, K. Berberidis, E.Z. Psarakis, Computer aided detection of prostate cancer using fused information from dynamic contrast enhanced and morphological magnetic resonance images, in: IEEE International Conference on Signal Processing and Communications, 2007, pp. 888-891.
102. D. Ampeliotis, A. Anonakoudi, K. Berberidis, E.Z. Psarakis, A. Kounoudes, A computer-aided system for the detection of prostate cancer based on magnetic resonance image analysis, in: International Symposium on Communications, Control and Signal Processing, 2008.
103. S. Mallat, A wavelet tour of signal processing, in: The Sparse Way, 3rd ed., Academic Press, Burlington, 2008.
104. Lopes R., Ayache A., Makni N., Puech P., Villers A., Mordon S., Betrouni N. Prostate cancer characterization on MR images using fractal features. Med. Phys. 2011, 38(1):83-95.
105. Pizurica A., Philips W., Lemahieu I., Acheroy M. A versatile wavelet domain noise filtration technique for medical imaging. IEEE Trans. Med. Imaging 2003, 22(3):323-331.
106. Middleton D., Esposito R. Simultaneous optimum detection and estimation of signals in noise. IEEE Trans. Inf. Theory 1968, 14(3):434-444.
107. Styner M., Brechbuhler C., Szckely G., Gerig G. Parametric estimate of intensity inhomogeneities applied to MRI. IEEE Trans. Med. Imaging 2000, 19(3):153-165.
108. M. Jungke, W. Von Seelen, G. Bielke, S. Meindl, M. Grigat, P. Pfannenstiel, A system for the diagnostic use of tissue characterizing parameters in NMR-tomography, in: Proceedings of Information Processing in Medical Imaging, vol. 87, 1987, pp. 471-481.
109. Vovk U., Pernus F., Likar B. A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans. Med. Imaging 2007, 26(3):405-421.
110. S. Viswanath, B.N. Bloch, M. Rosen, J. Chappelow, R. Toth, N. Rofsky, R. Lenkinski, E. Genega, A. Kalyanpur, A. Madabhushi, Integrating structural and functional imaging for computer assisted detection of prostate cancer on multi-protocol in vivo 3Tesla MRI, in: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 7260 of Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 2009.
111. Lv D., Guo X., Wang X., Zhang J., Fang J. Computerized characterization of prostate cancer by fractal analysis in MR images. J. Magn. Reson. Imaging 2009, 30(1):161-168.
112. Madabhushi A., Udupa J., Souza A. Generalized scale. theory, algorithms, and application to image inhomogeneity correction. Comput. Vis. Image Underst. 2006, 101(2):100-121.
113. Nyul L.G., Udupa J.K. On standardizing the MR image intensity scale. Magn. Reson. Med. 1999, 42(6):1072-1081.
114. Y. Artan, D. Langer, M. Haider, T.H. Van der Kwast, A. Evans, M. Wernick, I. Yetik, Prostate cancer segmentation with multispectral MRI using cost-sensitive Conditional Random Fields, in: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009, ISBI '09, 2009, pp. 278-281.
115. Artan Y., Haider M.A., Langer D.L., van der Kwast T.H., Evans A.J., Yang Y., Wernick M.N., Trachtenberg J., Yetik I.S. Prostate cancer localization with multispectral MRI using cost-sensitive support vector machines and conditional random fields. IEEE Trans. Image Process. 2010, 19(9):2444-2455.
116. P. Liu, S. Wang, B. Turkbey, P.C.P. Grant, K. Pinto, B.J. Wood, R.M. Summers, A prostate cancer computer-aided diagnosis system using multimodal magnetic resonance imaging and targeted biopsy labels, in: Proceedings of SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 2013, pp. 86701G-86701G-6.
117. Nyul L.G., Udupa J.K., Zhang X. New variants of a method of MRI scale standardization. IEEE Trans. Med. Imaging 2000, 19(2):143-150.
118. S. Viswanath, B.N. Bloch, J. Chappelow, P. Patel, N. Rofsky, R. Lenkinski, E. Genega, A. Madabhushi, Enhanced multi-protocol analysis via intelligent supervised embedding (EMPrAvISE): detecting prostate cancer on multi-parametric MRI, in: Proceedings of SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 2011.
