Advertisement

Evolutionary Algorithm-Based Classifier Parameter Tuning for Automatic Ovarian Cancer Tissue Characterization and Classification

  • U. Rajendra Acharya
  • Muthu Rama Krishnan Mookiah
  • S. Vinitha SreeEmail author
  • Ratna Yanti
  • Roshan Martis
  • Luca Saba
  • Filippo Molinari
  • Stefano Guerriero
  • Jasjit S. Suri
Chapter

Abstract

Purpose: Ovarian cancer is one of the most common gynecological cancers in women. It is difficult to accurately and objectively diagnose benign and malignant ovarian tumors using ultrasound and other tests. Hence, there is an imperative need to develop a computer-aided diagnostic (CAD) system for ovarian tumor classification in order to reduce patient anxiety and cost of unnecessary biopsies. In this paper, we present an automatic CAD system for the detection of benign and malignant ovarian tumors using advanced image processing and data mining techniques.

Materials and Methods: In the proposed system, Hu’s invariant moments, Gabor transform parameters, and entropies are first extracted from the acquired ultrasound images. Significant features are then used to train a probabilistic neural network (PNN) classifier for classifying the images into benign and malignant categories. The model parameter (σ) for which the PNN classifier performs the best is identified using genetic algorithm (GA).

Results: The proposed system was validated using 1,300 benign and 1,300 malignant images obtained from ten patients with benign and ten with malignant disease, respectively. We used 23 statistically significant (p < 0.0001) features. On evaluating the classifier using tenfold cross-validation technique, we were able to achieve an average classification accuracy of 99.8 %, sensitivity of 99.2 %, and specificity of 99.6 % with the σ of 0.264.

Conclusion: The proposed automated system is automated and hence is more objective, can be easily deployed in any computer, fast, accurate, and can act as an adjunct tool in helping the physicians make a confident call on the nature of the ovarian tumor under evaluation.

Keywords

Ovarian cancer Hu’s invariant moments Gabor transform Probabilistic neural network entropy Computer-aided diagnosis 

