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SEGMENTATION OF OVARIAN ULTRASOUND IMAGES USING CELLULAR NEURAL NETWORKS.

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Title: SEGMENTATION OF OVARIAN ULTRASOUND IMAGES USING CELLULAR NEURAL NETWORKS.
Authors: Cigale, Boris1 boris.cigale@uni-mb.si, Zazula, Damjan1
Source: International Journal of Pattern Recognition & Artificial Intelligence. Jun2004, Vol. 18 Issue 4, p563-581. 19p.
Subjects: Medical imaging systems, Biological neural networks, Artificial intelligence, Neural computers, Ovaries, Cognitive neuroscience
Abstract: Segmentation of ovarian ultrasound images using cellular neural networks (CNNs) is studied in this paper. The segmentation method consists of five successive steps where the first four uses CNNs. In the first step, only rough position of follicles is determined. In the second step, the results are improved by expansion of detected follicles. In the third step, previously undetected inexpressive follicles are determined, while the fourth step detects the position of ovary. All results are joined in the fifth step. The templates for CNNs were obtained by applying genetic algorithm. The segmentation method has been tested on 50 ovarian ultrasound images. The recognition rate of follicles was around 60% and misidentification rate was around 30%. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 13533484
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  Data: SEGMENTATION OF OVARIAN ULTRASOUND IMAGES USING CELLULAR NEURAL NETWORKS.
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  Data: <searchLink fieldCode="AR" term="%22Cigale%2C+Boris%22">Cigale, Boris</searchLink><relatesTo>1</relatesTo><i> boris.cigale@uni-mb.si</i><br /><searchLink fieldCode="AR" term="%22Zazula%2C+Damjan%22">Zazula, Damjan</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Pattern+Recognition+%26+Artificial+Intelligence%22">International Journal of Pattern Recognition & Artificial Intelligence</searchLink>. Jun2004, Vol. 18 Issue 4, p563-581. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Medical+imaging+systems%22">Medical imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Biological+neural+networks%22">Biological neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+computers%22">Neural computers</searchLink><br /><searchLink fieldCode="DE" term="%22Ovaries%22">Ovaries</searchLink><br /><searchLink fieldCode="DE" term="%22Cognitive+neuroscience%22">Cognitive neuroscience</searchLink>
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  Label: Abstract
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  Data: Segmentation of ovarian ultrasound images using cellular neural networks (CNNs) is studied in this paper. The segmentation method consists of five successive steps where the first four uses CNNs. In the first step, only rough position of follicles is determined. In the second step, the results are improved by expansion of detected follicles. In the third step, previously undetected inexpressive follicles are determined, while the fourth step detects the position of ovary. All results are joined in the fifth step. The templates for CNNs were obtained by applying genetic algorithm. The segmentation method has been tested on 50 ovarian ultrasound images. The recognition rate of follicles was around 60% and misidentification rate was around 30%. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Pattern Recognition & Artificial Intelligence is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1142/S0218001404003368
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      – Code: eng
        Text: English
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        PageCount: 19
        StartPage: 563
    Subjects:
      – SubjectFull: Medical imaging systems
        Type: general
      – SubjectFull: Biological neural networks
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Neural computers
        Type: general
      – SubjectFull: Ovaries
        Type: general
      – SubjectFull: Cognitive neuroscience
        Type: general
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      – TitleFull: SEGMENTATION OF OVARIAN ULTRASOUND IMAGES USING CELLULAR NEURAL NETWORKS.
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              Text: Jun2004
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              Y: 2004
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            – TitleFull: International Journal of Pattern Recognition & Artificial Intelligence
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