Ovarian Tissue Characterization in Ultrasound: A Review

Technology in Cancer Research and Treatment

Volumn 14, Issue 3, 2015, Pages 251-261

  Acharya, U Rajendra ^a,b   Molinari, Filippo ^c   Sree, S Vinitha ^d   Swapna, G ^e   Saba, Luca ^f   Guerriero, Stefano ^g   Suri, Jasjit S ^d,h,i  

^a DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

^b UNIVERSITY OF MALAYA   (Malaysia)

^c POLITECNICO DI TORINO   (Italy)

^d Global Biomedical Technologies Inc   (United States)

^e Government Engineering College   (India)

^f DEPARTMENT OF RADIOLOGY   (Italy)

^g UNIVERSITY OF CAGLIARI   (Italy)

^h AtheroPoint LLC   (United States)

ⁱ Nucl Eng Idaho State University   (United States)

Author keywords

benign; classifier; feature; malignant; ovarian; texture; tumor

Indexed keywords

CA 125 ANTIGEN;

ALGORITHM; ARTICLE; BLOOD ANALYSIS; CANCER STAGING; CLINICAL FEATURE; COMPUTER ASSISTED RADIOGRAPHY; EXPERIMENTAL DESIGN; FEMALE; GOLD STANDARD; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; MASS SPECTROMETRY; OVARIAN TISSUE; OVARY CANCER; TISSUE CHARACTERIZATION; TUMOR CLASSIFICATION; ULTRASOUND; COMPUTER ASSISTED DIAGNOSIS; OVARIAN NEOPLASMS; PATHOLOGY; PROCEDURES;

DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; OVARIAN NEOPLASMS; ULTRASONICS;

EID: 84952791134     PISSN: 15330346     EISSN: 15330338     Source Type: Journal    
DOI: 10.1177/1533034614547445     Document Type: Article

References (57)

