**5. Conclusion**

**Method Data set used**

**Table 12.** Experimental results for classification accuracy of the SVM based method for the three classes of ovaries in

Active contour with fuzzy classification D1 98.18%

Edge based method with Gaussian lowpass filter

comparison with the fuzzy based method [section 4(i)]

Optimal thresholding method with Sobel operator

Edge based method with contourlet transform method

Active contour with 3σ interval based method

Watershed segmentation method

**Ovary type**

Normal ovary

> Cystic ovary

Polycystic ovary

**Number of images**

192 Advancements and Breakthroughs in Ultrasound Imaging

**Number of correctly classified images**

**SVM Fuzzy**

\* Type I Error: Regions are not follicles, but they are recognized as follicles. \*\*Type II Error: Regions are follicles, but they are not recognized as follicles.

**[4(i)]**

**Manual detection by expert**

15 15 15 15 0 0 0 0

10 10 10 10 0 0 0 0

10 10 10 10 0 0 0 0

**Type I error (FAR)**

**SVM Fuzzy**

**[4(i)]**

HRGMF based thresholding method

HVST based method

**Classification accuracy**

**Type II error (FRR)**

**SVM Fuzzy**

**[4(i)]**

D1 62.3% D2 45.2%

D1 49.55% D2 50.68%

D1 50.77% D2 61.82%

D1 79.47% D2 68.86%

D1 75.2% D2 59.95%

D1 90.10% D2 92.76%

D1 92.37% D2 96.66% The main significant research contributions that fulfill the objectives set in the present chapter are :


The performance comparison of the various methods proposed in the present study and other methods in the literature is summarized in the Table 13. It is observed that :


These research contributions are expected to be useful for the design and development of the software tool to support the medical experts, namely, Gynecologists and Radiologists, in their effort for ovarian image analysis and to implement the same in automated diagnostic systems. Further, these research contributions serve as the basis for design of automatic systems for the detection of the follicles inside the ovary under the examination during the entire female cycle and to study the ovarian morphology and, thus, help the medical experts for monitoring follicles and identifying the ovarian type during the course of infertility treatment of patients. The burden of the experts is significantly reduced in their everyday routine, without sacrificing the accuracy of diagnosis and prognosis.

[2] Gougeon A., Adashi E. and Leung P. Dynamics of Human Follicular Growth, a Mor‐ phologic Perspective: Comprehensive Endocrinology, The Ovary, Raven Press, New

Follicle Detection and Ovarian Classification in Digital Ultrasound Images of Ovaries

http://dx.doi.org/10.5772/56518

195

[3] Pellicer A., Gaitán P., Neuspiller F., Ardiles G., Albert C., Remohí J. and Simón C., Ovarian Follicular Dynamics: From Basic Science to Clinical Practice, Journal of Re‐

[4] Hanna M. D., Chizen D. R. and Pierson R. Characteristics of Follicular Evacuation During Human Ovulation, J Ultrasound Obstet Gynaecol 1994; 4(6) 488-493.

[5] Pierson R. and Chizen D. Ultrasonography of Normal and Aberrant Ovulation.

[6] Bal A. and Mohan H. Malignant Transformation in Mature Cystic Teratoma of the Ovary: Report of Five Cases and Review of the Literature, Arch Gynecol Obstet,

[7] Yamanaka Y. and Tateiwa Y. Preoperative Diagnosis of Malignant Transformation in Mature Cystic Teratoma of The Ovary, Eur J Gynaecol Oncol 2005; 26(4)391-392.

[8] Battaglia C., Artini P. G., Genazzani A. D., Gremigni R., Slavatori M. R. and Sgherzi M. R., Color Doppler Analysis in Oligo and Amenorrheic Women with Polycystic

[9] Balen Adam H., Joop S. E. Laven, Seang-Lin Tan and Didier Dewailly, Ultrasound Assessment of the Polycystic Ovary: International Consensus Definitions, Human

[10] Pache T. D., Wladimiroff J. W., Hop W. C. and Fauser B. C. How to Discriminate Be‐ tween Normal and Polycystic Ovaries: Transvaginal US Study. Radiology 1992;183(2)

[11] Kelsey T.W. and Wallace W. H. B. Ovarian Volume Correlates Strongly with Number of Non Growing Follicles in the Human Ovary, Obstretics and Gynocology Interna‐

[12] Potocnik B., Zazula D., Korze D. Automated Computer-Assisted Detection of Folli‐ cles in Ultrasound Images of Ovary. Journal of Medical Systems 1997; 21(6) 445-457.

[13] Potocnik B., Viher B. and Zazula D. Computer assisted detection of ovarian follicles based on ultrasound images, Proc. of a szamitastechnika orvosies biological alkalma‐

[14] Potocnik B. and Zazula D. Aktivne krivulje-nadgradnja razpoznavalnih sistemov (Active contours-an extension of recognition systems). Proceedings of 7th Electrotech‐ nical and Computer Science Conference ERK 98, Portoroz, Slovenia, Vol. B, 1998;

Imaging in Infertility and Reproductive Endocrinology, 1994;155-166.

Ovary Syndrome. Gynecolog Endocrinto, 1997;11:105-110.

York, 1993: 21-39.

2007; 275(3)179-182.

421-423.

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tional, 2012; Article-id 305025.

zasai, Verzprem, Hungary, pp. 24-34; 1998.

productive Immunology 1998; 39(1-2) 29-61.

The future research in this direction would be to take up the analysis of ovarian images captured at regular intervals for various subjects undergoing drug susceptibility tests. Further, the image processing methods could be developed in the detection of ovarian cancer stages.
