Ovarian Tumor Characterization Using 3D Ultrasound

  • U. Rajendra Acharya
  • M. Muthu Rama Krishnan
  • Luca Saba
  • Filippo Molinari
  • Stefano Guerriero
  • Jasjit S. Suri


Among gynecological malignancies, ovarian cancer is the most frequent cause of death. Preoperative determination of whether a tumor is benign or malignant has often been found to be difficult. Because of such inconclusive findings from ultrasound images and other tests, many patients with benign conditions have been offered unnecessary surgeries thereby increasing patient anxiety and healthcare cost. The key objective of our work is to develop an adjunct computer-aided diagnostic (CAD) technique that uses ultrasound images of the ovary and image mining algorithms to accurately classify benign and malignant ovarian tumor images. In this algorithm, we extract texture features based on Local Binary Patterns (LBP) and Laws Texture Energy (LTE) and use them to build and train a support vector machine (SVM) classifier. Our technique was validated using 1,000 benign and 1,000 malignant images, and we obtained a high accuracy of 99.9 % using a SVM classifier with a radial basis function (RBF) kernel. The high accuracy can be attributed to the determination of the novel combination of the 16 texture-based features that quantify the subtle changes in the images belonging to both classes. The proposed algorithm has the following characteristics: cost-effectiveness, complete automation, easy deployment, and good end-user comprehensibility. We have also developed a novel integrated index, Ovarian Cancer Index (OCI), which is a combination of the texture features, to present the physicians with a more transparent adjunct technique for ovarian tumor classification.


Ovarian cancer Local Binary Pattern Laws Texture Energy Classification Support vector machine Computer-aided diagnosis Ovarian Cancer Index 



Cancer antigen 125


Computer-aided diagnosis


Digital Imaging and Communications in Medicine






Local Binary Pattern


Laws Texture Energy


Mass spectrometry


Ovarian Cancer Index


Positive predictive value


Radial basis function


Standard deviation


Support vector machine


Texture energy measurements






Conflict of Interest Statement

None of the authors have any financial or personal conflict of interest that could inappropriately influence the writing or publication of this manuscript.


  1. 1.
    NCI (National Cancer Institute) on ovarian cancer. Information available at Last accessed Aug 2011.
  2. 2.
    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
  3. 3.
    Zaidi SI. Fifty years of progress in gynecologic ultrasound. Int J Gynaecol Obstet. 2007;99:195–7.PubMedCrossRefGoogle Scholar
  4. 4.
    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
  5. 5.
    Nossov V, Amneus M, Su F, Lang J, Janco JM, Reddy ST, Farias-Eisner R. The early detection of ovarian cancer: from traditional methods to proteomics. Can we really do better than serum CA-125? Am J Obstet Gynecol. 2008;199:215–23.PubMedCrossRefGoogle Scholar
  6. 6.
    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
  7. 7.
    Lenic M, Zazula D, Cigale, B. 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, 2007, p. 205–12.Google Scholar
  8. 8.
    Hiremath PS, Tegnoor JR. Recognition of follicles in ultrasound images of ovaries using geometric features. In: Proceedings of international conference on Biomedical and Pharmaceutical Engineering, Singapore, 2009, p. 1–8.Google Scholar
  9. 9.
    Deng Y, Wang Y, Chen P. Automated detection of polycystic ovary syndrome from ultrasound images. In: Proceedings of the 30th annual international IEEE Engineering in Medicine and Biology Society conference, Vancouver, 2008, p. 4772–5.Google Scholar
  10. 10.
    Sohail ASM, Rahman MM, Bhattacharya P, Krishnamurthy S, Mudur SP. 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, 2010, p. 288–91.Google Scholar
  11. 11.
    Sohail ASM, Bhattacharya P, Mudur SP, Krishnamurthy S. Selection of optimal texture descriptors for retrieving ultrasound medical images. In: IEEE international symposium on Biomedical Imaging: From Nano to Macro, Chicago, 2011, p. 10–6.Google Scholar
  12. 12.
    Molinari F, Liboni W, Giustetto P, Badalamenti S, Suri JS. Automatic computer-based tracings (ACT) in longitudinal 2-D ultrasound images using different scanners. J Mech Med Biol. 2009;9:481–505.CrossRefGoogle Scholar
  13. 13.
    Ojala T, Pietikäinen M, Harwood D. A comparative study of texture measures with classification based on feature distributions. Pattern Recogn. 1996;29:51–9.CrossRefGoogle Scholar
  14. 14.
    Ojala T, Pietikäinen M, Maenpaa T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal. 2002;24:971–87.CrossRefGoogle Scholar
  15. 15.
    Liao S, Law MWK, Chung ACS. Dominant local binary patterns for texture classification. IEEE Trans Image Process. 2009;18:1107–18.PubMedCrossRefGoogle Scholar
  16. 16.
    Zhang B, Gao Y, Zhao S, Liu J. Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans Image Process. 2010;19:533–44.PubMedCrossRefGoogle Scholar
  17. 17.
    Laws KI. Rapid texture identification. SPIE Conf Series. 1980;238:376–80.Google Scholar
  18. 18.
    Petrou M, Sevilla PG. Image processing – dealing with texture. Chichester: Wiley; 2006.CrossRefGoogle Scholar
  19. 19.
    Mirmehdi M, Xie X, Suri JS. Handbook of texture analysis. London: Imperial College Press; 2008.CrossRefGoogle Scholar
  20. 20.
    Vapnik V. Statistical learning theory. New York: Wiley; 1998.Google Scholar
  21. 21.
    Burgess CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc. 1998;2:1–47.Google Scholar
  22. 22.
    Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20:273–97.Google Scholar
  23. 23.
    David V, Sanchez A. Advanced support vector machines and kernel methods. Neurocomputing. 2003;55:5–20.CrossRefGoogle Scholar
  24. 24.
    Muller KR, Mika S, Ratsch G, Tsuda K, Scholkopf B. An introduction to kernel based learning algorithms. IEEE Trans Neural Network. 2001;12:181–201.CrossRefGoogle Scholar
  25. 25.
    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
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • U. Rajendra Acharya
    • 1
    • 2
  • M. Muthu Rama Krishnan
    • 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.Department of Biomedical Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia
  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.Department of Biomedical EngineeringCTO, Global Biomedical TechnologiesRosevilleUSA
  7. 7.Department of Biomedical EngineeringIdaho State University (Aff.)PocatelloUSA

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