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Proceedings Paper

Neural analysis of bovine ovaries ultrasound images in the identification process of the corpus luteum
Author(s): K. Górna; B. M. Jaśkowski; P. Okoń; M. Czechlowski; K. Koszela; M. Zaborowicz; P. Idziaszek
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Paper Abstract

The aim of the paper is to shown the neural image analysis as a method useful for identifying the development stage of the domestic bovine corpus luteum on digital USG (UltraSonoGraphy) images. Corpus luteum (CL) is a transient endocrine gland that develops after ovulation from the follicle secretory cells. The aim of CL is the production of progesterone, which regulates many reproductive functions. In the presented studies, identification of the corpus luteum was carried out on the basis of information contained in ultrasound digital images. Development stage of the corpus luteum was considered in two aspects: just before and middle of domination phase and luteolysis and degradation phase. Prior to the classification, the ultrasound images have been processed using a GLCM (Gray Level Co-occurence Matrix). To generate a classification model, a Neural Networks module implemented in the STATISTICA was used. Five representative parameters describing the ultrasound image were used as learner variables. On the output of the artificial neural network was generated information about the development stage of the corpus luteum. Results of this study indicate that neural image analysis combined with GLCM texture analysis may be a useful tool for identifying the bovine corpus luteum in the context of its development phase. Best-generated artificial neural network model was the structure of MLP (Multi Layer Perceptron) 5:5-17-1:1.

Paper Details

Date Published: 21 July 2017
PDF: 6 pages
Proc. SPIE 10420, Ninth International Conference on Digital Image Processing (ICDIP 2017), 104204H (21 July 2017); doi: 10.1117/12.2281723
Show Author Affiliations
K. Górna, Poznan Univ. of Life Sciences (Poland)
B. M. Jaśkowski, Poznan Univ. of Life Sciences (Poland)
P. Okoń, Poznan Univ. of Life Sciences (Poland)
M. Czechlowski, Poznan Univ. of Life Sciences (Poland)
K. Koszela, Poznan Univ. of Life Sciences (Poland)
M. Zaborowicz, Poznan Univ. of Life Sciences (Poland)
P. Idziaszek, Poznan Univ. of Life Sciences (Poland)


Published in SPIE Proceedings Vol. 10420:
Ninth International Conference on Digital Image Processing (ICDIP 2017)
Charles M. Falco; Xudong Jiang, Editor(s)

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