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Medical & Biological Engineering & Computing

Erschienen in:

01.04.2013 | Original Article

Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns

verfasst von: Meng Dong, Mark G. Eramian, Simone A. Ludwig, Roger A. Pierson

Erschienen in: Medical & Biological Engineering & Computing | Ausgabe 4/2013

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Abstract

In this study, we propose a fully automatic algorithm to detect and segment corpora lutea (CL) using genetic programming and rotationally invariant local binary patterns. Detection and segmentation experiments were conducted and evaluated on 30 images containing a CL and 30 images with no CL. The detection algorithm correctly determined the presence or absence of a CL in 93.33 % of the images. The segmentation algorithm achieved a mean (±standard deviation) sensitivity and specificity of 0.8693 ± 0.1371 and 0.9136 ± 0.0503, respectively, over the 30 CL images. The mean root mean squared distance of the segmented boundary from the true boundary was 1.12 ± 0.463 mm and the mean maximum deviation (Hausdorff distance) was 3.39 ± 2.00 mm. The success of these algorithms demonstrates that similar algorithms designed for the analysis of in vivo human ovaries are likely viable.

Vorheriger Artikel Effects of design parameters of total hip components on the impingement angle and determination of the preferred liner skirt shape with an adequate oscillation angle

Nächster Artikel SEMG-based hand motion recognition using cumulative residual entropy and extreme learning machine

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Adams GP, Pierson RA (1995) Bovine model for study of ovarian follicular dynamics in humans. Theriogenology 43(1): 113–120CrossRef

Ahmed W, Eramian MG (2010) Automated detection of grayscale bar and distance scale in ultrasound images. In Dawant BM, Haynor DR (eds) Medical Imaging 2010: image processing, proceedings of SPIE, vol 7623, pp 1–11

Baerwald AR, Adams GP, Pierson RA (2003) A new model for ovarian follicular development during the human menstrual cycle. Fertil Steril 80(1): 116–122PubMedCrossRef

Baerwald AR, Adams GP, Pierson RA (2005) Form and function of the corpus luteum during the human menstrual cycle. Ultrasound Obstet Gynecol 25: 498–507PubMedCrossRef

Brodatz P (1966) Textures: a photographic album for artists and designers. Dover Publications, New York

Iakovidis DK, Keramidas EG, Maroulis D (2008) Fuzzy local binary patterns for ultrasound texture characterization. In: Proceedings of the 5th international conference on image analysis and recognition, pp 750–759

Koza JR (1992) Genetic programming: on the programming of the computers by means of natural selection. MIT Press, Cambridge

Koza JR (1994) Genetic programming II: automatic discovery of reusable programs. MIT Press, Cambridge

Lategahn H, Gross S, Stehle T, Aach T (2010) Texture classification by modeling joint distributions of local patterns with gaussian mixtures. IEEE Trans Image Process 19(6):1548–1557PubMedCrossRef

10.

Lawrence MJ, Eramian MG, Pierson RA, Neufeld E (2007) Computer assisted detection of polycystic ovarian morphology in ultrasound images. In: Proceedings of the 4th Canadian conference on computer and robot vision. IEEE Computer Society (56/103 submissions accepted), pp 105–112.

11.

Miller BL, Goldberg DE (1995) Genetic algorithms, tournament selection, and the effects of noise. Technical report, University of Illinois, Urbana-Champaign

12.

Negnevitsky M (2005) Artificial intelligence. Addison-Wesley, Menlo Park

13.

Neill JD (ed) (2005) Knobil and Neill’s physiology of reproduction. Academic Press, New York

14.

Ojala T, Pietikainen M, Harwood D (1996) A comparative study of texture measures with classification based on feature distributions. Pattern Recognit 29(1):51–59CrossRef

15.

Ojala T, Pietikainen M, Maenpaa T (2000) Gray scale and rotation invariant texture classification with local binary patterns. In: Proceedings of the 6th European conference on computer vision—part I, pp 404–420. Springer, Berlin

16.

Rusnell BJ, Pierson RA, Singh J, Adams GP, Eramian MG (2008) Level set segmentation of bovine corpora lutea in ex situ ovarian ultrasound images. Reprod Biol Endocrinol 6:33PubMedCrossRef

17.

Singh J, Pierson RA, Adams GP (1998) Ultrasound image attributes of bovine ovarian follicles: endocrine and functional correlates. J Reprod Fertil 112:19–29PubMedCrossRef

18.

Singh J, Roger A. Pierson, Gregg P. Adams (1997) Ultrasound image attributes of the bovine corpus luteum: structural and functional correlates. J Reprod Fertil 109(1):35–44PubMedCrossRef

19.

Song A, Ciesielski V (2008) Texture segmentation by genetic programming. Evol Comput 16(4):461–481PubMedCrossRef

Titel: Automatic detection and segmentation of bovine corpora lutea in ultrasonographic ovarian images using genetic programming and rotation invariant local binary patterns
verfasst von: Meng Dong
Mark G. Eramian
Simone A. Ludwig
Roger A. Pierson
Publikationsdatum: 01.04.2013
Verlag: Springer-Verlag
Erschienen in: Medical & Biological Engineering & Computing / Ausgabe 4/2013
Print ISSN: 0140-0118
Elektronische ISSN: 1741-0444
DOI: https://doi.org/10.1007/s11517-012-1009-2

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