119. Viswanath S.E., Bloch N.B., Chappelow J.C., Toth R., Rofsky N.M., Genega E.M., Lenkinski R.E., Madabhushi A. Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3Tesla endorectal, in vivo T2-weighted MR imagery. J. Magn. Reson. Imaging 2012, 36(1):213-224.
120. Madabhushi A., Udupa J.K. New methods of MR image intensity standardization via generalized scale. Med. Phys. 2006, 33(9):3426-3434.
121. E. Niaf, O. Rouviere, C. Lartizien, Computer-aided diagnosis for prostate cancer detection in the peripheral zone via multisequence MRI, in: Proceedings of SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 2011.
122. Niaf E., Rouviere O., Mege-Lechevallier F., Bratan F., Lartizien C. Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRI. Phys. Med. Biol. 2012, 57(12):3833-3851.
123. Wiart M., Curiel L., Gelet A., Lyonnet D., Chapelon J.Y., Rouviere O. Influence of perfusion on high-intensity focused ultrasound prostate ablation. a first-pass MRI study. Magn. Reson. Med. 2007, 58(1):119-127.
124. Chen L., Weng Z., Goh L., Garland M. An efficient algorithm for automatic phase correction of {NMR} spectra based on entropy minimization. J. Magn. Reson. 2002, 158(1-2):164-168.
125. M. Osorio-Garcia, A. Croitor Sava, D.M. Sima, F. Nielsen, U. Himmelreich, S. Van Huffel, Magnetic resonance spectroscopy, InTech, 2012, pp. 1-27 (Chapter: Quantification improvements of 1H MRS Signals).
126. Parfait S., Walker P., Créhange G., Tizon X., Mitéran J. Classification of prostate magnetic resonance spectra using Support Vector Machine. Biomed. Signal Process. Control 2012, 7(5):499-508.
127. Zhu H., Ouwerkerk R., Barker P.B. Dual-band water and lipid suppression for MR spectroscopic imaging at 3Tesla. Magn. Reson. Med. 2010, 63(6):1486-1492.
128. Kelm B.M., Menze B.H., Zechmann C.M., Baudendistel K.T., Hamprecht F.A. Automated estimation of tumor probability in prostate magnetic resonance spectroscopic imaging: pattern recognition vs quantification. Magn. Reson. Med. 2007, 57(1):150-159.
129. W. Pijnappel, A. van den Boogaart, R. de Beer, D. van Ormondt, SVD-based quantification of magnetic resonance signals, J. Magn. Reson. (1969) 97 (1) (1992) 122-134.
130. Lieber C.A., Mahadevan-Jansen A. Automated method for subtraction of fluorescence from biological Raman spectra. Appl. Spectrosc. 2003, 57(11):1363-1367.
131. Devos A., Lukas L., Suykens J.A., Vanhamme L., Tate A.R., Howe F.A., Majos C., Moreno-Torres A., van der Graaf M., Arus C., Van Huffel S. Classification of brain tumours using short echo time 1H MR spectra. J. Magn. Reson. 2004, 170(1):164-175.
132. Tiwari P., Viswanath S., Kurhanewicz J., Sridhar A., Madabhushi A. Multimodal wavelet embedding representation for data combination (MaWERiC). integrating magnetic resonance imaging and spectroscopy for prostate cancer detection. NMR Biomed. 2012, 25(4):607-619.
133. Ghose S., Oliver A., Marti R., Llado X., Vilanova J.C., Freixenet J., Mitra J., Sidibe D., Meriaudeau F. A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images. Comput. Methods Programs Biomed. 2012, 108(1):262-287.
134. Chilali O., Ouzzane A., Diaf M., Betrouni N. A survey of prostate modeling for image analysis. Comput. Biol. Med. 2014, 53(0):190-202.
135. Matulewicz L., Jansen J.F., Bokacheva L., Vargas H.A., Akin O., Fine S.W., Shukla-Dave A., Eastham J.A., Hricak H., Koutcher J.A., Zakian K.L. Anatomic segmentation improves prostate cancer detection with artificial neural networks analysis of 1H magnetic resonance spectroscopic imaging. J. Magn. Reson. Imaging 2013, n/a-n/a.
