Abstract
Uterine fibroid is the most predominant problem among women of child-bearing age where the secretion of estrogen hormone plays significant role. The presence of fibroid can cause severe pain, infertility, and repeated miscarriages. Since the detection of fibroid and treatment is the crucial factor on women health especially in pregnancy, ultrasound (US) imaging is the most common modality for detecting fibroids. Because of the presence of speckle noise, the segmentation of fibroid from an US image is the tedious process. The proposed methodology has been used for automating this task by morphological functions available in graphical vision assistant tool. The modified morphological image cleaning (MMIC) algorithm for filtering and Canny edge detector have been utilized for fibroid segmentation and binary image morphological approaches adopted for analyzing the fibroid. The proposed algorithm has been developed, implemented, and validated in LabVIEW vision assistant toolbox. The outcomes of the proposed method have been evaluated and appreciated by experienced gynecologists and found that the manual intervention is eliminated on the investigation of diseased.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Hassani N (1975) Ultrasonic evaluation of uterine fibroids -the sonic shadow sign. J Natl Med Assoc 67(4):307–311
Karamam M, Kutay M, Bozdagi G (1995) An adaptive speckle suppression filter for medical ultrasonic images. IEEE Trans Med, Imaging
Peters RA II (1995) A new algorithm for image noise reduction using mathematical morphology. IEEE Trans Image Process 4:554–568
Thangavel K, Manavalan R, Aroquiaraj IL (2009) Removal of speckle noise from ultrasound medical image based on special filters: comparative study. ICGST-GVIP J, 9(3):25–32, ISSN 1687-398X
Vanithamani R, Umamaheswari G (2010) Performance analysis of filters for speckle reduction in medical ultrasound images. Int J Comput Appl 12(6):0975–8887
Jeyalakshmi TR, Ramar K (2010) A modified method for speckle noise removal in ultrasound medical images. Int J Comput Electr Eng 2(1):1793–8163
Noble JA, Boukerroui D (2006) Ultrasound image segmentation: a survey. IEEE Trans Med Imaging 25:987–1010
Thakur A, Anand RS (2005) A local statistics based region growing segmentation method for ultrasound medical images. Int J Signal Process 1:141–146
Shan J, Cheng HD, Wang Y (2008) A novel automatic seed point selection algorithm for breast ultrasound images. In: 19th international conference on pattern recognition
Infantosi AFC, Luz LMS, Pereira WCA, Alvarenga AV (2008) Breast ultrasound segmentation using morphologic operators and a gaussian function constraint. In: Proceedings 20, www.springerlink.com © Springer, Berlin Heidelberg, pp 520–523
Nallaperumal K, Krishnaveni K, Varghese J, Saudia S, Annam S, Kumar P (2007) A novel multi-scale morphological watershed segmentation algorithm. JISE 1(2), GA, USA, ISSN:1934-5
Bleau A, Leon LJ (2000) Watershed-based segmentation and region merging. Comput Vision Image Underst 77:317–370
Levner I, Zhang H (2007) Classification-driven watershed segmentation. IEEE Trans Image Process 16:1437–1445
Vincent L (1992) Morphological area openings and closings for grayscale image. In: Proceedings of NATO shape in picture workshop, Driebergen, The Netherlands, pp 197–208
Yang X, Ding M, Lou L, Yuchi M, Qiu W, Sun Y (2011) Common carotid artery lumen segmentation in B-mode ultrasound transverse view images. I J Image Graphics Signal Process 5:15–21
Hiremath PS, Tegnoor JR (2010) Automatic detection of follicles in ultrasound images of ovaries using edge based method. IJCA special issue on “recent trends in image processing and pattern recognition” RTIPPR
Jeyalakshmi TR, Ramar K (2009) Segmentation of uterine fibroid using morphology: an automatic approach. 978-1-4244-4711-4/09/$25.00 ©2009 IEEE
Jeyalakshmi R, Kadarkarai R (2010) Segmentation and feature extraction of fluid- filled uterine fibroid—a knowledge-based approach. Maejo Int J Sci Technol 4(03):405–416
Gonzalez RC, Woods RE (2008) Digital image processing. 3rd edn Pearson Education Inc., Publishing as Prentice Hall
Acknowledgments
We would like to sincerely thank, Dr. N. Kalpana, Hari Scan Center, Erode, for providing the US images and validation of proposed work. Karunya University, Dr. N.G.P Institute of Technology, Coimbatore, and Kovai Medical Center Hospitals are also acknowledged for their facilitation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Prabakar, S., Porkumaran, K., Guna Sundari, J. (2014). Uterine Fibroid Segmentation and Measurement Based on Morphological Functions in Graphical Vision Assistant Tool. In: Sridhar, V., Sheshadri, H., Padma, M. (eds) Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, vol 248. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1157-0_36
Download citation
DOI: https://doi.org/10.1007/978-81-322-1157-0_36
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1156-3
Online ISBN: 978-81-322-1157-0
eBook Packages: EngineeringEngineering (R0)