1. A.JemalR.SiegelJ.XuE.Ward. Cancer statistics, 2010. CA Cancer J Clin. 2010;60(5):277–300. doi: 10.3322/CA.2007.0010.
2. M.J.HornerL.A.G.RiesM.Krapcho, eds. SEER Cancer Statistics Review, 1975-2006. Bethesda, MD: National Cancer Institute; 2009. SEER Web site. seer.cancer.gov/csr/1975_2006. Based on November 2008 SEER data submission. Published May 29, 2009.
3. J.M.PiekP.J.van DiestR.H.Verheijen. Ovarian carcinogenesis: an alternative hypothesis. Adv Exp Med Biol. 2008;622:79–87. doi: 10.1007/978-0-387-68969-2_7.
4. W.D.FoulkesA.Y.Shuen. In brief: BRCA1 and BRCA2. J Pathol. 2013;230(4):347–349. doi: 10.1002/path.4205.
5. N.F.VlahosK.P.EconomopoulosS.Fotiou. Endometriosis, in vitro fertilization and the risk of gynaecological malignancies, including ovarian and breast cancer. Best Pract Res Clin Obstet Gynaecol. 2010;24(1):39–50. doi: 10.1016/j.bpobgyn.2009.08.004.
6. S.R.LakhaniS.ManekF.Penault-Llorca. Pathology of ovarian cancers in BRCA1 and BRCA2 carriers. Clin Cancer Res. 2004;10(7):2473–2481. doi: 10.1158/1078-0432.CCR-1029-3.
7. C.R.BankheadS.T.KehoeJ.Austoker. Symptoms associated with diagnosis of ovarian cancer: a systematic review. BJOG. 2005;112(7):857–865. doi: 10.1111/j.1471-0528.2005.00572.x.
8. B.A.GoffL.S.MandelC.H.MelanconH.G.Muntz. Frequency of symptoms of ovarian cancer in women presenting to primary care clinics. JAMA. 2004;291(22):2705–2712. doi:10.1001/jama.291.22.2705.
9. A.B.RyersonC.EhemanJ.Burton. Symptoms, diagnoses, and time to key diagnostic procedures among older U.S. women with ovarian cancer. Obstet Gynecol. 2007;109(5):1053–1061. doi: 10.1097/01.AOG.0000260392.70365.5e
10. M.A.RossingK.G.WicklundK.L.Cushing-HaugenN.S.Weiss. Predictive value of symptoms for early detection of ovarian cancer. J Natl Cancer Inst. 2010;102(4):222–229. doi: 10.1093/jnci/djp500.
11. K.A.Rao. Textbook of Gynaecology. Gurgaon, India: Elsevier India; 2008.
12. R.C.BastJrD.BadgwellZ.Lu. New tumor markers: CA125 and beyond. Int J Gynecol Cancer. 2005;15(suppl 3):274–281. doi: 10.1111/j.1525-1438.2005.00441.x.
13. S.Zaidi. Fifty years of progress in gynecologic ultrasound. Int J Gynaecol Obstet. 2007;99(3):195–197. doi: 10.1016/j.ijgo.2007.08.002.
14. J.Laura. Test to help determine if ovarian masses are cancer. Wall St J. 2010. (http://online.wsj.com/news/articles/SB10001424052748704869304575109703066893506)
15. M.LenicD.ZazulaB.Cigale. Segmentation of ovarian ultrasound images using single template cellular neural networks trained with support vector machines. In: Proceedings of 20th IEEE International Symposium on Computer-Based Medical Systems, Maribor, June 20-22, 2007; 2007:205–212.
16. A.S.M.SohailM.M.RahmanP.BhattacharyaS.KrishnamurthyS.P.Mudur. Retrieval and classification of ultrasound images of ovarian cysts combining texture features and histogram moments. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, April 14-17, 2010; 2010:288–291.
17. A.S.M.SohailP.BhattacharyaS.P.MudurS.Krishnamurthy. Selection of optimal texture descriptors for retrieving ultrasound medical images. In: IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Chicago, IL, March 30, 2011-April 2, 2011; 2011:10–16.
18. U.R.AcharyaS.V.SreeM.M.Krishnan. Ovarian tumor characterization using 3D ultrasound. Technol Cancer Res Treat. 2012;11(6):543–552. doi: 10.7785/tcrt.2012.500272.
19. U.R.AcharyaM.R.MookiahS.Vinitha Sree. Evolutionary algorithm-based classifier parameter tuning for automatic ovarian cancer tissue characterization and classification. Ultraschall Med. 2014;35(3):237–245. doi: 10.1055/s-0032-1330336.
20. U.R.AcharyaS.V.SreeL.SabaF.MolinariS.GuerrieroJ.S.Suri. Ovarian tumor characterization and classification using ultrasound-a new online paradigm. J Digit Imaging. 2013;26(3):544–553. doi: 10.1007/s10278-012-9553-8.
21. U.R.AcharyaS.V.SreeS.Kulshreshtha. GyneScan: an improved online paradigm for screening of ovarian cancer via tissue characterization. Technol Cancer Res Treat. 2014;13(6):529–539. doi: 10.7785/tcrtexpress.
22. T.H.BourneS.CampbellK.Reynolds. The potential role of serum CA 125 in an ultrasound-based screening program for familial ovarian cancer. Gynecol Oncol. 1994;52(3):379–385. doi: 10.1006/gyno.1994.1065.
23. D.O.SparkmanMass Spectrometry Desk Reference. Pittsburgh: Global View Pub; 2000. ISBN 0-9660813-2-3.
24. A.M.SassoneI.E.Timor-TrishA.ArtnerC.WesthoffW.B.Warren. Transvaginal sonographic characterization of ovarian disease: evaluation of a new scoring system to predict ovarian malignancy. Obstet Gynecol. 1991;78(1):70–76.
25. T.OjalaM.PietikäinenD.A.HarwoodComparative study of texture measures with classification based on feature distributions. Pattern Recogn. 1996;29(1):51–59.
26. T.OjalaM.PietikäinenT.Maenpaa. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal. 2002;24(7):971–987.
27. S.LiaoM.W.LawA.C.Chung. Dominant local binary patterns for texture classification. IEEE Trans Image Process. 2009;18(5):1107–1118.