136. Puech P., Betrouni N., Makni N., Dewalle A.S., Villers A., Lemaitre L. Computer-assisted diagnosis of prostate cancer using DCE-MRI data. design, implementation and preliminary results. Int. J. Comput. Assist. Radiol. Surg. 2009, 4(1):1-10.
137. P.C. Vos, T. Hambrock, J.O. Barenstz, H.J. Huisman, Combining T2-weighted with dynamic MR images for computerized classification of prostate lesions, in: Medical Imaging 2008: Computer-Aided Diagnosis, vol. 6915, SPIE, 2008.
138. Vos P.C., Hambrock T., Hulsbergen-van de Kaa C.A., Futterer J.J., Barentsz J.O., Huisman H.J. Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI. Med. Phys. 2008, 35(3):888-899.
139. G.J.S. Litjens, J.O. Barentsz, N. Karssemeijer, H.J. Huisman, Automated computer-aided detection of prostate cancer in MR images: from a whole-organ to a zone-based approach, in: Proceedings of SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 2012, pp. 83150G-83150G-6.
140. Klein S., van der Heide U.A., Lips I.M., van Vulpen M., Staring M., Pluim J.P. Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Med. Phys. 2008, 35(4):1407-1417.
141. Litjens G., Debats O., van de Ven W., Karssemeijer N., Huisman H. A pattern recognition approach to zonal segmentation of the prostate on MRI. Med. Image Comput. Comput. Assist. Interv. 2012, 15(Pt 2):413-420.
142. Litjens G., Debats O., Barentsz J., Karssemeijer N., Huisman H. Computer-iided detection of prostate cancer in MRI. IEEE Trans. Med. Imaging 2014, 33(5):1083-1092.
143. Litjens G., Toth R., van de Ven W., Hoeks C., Kerkstra S., van Ginneken B., Vincent G., Guillard G., Birbeck N., Zhang J., Strand R., Malmberg F., Ou Y., Davatzikos C., Kirschner M., Jung F., Yuan J., Qiu W., Gao Q., Edwards P.E., Maan B., van der Heijden F., Ghose S., Mitra J., Dowling J., Barratt D., Huisman H., Madabhushi A. Evaluation of prostate segmentation algorithms for MRI. the PROMISE12 challenge. Med. Image Anal. 2014, 18(2):359-373.
144. Langerak T.R., van der Heide U.A., Kotte A.N., Viergever M.A., van Vulpen M., Pluim J.P. Label fusion in atlas-based segmentation using a selective and iterative method for performance level estimation (SIMPLE). IEEE Trans. Med. Imaging 2010, 29(12):2000-2008.
145. Viswanath S., Bloch B.N., Genega E., Rofsky N., Lenkinski R., Chappelow J., Toth R., Madabhushi A. A comprehensive segmentation, registration, and cancer detection scheme on 3Tesla in vivo prostate DCE-MRI. Med. Image Comput. Comput. Assist. Interv. 2008, 11(Pt 1):662-669.
146. Toth R., Chappelow J., Rosen M., Pungavkar S., Kalyanpur A., Madabhushi A. Multi-attribute non-initializing texture reconstruction based active shape model (MANTRA). Med. Image Comput. Comput. Assist. Interv. 2008, 11(Pt 1):653-661.
147. Cootes T.F., Taylor C.J., Cooper D.H., Graham J. Active shape models-their training and application. Comput. Vis. Image Underst. 1995, 61(1):38-59.
148. G.J.S. Litjens, P.C. Vos, J.O. Barentsz, N. Karssemeijer, H.J. Huisman, Automatic computer aided detection of abnormalities in multi-parametric prostate MRI, in: Proceedings of SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 2011, pp. 79630T-79630T-7.
149. Vos P.C., Barentsz J.O., Karssemeijer N., Huisman H.J. Automatic computer-aided detection of prostate cancer based on multiparametric magnetic resonance image analysis. Phys. Med. Biol. 2012, 57(6):1527-1542.
150. H. Huisman, P. Vos, G. Litjens, T. Hambrock, J. Barentsz, Computer aided detection of prostate cancer using T2, DWI and DCE MRI: methods and clinical applications, in: Proceedings of the 2010 international conference on Prostate cancer imaging: computer-aided diagnosis, prognosis, and intervention, MICCAI'10, Springer-Verlag, Berlin, Heidelberg, 2010, pp. 4-14.