References

  1. 1.
    Jemal A, Siegel R, Ward E. Cancer statistics, 2010. CA Cancer J Clin. 2010;60:277–300.PubMedCrossRefGoogle Scholar
  2. 2.
    NIH Consensus Development Panel on Ovarian Cancer. NIH consensus conference. Ovarian cancer. Screening, treatment, and follow-up. JAMA. 1995;273:491–7.CrossRefGoogle Scholar
  3. 3.
    Horner MJ, Ries LAG, Krapcho M, Neyman N, Aminou R, Howlader N, Altekruse SF, Feuer EJ, Huang L, Mariotto A, Miller BA, Lewis DR, Eisner MP, Stinchcomb DG, Edwards BK, editors. SEER cancer statistics review, 1975–2006, National Cancer Institute, Bethesda. SEER Website. seer.cancer.gov/csr/1975_2006. Based on November 2008 SEER data submission. Published 29 May 2009.
  4. 4.
    Predanic M, Vlahos N, Pennisi JA, Moukhtar M, Alee FA. Color and pulsed Doppler sonography, gray-scale imaging, and serum CA 125 in the assessment of adnexal disease. Obstet Gynecol. 1996;88:283–8.PubMedCrossRefGoogle Scholar
  5. 5.
    Wu CC, Lee CN, Chen TM, Lai JI, Hsieh CY, Hwieh FJ. Factors contributing to the accuracy in diagnosing ovarian malignancy by color Doppler ultrasound. Obstet Gynecol. 1994;84:605–8.PubMedGoogle Scholar
  6. 6.
    Iyer VR, Lee SI. MRI, CT, and PET/CT for ovarian cancer detection and adnexal lesion characterization. AJR Am J Roentgenol. 2010;194:311–21.PubMedCrossRefGoogle Scholar
  7. 7.
    Sohaib SA, Reznek RH. MR imaging in ovarian cancer. Cancer Imaging. 2007;7 Spec No A:S119–29.PubMedCrossRefGoogle Scholar
  8. 8.
    Frangioni JV. New technologies for human cancer imaging. J Clin Oncol. 2008;26:4012–21.PubMedCrossRefGoogle Scholar
  9. 9.
    Anderiesz C, Quinn MA. Screening for ovarian cancer. Med J Aust. 2003;178:655–6.PubMedGoogle Scholar
  10. 10.
    Jeong YY, Outwater EK, Kang HK. Imaging evaluation of ovarian masses. Radiographics. 2000;20:1445–70.PubMedCrossRefGoogle Scholar
  11. 11.
    Pascual MA, Graupera B, Hereter L, Rotili A, Rodriguez I, Alcázar JL. Intra-and interobserver variability of 2D and 3D transvaginal sonography in the diagnosis of benign versus malignant adnexal masses. J Clin Ultrasound. 2011;39:316–21.PubMedCrossRefGoogle Scholar
  12. 12.
    Guerriero S, Alcazar JL, Pascual MA, Ajossa S, Gerada M, Bargellini R, Virgilio B, Melis GB. Intraobserver and interobserver agreement of greyscale typical ultrasonographic patterns for the diagnosis of ovarian cancer. Ultrasound Med Biol. 2008;34:1711–6.PubMedCrossRefGoogle Scholar
  13. 13.
    Kim KA, Park CM, Lee JH, Kim HK, Cho SM, Kim B, Seol HY. Benign ovarian tumors with solid and cystic components that mimic malignancy. AJR Am J Roentgenol. 2004;182:1259–65.PubMedCrossRefGoogle Scholar
  14. 14.
    Acharya UR, Vinitha Sree S, Krishnan MM, Molinari F, Garberoglio R, Suri JS. Non-invasive automated 3D thyroid lesion classification in ultrasound: a class of ThyroScan™ systems. Ultrasonics. 2012;52:508–20.PubMedCrossRefGoogle Scholar
  15. 15.
    Saba L, Gao H, Acharya UR, Sannia S, Ledda G, Suri JS. Analysis of carotid artery plaque and wall boundaries on CT images by using a semi-automatic method based on level set model. Neuroradiology. 2012;54(11):1207–14. PubMed PMID: 22562690.PubMedCrossRefGoogle Scholar
  16. 16.
    Renz C, Rajapakse JC, Razvi K, Liang SKC. Ovarian cancer classification with missing data. In: Proceedings of 9th international conference on Neural Information Processing, Singapore, 2002, vol. 2, p. 809–13.Google Scholar
  17. 17.
    Assareh A, Moradi MH. 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, 2007, p. 502–6.Google Scholar
  18. 18.
    Tan TZ, Quek C, Ng GS, Razvi K. Ovarian cancer diagnosis with complementary learning fuzzy neural network. Artif Intell Med. 2008;43:207–22.PubMedCrossRefGoogle Scholar
  19. 19.
    Meng H, Hong W, Song J, Wang L. Feature extraction and analysis of ovarian cancer proteomic mass spectra. In: 2nd international conference on Bioinformatics and Biomedical Engineering, Shanghai, 2008, p. 668–71.Google Scholar
  20. 20.
    Tang KL, Li TH, Xiong WW, Chen K. Ovarian cancer classification based on dimensionality reduction for SELDI-TOF data. BMC Bioinformatics. 2010;11:109.PubMedCentralPubMedCrossRefGoogle Scholar
  21. 21.
    Petricoin F. Use of proteomic patterns serum to identify ovarian cancer. The Lancet. 2002;359:572–7.CrossRefGoogle Scholar
  22. 22.
    Tailor A, Jurkovic D, Bourne TH, Collins WP, Campbell S. Sonographic prediction of malignancy in adnexal masses using an artificial neural network. Br J Obstet Gynaecol. 1999;106:21–30.PubMedCrossRefGoogle Scholar
  23. 23.
    Brüning J, Becker R, Entezami M, Loy V, Vonk R, Weitzel H, Tolxdorff T. Knowledge-based system ADNEXPERT to assist the sonographic diagnosis of adnexal tumors. Methods Inf Med. 1997;36:201–6.PubMedGoogle Scholar
  24. 24.
    Biagiotti R, Desii C, Vanzi E, Gacci G. Predicting ovarian malignancy: application of artificial neural networks to transvaginal and color Doppler flow US. Radiology. 1999;210:399–403.PubMedCrossRefGoogle Scholar
