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Fuzzy inference system for follicle detection in ultrasound images of ovaries

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Abstract

The ovarian ultrasound imaging is an effective tool in infertility treatment. Monitoring the follicles is especially important in human reproduction. Periodic measurements of the size and shape of follicles over several days are the primary means of evaluation by physicians. Today monitoring the follicles is done by non-automatic means with human interaction. This work can be very demanding and inaccurate and, in most of the cases, means only an additional burden for medical experts. To improve the performance of follicle detection in ultrasound images of ovaries, we develop a new algorithm using fuzzy logic. The proposed method employs contourlet transform for despeckling the ultrasound images of ovaries, active contours without edge method for segmentation and fuzzy logic for classification. The follicles in an ovary are characterized by seven geometric features which are used as inputs to the fuzzy logic block of the Fuzzy Inference System. The output of the fuzzy logic block is a follicle class or non follicle class. The fuzzy-knowledge-base consists of a set of physically interpretable if-then rules providing physical insight into the process. The experimentation has been done using sample ultrasound images of ovaries and the results are compared with the inferences drawn by interval based classifier and also those drawn by the medical expert. The experimental results demonstrate the efficacy of the proposed method.

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References

  • Alcala-Fdez J, Alcala R, Herrera F (2011) A fuzzy association rule-based classification model for high-dimensional problems with genetic rule selection and lateral tuning. IEEE TransFuzzy Syst 19(5):857–872

    Google Scholar 

  • Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277

    Google Scholar 

  • Chan TF, Yezrielev Sandberg B, Vese LA (2000) Active contours without edges for vector valued images. J Vis Commun Image Represent 11:130–141

    Google Scholar 

  • Cigale B, Zazula D (2000) Segmentation of ovarian ultrasound images using cellular neural networks. In: Proceedings of 7th international workshop on systems, signals and image processing, Maribor, Slovenia, pp 33–36

  • Dash S et al (2003) Fuzzy-logic based trend classification for fault diagnosis of chemical processes. Int J Comput Chem Eng 27:347–362

    Article  Google Scholar 

  • Gonzalez RC, Woods RE (2002) Digital Image Processing. 2nd edn. Pearson Edu, London

  • Gore MA, Nayudu PL, Vlaisavljevic V, Thomus N (1995) Prediction of ovarian cycle outcome by follicular characteristics, Stage I. In: Human reproduction, vol 10. Elsevier, Amsterdam, pp 2313–2319

  • Hiremath PS, Akkasaliger P (2009) Despeckling medical ultrasound images using the contourlet transform. In: 4th AMS Indian international conference on artificial intelligence (IICAI-09), 16–18 Dec. 2009, Tumkur, India

  • Hiremath PS, Tegnoor JR (2008) Automatic detection of follicles in ultrasound images of ovaries. In: Proceedings of 2nd international conference on cognition and recognition (ICCR08), 8–10 April 2008, Mysore, India, pp 468–473

  • Hiremath PS, Tegnoor JR (2009) Automatic detection of follicles in ultrasound images of ovaries using optimal threshoding method. Int J Comput Eng 1(1):221–225. ISSN-0975-6116, 2009

    Google Scholar 

  • Hiremath PS, Tegnoor JR (2009) Automatic detection of follicles in ultrasound images of ovaries. In: Proceedings of international conference on systemics, cybernetics and informatics—(ICSCI09), 07–10 Dec. 2009, Hyderabad, India, pp 327–330

  • Hiremath PS, Tegnoor JR (2009) Automatic detection, of follicles in ultrasound images of ovaries using horizontal and vertical scanline thresholding method. In: Proceedings of 2nd international conference on signal and image processing—(ICSIP09), 12–14, (Aug. 2009) Mysore. India, pp 468–473

  • Hiremath PS, Tegnoor JR (2009) Detection follicle, in ultrasound images of ovaries using scanline thresholding method. In: Proceedings of 2nd IEEE international conference on advances in computer vision and information technology (ACVIT-09), 16–18, (Dec. 2009) Aurangabad, India, pp 245–251

  • Hiremath PS, Tegnoor JR (2009) Recognition of follicles in ultrasound images of ovaries using geometric features. In: Proceedings of 2nd IEEE international conference on biomedical and pharmaceutical engineering (ICBPE-09), 2–4 Dec. 2009, Singapore. ISBN:978-1-4244-4764-0/09

  • Hiremath PS, Tegnoor JR (2010) Automatic detection of follicles in ultrasound images of ovaries using active contours method. In: Proceedings of IEEE international conference on computational intelligence and computing research (ICCIC-2010), 28–29 Dec. 2010, Coimbatore, India. ISBN:97881-8371-3627/10

  • Hiremath PS, Tegnoor JR (2010) Automatic detection of follicles in ultrasound images of ovaries using edge based method. Int J Comput Appl (IJCA) (Special issue on RTIPPR(2):No. 3, Article 8), pp 120–125. ISSN:0975–8887

  • Hiremath PS, Tegnoor JR (2010) Automatic detection of follicles in ultrasound images of ovaries using HRGMF based segmentation. Int J Multimedia Comput Vis Mach Learn 1(1):83–87

    Google Scholar 

  • Hiremath PS, Tegnoor JR (2010) Contourlet based method for follicle detection in ultrasound images of ovaries. In: Proceedings of national seminar on recent trends in image processing and pattern recognition (RTIPPR-2010), 15–16 Feb. 2010, Bidar, India, pp 114–120. ISBN:978-93-80043-74-6