151. Tiwari P., Rosen M., Madabhushi A. A hierarchical spectral clustering and nonlinear dimensionality reduction scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS). Med. Phys. 2009, 36(9):3927-3939.
152. Shi J., Malik J. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 2000, 22(8):888-905.
153. Maintz J.B., Viergever M.A. A survey of medical image registration. Med. Image Anal. 1998, 2(1):1-36.
154. Zitová B., Flusser J. Image registration methods. a survey. Image Vis. Comput. 2003, 21(11):977-1000.
155. J. Mitra, R. Marti, A. Oliver, X. Llado, J.C. Vilanova, F. Meriaudeau, A comparison of thin-plate splines with automatic correspondences and B-splines with uniform grids for multimodal prostate registration, in: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, vol. 7964 of Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 2011.
156. Mitra J., Kato Z., Marti R., Oliver A., Llado X., Sidibe D., Ghose S., Vilanova J.C., Comet J., Meriaudeau F. A spline-based non-linear diffeomorphism for multimodal prostate registration. Med. Image Anal. 2012, 16(6):1259-1279.
157. R. Toth, S. Doyle, S. Pungavkar, A. Kalyanpur, A. Madabhushi, A boosted ensemble scheme for accurate landmark detection for active shape models, in: SPIE Medical Imaging, vol. 7260, Orlando, FL, 2009.
158. Pluim J., Maintz J., Viergever M. Mutual-information-based registration of medical images. a survey. IEEE Trans. Med. Imaging 2003, 22(8):986-1004.
159. Chappelow J., Bloch B.N., Rofsky N., Genega E., Lenkinski R., DeWolf W., Madabhushi A. Elastic registration of multimodal prostate MRI and histology via multiattribute combined mutual information. Med. Phys. 2011, 38(4):2005-2018.
160. Byrd R.H., Lu P., Nocedal J., Zhu C. A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput. 1995, 16(5):1190-1208.
161. Viola P., Wells W.M. Alignment by maximization of mutual information. Int. J. Comput. Vis. 1997, 24(2):137-154.
162. J. Mitra, Multimodal image registration applied to magnetic resonance and ultrasound prostatic images (Ph.D. thesis), Universitat de Girona and Université de Bourgogne, 2012.
163. V. Giannini, A. Vignati, S. Mazzetti, M. De Luca, C. Bracco, M. Stasi, F. Russo, E. Armando, D. Regge, A prostate CAD system based on multiparametric analysis of DCE T1-w, and DW automatically registered images, in: Proceedings of SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 2013, pp. 86703E-86703E-6.
164. Vos P.C., Hambrock T., Barenstz J.O., Huisman H.J. Computer-assisted analysis of peripheral zone prostate lesions using T2-weighted and dynamic contrast enhanced T1-weighted MRI. Phys. Med. Biol. 2010, 55(6):1719-1734.
165. Rueckert D., Sonoda L.I., Hayes C., Hill D.L., Leach M.O., Hawkes D.J. Nonrigid registration using free-form deformations. application to breast MR images. IEEE Trans. Med. Imaging 1999, 18(8):712-721.
166. Liu X., Langer D.L., Haider M.A., Yang Y., Wernick M.N., Yetik I.S. Prostate cancer segmentation with simultaneous estimation of Markov random field parameters and class. IEEE Trans. Med. Imaging 2009, 28(6):906-915.
167. S. Mazzetti, M. De Luca, C. Bracco, A. Vignati, V. Giannini, M. Stasi, F. Russo, E. Armando, S. Agliozzo, D. Regge, A CAD system based on multi-parametric analysis for cancer prostate detection on DCE-MRI, in: Proceedings of SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 2011, pp. 79633Q-79633Q-7.
168. Sung Y.S., Kwon H.J., Park B.W., Cho G., Lee C.K., Cho K.S., Kim J.K. Prostate cancer detection on dynamic contrast-enhanced MRI. computer-aided diagnosis versus single perfusion parameter maps. AJR Am. J. Roentgenol. 2011, 197(5):1122-1129.