  25. 25.
    Zimmer Y, Tepper R, Akselrod S. An automatic approach for morphological analysis and malignancy evaluation of ovarian masses using B-scans. Ultrasound Med Biol. 2003;29:1561–70.PubMedCrossRefGoogle Scholar
  26. 26.
    Lucidarme O, Akakpo JP, Granberg S, Sideri M, Levavi H, Schneider A, Autier P, Nir D, Bleiberg H, Ovarian HistoScanning Clinical Study Group. A new computer-aided diagnostic tool for non-invasive characterisation of malignant ovarian masses: results of a multicentre validation study. Eur Radiol. 2010;20:1822–30.PubMedCrossRefGoogle Scholar
  27. 27.
    Bellman RE. Dynamic programming. Mineola: Courier Dover Publications; 2003.Google Scholar
  28. 28.
    Hata T, Yanagihara T, Hayashi K, Yamashiro C, Ohnishi Y, Akiyama M, Manabe A, Miyazaki K. Three-dimensional ultrasonographic evaluation of ovarian tumours: a preliminary study. Hum Reprod. 1999;14:858–61.PubMedCrossRefGoogle Scholar
  29. 29.
    Laban M, Metawee H, Elyan A, Kamal M, Kamel M, Mansour G. Three-dimensional ultrasound and three-dimensional power Doppler in the assessment of ovarian tumors. Int J Gynaecol Obstet. 2007;99:201–5.PubMedCrossRefGoogle Scholar
  30. 30.
    Cohen LS, Escobar PF, Scharm C, Glimco B, Fishman DA. Three-dimensional power Doppler ultrasound improves the diagnostic accuracy for ovarian cancer prediction. Gynecol Oncol. 2001;82:40–8.PubMedCrossRefGoogle Scholar
  31. 31.
    Hu M. Visual pattern recognition by moment invariants. IRE Trans Info Theory. 1962;8:179–87.Google Scholar
  32. 32.
    Shen L, Bai L. A review of Gabor wavelets for face recognition. Patt Anal Appl. 2006;9:273–92.CrossRefGoogle Scholar
  33. 33.
    Manjunath BS, Ma WY. Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell. 1996;18:837–42.CrossRefGoogle Scholar
  34. 34.
    Pharwaha APS, Singh B. Shannon and non-shannon measures of entropy for statistical texture feature extraction in digitized mammograms. Proceedings of the World Congress on Engineering and Computer Science (WCECS). San Francisco, USA. 2009, Vol 2. p. 2179.Google Scholar
  35. 35.
    Box JF. Guinness, gosset, fisher, and small samples. Statist Sci. 1987;2:45–52.CrossRefGoogle Scholar
  36. 36.
    Specht DF. Probabilistic neural networks. Neural Networks. 1990;3:109–18.CrossRefGoogle Scholar
  37. 37.
    Raghu PP, Yegnanarayana B. Supervised texture classification using a probabilistic neural network and constraint satisfaction model. IEEE Trans Neural Netw. 1998;9:516–22.PubMedCrossRefGoogle Scholar
  38. 38.
    Ng EYK, Acharya UR, Keith LG, Lockwood S. Detection and differentiation of breast cancer using neural classifiers with first warning thermal sensors. Inform Sciences. 2007;177:4526–38.CrossRefGoogle Scholar
  39. 39.
    Goldberg DE. Genetic algorithms in search, optimization, and machine learning. Reading: Addison Wesley Professional Publishers, Boston, MA, USA. 1989.Google Scholar
  40. 40.
    Deb K. Multi-objective optimization using evolutionary algorithms. Chichester/New York: Wiley; 2009.Google Scholar
  41. 41.
    Bast Jr RC, Badgwell D, Lu Z, Marquez R, Rosen D, Liu J, Baggerly KA, Atkinson EN, Skates S, Zhang Z, Lokshin A, Menon U, Jacobs I, Lu K. New tumor markers: CA125 and beyond. Int J Gynecol Cancer. 2005;15:274–81.PubMedCrossRefGoogle Scholar
  42. 42.
    Zaidi SI. Fifty years of progress in gynecologic ultrasound. Int J Gynaecol Obstet. 2007;99:195–7.PubMedCrossRefGoogle Scholar
  43. 43.
    Menon U, Talaat A, Rosenthal AN, Macdonald ND, Jeyerajah AR, Skates SJ, Sibley K, Oram DH, Jacobs IJ. Performance of ultrasound as a second line test to serum CA125 in ovarian cancer screening. BJOG. 2000;107:165–9.PubMedCrossRefGoogle Scholar
  44. 44.
    Acharya UR, Sree SV, Krishnan MRM, Saba L, Molinari F, Guerriero S, Suri JS. Ovarian tumor characterization using 3D ultrasound. Technol Cancer Res Treat. 2012;11(6):543–52.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • U. Rajendra Acharya
    • 1
  • Muthu Rama Krishnan Mookiah
    • 1
  • S. Vinitha Sree
    • 2
    Email author
  • Ratna Yanti
    • 1
  • Roshan Martis
    • 1
  • Luca Saba
    • 3
  • Filippo Molinari
    • 4
  • Stefano Guerriero
    • 5
  • Jasjit S. Suri
    • 6
    • 7
  1. 1.Department of Electronics and Computer EngineeringNgee Ann PolytechnicSingaporeSingapore
  2. 2.Global Biomedical Technologies Inc.RosevilleUSA
  3. 3.Department of RadiologyAzienda Ospedaliero Universitaria di CagliariCagliariItaly
  4. 4.Biolab, Department of Electronics and TelecommunicationsPolitecnico di TorinoTorinoItaly
  5. 5.Department of Obstetrics and GynecologyUniversity of Cagliari, Ospedale San Giovanni di DioCagliariItaly
  6. 6.CTO, Global Biomedical TechnologiesRosevilleUSA
  7. 7.Department of Electrical EngineeringIdaho State University (Aff.)PocatelloUSA

Personalised recommendations