  • Hiremath PS, Tegnoor JR (2010) Follicle detection in ultrasound images of ovaries using active contours method. In: Proceedings of 3rd IEEE in international conference on signal and image processing (ICSIP 10), 15–17 Dec. 2010, Kaveraipettai, Chennai, India, pp 286–291, ISBN:978-1-4244-8594-9/10

  • Hiremath PS, Tegnoor JR (2010) Fuzzy logic based detection of follicles in ultrasound images of ovaries. In: Proceedings of 5th Indian international conference on artificial intelligence (IICAI-2011), 14–16 Dec. 2011, Tumkur, Karnataka, India, pp 178–189. ISBN:978-0-9727412-8-6

  • Hiremath PS, Tegnoor JR (2012) Automated ovarian classification in digital ultrasound images using SVM. Int J Eng Res Technol 1(6)

  • Jang JS, Gulley N (1995) Fuzzy logic toolbox. The MathWorks Inc, Natick

  • Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vision 1:321–331

    Google Scholar 

  • Kerre EE, Nachtegael M (2000) Fuzzy techniques in image processing. Springer, Heidelberg

    Book  MATH  Google Scholar 

  • Khademi A, Sahba F, Venetsanopoulos A, Krishnan S (2009) Region, lesion and border-based multiresolution analysis of mammogram lesions. In: International conference on image analysis and recognition, Halifax, pp 802–813

  • Krivanek A, Sonka M (1998) Ovarian ultrasound image analysis follicle segmentation. IEEE Trans Med Imaging 17(6):935–944

    Article  Google Scholar 

  • Mamdani EH, Assilian S (1975) An experiment in linguistic synthesis with a fuzzy logic controller. Int J Man Mach Stud 7:1–13

    Article  MATH  Google Scholar 

  • Maryruth LJ, Eramian MG (2007) Computer assisted detection of polycystic ovary morphology. Ultrasound Images 23(2):306–309

    Google Scholar 

  • Mehrotra P, Chakraborthy C, Ghoshdastidar B, Ghostidas S (2011) Automated ovarian follicle detection for polycystic ovarian syndrome. In: Proceedings of IEEE international conference on image information processing (ICIIP), 3–5 Nov 2011, pp 1–4

  • Nedeljkovic I (2004) Image classification based on fuzzy logic. In: The international archives of the photogrammetry. Remote sensing and spatial information sciences, vol 34, Part XXX

  • Palacios A, Gacto MJ, Alcala-Fdez J (2012) Mining fuzzy association rules from low quality data. Soft Comput 16(5):883–901

    Article  Google Scholar 

  • Potocnik B, Zazula D, Korze D (1997) Automated computer assisted detection of follicles in ultrasound images of ovary. J Med Syst 21(6):445–457

    Article  Google Scholar 

  • Potocnik B, Zazula D (2002) Automated analysis of sequence of ovarian ultrasound images. Part I: segmentation of single 2D images. Image Vis Comput 20(3):217–225

    Article  Google Scholar 

  • Potocnik B, Zazula D (2002) Automated analysis of sequence of ovarian ultrasound images. Part II: prediction based object recognition from a sequence of images. Image Vis Comput 20(3):227–235

    Article  Google Scholar 

  • Potocnik B, Viher B, Zazula D (1998) Computer assisted detection of ovarian follicles based on ultrasound images. In: Proceedings of a Szamitastechnika Orvosies Biological Alkalmazasai, Verzprem, Hungary, pp 24–34

  • Potocnik B, Zazula D (2002) The XUltra project-automated analysis of ovarian ultrasound images. In: Proceedings of the 15th IEEE symposium on computer based medical systems (CBMS’02), Maribor, Slovenia, 7–7 Jun. 2002, pp 262–267

  • Sarty GE, Liang W, Sonka M, Pierson RA (1998) Semiautomated segmentation of ovarian follicular ultrasound images using a knowledge based algorithm. Ultrasound Med Biol 24(1):27–42

    Google Scholar 

  • Sonka M, Halvic V, Boyale R (1994) Image processing, analysis and machine vision. Chapman and hall, London

    Google Scholar 

  • Takagi T (1985) Fuzzy identification of systems and its applications to modeling and control. IEEE Trans Syst Man Cybern 15:116–132

    Article  MATH  Google Scholar 

  • Tsoukalas L H, Uhrig RE (1997) Fuzzy and neural approaches in engineering. Wiley New York, p 587

  • Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes. In: IEEE Trans Syst Man Cybern 3(1)

  • Zadeh LA (1965) Fuzzy Sets. Inf Control 8:333–335

    Article  MathSciNet  Google Scholar 

  • Zadeh LA (1968) Fuzzy algorithms. Inf Control 12:94–102

    Article  MATH  MathSciNet  Google Scholar 

  • Zadeh LA (1984) Making computers think like people. IEEE Spectr 8:26–31

    Article  Google Scholar 

Download references

Acknowledgments

The authors are indebted to the referees for their helpful comments and suggestions. Further, the authors are also thankful to Mediscan Diagnostics Care, Gulbarga, for providing the ultrasound images of ovaries. We are indebted to Dr. Suchitra C. Durgi, Radiologist, Dr. Chetan Durgi, Radiologist and Dr. Suvarna M.Tegnoor, Gynecologist, Gulbarga, for helpful discussions.

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Correspondence to Jyothi R. Tegnoor.

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Communicated by A. Di Nola.

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Hiremath, P.S., Tegnoor, J.R. Fuzzy inference system for follicle detection in ultrasound images of ovaries. Soft Comput 18, 1353–1362 (2014). https://doi.org/10.1007/s00500-013-1148-x

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