169. Tiwari P., Madabhushi A., Rosen M. A hierarchical unsupervised spectral clustering scheme for detection of prostate cancer from magnetic resonance spectroscopy (MRS). Med. Image Comput. Comput. Assist. Interv. 2007, 10(Pt 2):278-286.
170. Tiwari P., Rosen M., Madabhushi A. Consensus-locally linear embedding (C-LLE). application to prostate cancer detection on magnetic resonance spectroscopy. Med. Image Comput. Comput. Assist. Interv. 2008, 11(Pt 2):330-338.
171. Tiwari P., Rosen M., Reed G., Kurhanewicz J., Madabhushi A. Spectral embedding based probabilistic boosting tree (ScEPTre). classifying high dimensional heterogeneous biomedical data. Med. Image Comput. Comput. Assist. Interv. 2009, 12(Pt 2):844-851.
172. Tiwari P., Kurhanewicz J., Rosen M., Madabhushi A. Semi supervised multi kernel (SeSMiK) graph embedding. identifying aggressive prostate cancer via magnetic resonance imaging and spectroscopy. Med. Image Comput. Comput. Assist. Interv. 2010, 13(Pt 3):666-673.
173. Tiwari P., Kurhanewicz J., Madabhushi A. Multi-kernel graph embedding for detection, Gleason grading of prostate cancer via MRI/MRS. Med. Image Anal. 2013, 17(2):219-235.
174. S. Viswanath, P. Tiwari, M. Rosen, A. Madabhushi, A meta-classifier for detecting prostate cancer by quantitative integration of In Vivo magnetic resonance spectroscopy and magnetic resonance imaging, in: Medical Imaging 2008: Computer-Aided Diagnosis, vol. 6915, SPIE, 2008.
175. Langer D.L., van der Kwast T.H., Evans A.J., Trachtenberg J., Wilson B.C., Haider M.A. Prostate cancer detection with multi-parametric MRI. logistic regression analysis of quantitative T2, diffusion-weighted imaging, and dynamic contrast-enhanced MRI. J. Magn. Reson. Imaging 2009, 30(2):327-334.
176. Prewitt J. Picture Processing and Psychohistories 1970, Academic Press, New York, Chapter: Object enhancement and extraction.
177. I. Sobel, Camera models and machine perception, Technical Report, DTIC Document, 1970.
178. Kirsch R. Computer determination of the constituent structure of biological images. Comput. Biomed. Res. 1971, 4(3):315-328.
179. Gabor D. Theory of communication. Part 1. the analysis of information. J. Inst. Electr. Eng.-Part III: Radio Commun. Eng. 1946, 93(26):429-441.
180. Daugman J.G. Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. A 1985, 2(7):1160-1169.
181. Haralick R., Shanmugam K., Dinstein I. Textural features for image classification. IEEE Trans. Syst. Man Cybern. 1973, SMC-3(6):610-621.
182. T. Antic, Y. Peng, Y. Jiang, M.L. Giger, S. Eggener, A. Oto, A study of T2-weighted MR image texture features and diffusion-weighted MR image features for computer-aided diagnosis of prostate cancer, in: Proceedings of SPIE 8670, Medical Imaging 2013: Computer-Aided Diagnosis, 2013, pp. 86701H-86701H-6.
183. Benassi A., Cohen S., Istas J. Identifying the multifractional function of a Gaussian process. Stat. Probab. Lett. 1998, 39(4):337-345.
184. Ahmed N., Natarajan T., Rao K. Discrete cosine transform. IEEE Trans. Comput. 1974, C-23(1):90-93.
185. Leung T., Malik J. Representing and recognizing the visual appearance of materials using three-dimensional textons. Int. J. Comput. Vis. 2001, 43(1):29-44.
186. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, CVPR 2005, vol. 1, 2005, pp. 886-893.
187. Belongie S., Malik J., Puzicha J. Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 2002, 24(4):509-522.
188. Zhao G., Ahonen T., Matas J., Pietikainen M. Rotation-invariant image and video description with local binary pattern features. IEEE Trans. Image Process. 2012, 21(4):1465-1477.
189. Ojala T., Pietikäinen M., Harwood D. A comparative study of texture measures with classification based on featured distributions. Pattern Recognit. 1996, 29(1):51-59.