28. B.ZhangY.GaoS.ZhaoJ.Liu. Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans Image Process. 2010;19(2):533–544.
29. R.GuptaP.E.Undrill. The use of texture analysis to delineate suspicious masses in mammography. Phys Med Biol. 1995;40(5):835–855. doi:10.1088/0031-9155/40/5/009.
30. K.I.Laws. Rapid texture identification. SPIE Conference Series. 1980;238:376–380.
31. A.P.S.PharwahaB.Singh. Shannon and non-shannon measures of entropy for statistical texture feature extraction in digitized mammograms. Lect Notes Eng Comput Sci. 2179 (2009)
32. M.Hu. Visual pattern recognition by moment invariants. IRE Trans Info Theory. 1962;8(2):179–187. doi: 10.1109/TIT.1962.1057692.
33. M.KociołekA.MaterkaM.StrzeleckiP.Szczypiński. Discrete wavelet transform–derived features for digital image texture analysis. Proc of International Conference on Signals and Electronic Systems. 2001;(18-21):163–168.
34. M.R.K.KrishnanU.R.AcharyaC.M.LimA.PetznickJ.S.Suri. Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features. Knowl Based Syst. 2012;33:73–82. doi: 10.1016/j.knosys.2012.02.010.
35. C.L.NikiasJ.M.Mendel. Signal processing with higher order spectra. IEEE Signal Processing Magazine. 1993;10(3):10–37. doi: 10.1109/79.221324.
36. C.L.NikiasM.R.Rughuveer. Bispectrum estimation: a digital signal processing framework. Proc IEEE. 1987;75(7):869–890. doi: 10.1109/PROC.1987.13824.
37. R.A.Haubrich. Earth Noises, 5 to 500 milicycles per second 1. J Geophysics Res. 1985;1415–1427.
38. K.C.ChuaV.ChandranU.R.AcharyaC.M.Lim. Analysis of epileptic EEG signals using higher order spectra. J Med Eng Technol. 2009;33(1):42–50. doi: 10.1080/03091900701559408.
39. J.HanM.M.Kamber. Data Mining: Concepts and Techniques, 2nd edition. Burlington, MA: Morgan Kaufmann; 2006.
40. P.D.Wasserman. Advanced Methods in Neural Computing. New York, USA: Van Nostrand Reinhold; 1993.
41. S.Haykin. Neural Networks and Learning Machines. 3 rd ed. Upper Saddle River, NJ: Prentice Hall; 2008.
42. V.Vapnik. Statistical Learning Theory. New York: Wiley; 1998.
43. C.J.C.Burgess. A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc. 1998;2(2):1–47.
44. N.ChristianiniJ.Taylor. Support Vector Machines and other kernel-based learning methods. Cambridge: Cambridge University Press; 2000.
45. K.R.MullerS.MikaG.RatschK.TsudaB.Scholkopf. An introduction to kernel based learning algorithms. IEEE Trans Neural Network. 2001;12(2):181–201.
46. C.W.HsuC.C.ChangC.J.Lin. A Practical Guide to Support Vector Classification. Taipei City, Taiwan: Technical report, Department of Computer Science, National Taiwan University; 2003.
47. C.RenzJ.C.RajapakseK.RazviS.K.C.Liang. Ovarian cancer classification with missing data. In: Proceedings of 9th International Conference on Neural Information Processing. 2002;2:809–813.
48. A.AssarehM.H.Moradi. Extracting efficient fuzzy if-then rules from mass spectra of blood samples to early diagnosis of ovarian cancer. In: IEEE Symposium on Computational Intelligence and Bioinformatics and Computational Biology, Honolulu, HI, April 1-5, 2007; 2007:502–506.
49. T.Z.TanC.QuekG.S.NgK.Razvi. Ovarian cancer diagnosis with complementary learning fuzzy neural network. Artif Intell Med. 2008;43(3);207–222. doi: 10.1016/j.artmed.2008.04.003.
50. H.MengW.HongJ.SongL.Wang. Feature extraction and analysis of ovarian cancer proteomic mass spectra. In: 2nd International Conference on Bioinformatics and Biomedical Engineering, Shanghai, May 16-18, 2008; 2008:668–671.
51. K.L.TangT.H.LiW.W.XiongK.Chen. Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data. BMC Bioinformatics. 2010;11:109. doi: 10.1186/1471-2105-11-109.
52. E.F.PetricoinA.M.ArdekaniB.A.Hitt. Use of proteomic patterns in serum to identify ovarian cancer. Lancet. 2002;359(9306):572–577. doi: 10.1016/S0140-6736(02)07746-2.
53. A.TailorD.JurkovicT.H.BourneW.P.CollinsS.Campbell. Sonographic prediction of malignancy in adnexal masses using an artificial neural network. Br J Obstet Gynaecol. 1999;106(1):21–30. doi: 10.1111/j.1471-0528.1999.tb08080.x.
54. J.BrüningR.BeckerM.Entezami. Knowledge-based system ADNEXPERT to assist the sonographic diagnosis of adnexal tumors. Methods Inf Med. 1997;36(3):201–206.
55. R.BiagiottiC.DesiiE.VanziG.Gacci. Predicting ovarian malignancy: application of artificial neural networks to transvaginal and color Doppler flow US. Radiology. 1999;210(2):399–403. doi:http://dx.doi.org/10.1148/radiology.210.2.r99fe18399.
56. Y.ZimmerR.TepperS.Akselrod. An automatic approach for morphological analysis and malignancy evaluation of ovarian masses using B-scans. Ultrasound Med Biol. 2003;29(11):1561–1570. doi: 10.1016/j.ultrasmedbio.2003.08.013
57. O.LucidarmeJ.P.AkakpoS.Granberg. A new computer-aided diagnostic tool for non-invasive characterisation of malignant ovarian masses: results of a multicentre validation study. Eur Radiol. 2010;20(8):1822–1830. doi: 10.1007/s00330-010-1750-6.