190. Tofts P.S. Modeling tracer kinetics in dynamic Gd-DTPA MR imaging. J. Magn. Reson. Imaging 1997, 7(1):91-101.
191. Castorina P., Delsanto P.P., Guiot C. Classification scheme for phenomenological universalities in growth problems in physics and other sciences. Phys. Rev. Lett. 2006, 96:188701.
192. Ratiney H., Sdika M., Coenradie Y., Cavassila S., van Ormondt D., Graveron-Demilly D. Time-domain semi-parametric estimation based on a metabolite basis set. NMR Biomed. 2005, 18(1):1-13.
193. Vanhamme L., van den Boogaart A., Van Huffel S. Improved method for accurate and efficient quantification of MRS data with use of prior knowledge. J. Magn. Reson. 1997, 129:35-45.
194. T. Coleman, Y. Li, An interior trust region approach for nonlinear minimization subject to bounds, Technical Report, Cornell University, 1993.
195. Provencher S.W. Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn. Reson. Med. 1993, 30(6):672-679.
196. Coifman R., Wickerhauser M. Entropy-based algorithms for best basis selection. IEEE Trans. Inf. Theory 1992, 38(2):713-718.
197. Saeys Y., Inza I., Larranaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007, 23(19):2507-2517.
198. Peng H., Long F., Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27(8):1226-1238.
199. I. Fodor, A survey of dimension reduction techniques, Technical Report UCRL-ID-148494, Lawrence Livermore National Laboratory, 2002.
200. Jolliffe I.T. Principal Component Analysis 2002, Springer, New York. 2nd Ed.
201. M. Belkin, P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, in: Advances in Neural Information Processing Systems 14, MIT Press, 2001, pp. 585-591, . http://papers.nips.cc/book/advances-in-neural-information-processing-systems-14-2001.
202. Roweis S.T., Saul L.K. Nonlinear dimensionality reduction by locally linear embedding. Science 2000, 290(5500):2323-2326.
203. Bishop C.M. Pattern Recognition and Machine Learning 2006, Springer-Verlag, Inc., New York, Secaucus, NJ, USA.
204. Fred A., Jain A. Combining multiple clusterings using evidence accumulation. IEEE Trans. Pattern Anal. Mach. Intell. 2005, 27(6):835-850.
205. Friedman J. Regularized discriminant analysis. J. Am. Stat. Assoc. 1989, 84(405):165-175.
206. I. Rish, An empirical study of the naive Bayes classifier, in: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, 2001, pp. 41-46.
207. Freund Y., Schapire R. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 1997, 55(1):119-139.
208. Breiman L. Random forests. Mach. Learn. 2001, 45(1):5-32.
209. Z. Tu, Probabilistic boosting-tree: learning discriminative models for classification, recognition, and clustering, in: Tenth IEEE International Conference on Computer Vision, 2005, ICCV 2005, vol. 2, 2005, pp. 1589-1596.
210. Friedman J., Hastie T., Tibshirani R. Additive logistic regression. a statistical view of boosting. Ann. Stat. 1998, 28:2000.
211. Rasmussen C., Williams C. Gaussian Processes for Machine Learning 2005, The MIT Press, Cambridge.
212. Vapnik V., Lerner A. Pattern Recognition using Generalized Portrait Method. Autom. Remote Control 1964, 24:774-780.
213. B.E. Boser, I.M. Guyon, V.N. Vapnik, A training algorithm for optimal margin classifiers, in: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT '92, ACM, New York, NY, USA, 1992, pp. 144-152.
214. Tipping M. Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 2001, 1:211-244.
215. J. Quinonero-Candela, A. Girard, C. Rasmussen, Prediction at an Uncertain Input for Gaussian procePssesPP and relevance vector machines application to Multiple-Step ahead time-series forecasting, Technical Report, DTU Informatics, 2002.
216. D.F. Specht, Probabilistic neural networks for classification, mapping, or associative memory, in: 1998 IEEE International Conference on Neural Networks, vol. 1, 1988, pp. 525-532.
217. Efron B. Estimating the error rate of a prediction rule. improvement on cross-validation. J. Am. Stat. Assoc. 1983, 78(382):316-331.
218. Metz C.E. Receiver operating characteristic analysis. a tool for the quantitative evaluation of observer performance and imaging systems. J. Am. Coll. Radiol. 2006, 3(6):413-422.