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Biological shape characterization for automatic image recognition and diagnosis of protozoan parasites of the genus Eimeria

Authors:

César A. B. Castañón,

Sandra Fernandez,

Luciano da F. CostaAuthors Info & Claims

Pattern Recognition, Volume 40, Issue 7

Pages 1899 - 1910

https://doi.org/10.1016/j.patcog.2006.12.006

Published: 01 July 2007 Publication History

Abstract

We describe an approach of automatic feature extraction for shape characterization of seven distinct species of Eimeria, a protozoan parasite of domestic fowl. We used digital images of oocysts, a round-shaped stage presenting inter-specific variability. Three groups of features were used: curvature characterization, size and symmetry, and internal structure quantification. Species discrimination was performed with a Bayesian classifier using Gaussian distribution. A database comprising 3891 micrographs was constructed and samples of each species were employed for the training process. The classifier presented an overall correct classification of 85.75%. Finally, we implemented a real-time diagnostic tool through a web interface, providing a remote diagnosis front-end.

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Cited By

Pan YYe WXie DWang JWang HQiu H(2024)Outlier classification for microbiological open set recognitionComputers and Electronics in Agriculture10.1016/j.compag.2024.109104224:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.compag.2024.109104
Vázquez IAyasi BSeker HLuengo JSedano JGarcía-Vico A(2024)Combining traditional and spiking neural networks for energy-efficient detection of Eimeria parasitesApplied Soft Computing10.1016/j.asoc.2024.111681160:COnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111681
Ray KSaharia SSarma N(2024)Segmentation Approaches of Parasite Eggs in Microscopic Images: A SurveySN Computer Science10.1007/s42979-024-02709-45:4Online publication date: 6-Apr-2024
https://dl.acm.org/doi/10.1007/s42979-024-02709-4
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Index Terms

Biological shape characterization for automatic image recognition and diagnosis of protozoan parasites of the genus Eimeria
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Object recognition
      2. Computer vision representations
        Shape representations
  2. Machine learning

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Published In

cover image Pattern Recognition

Pattern Recognition Volume 40, Issue 7

July, 2007

272 pages

ISSN:0031-3203

Issue’s Table of Contents

Copyright © Pattern Recognition Society © 2007.

Publisher

Elsevier Science Inc.

United States

Publication History

Published: 01 July 2007

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Cited By

Pan YYe WXie DWang JWang HQiu H(2024)Outlier classification for microbiological open set recognitionComputers and Electronics in Agriculture10.1016/j.compag.2024.109104224:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.compag.2024.109104
Vázquez IAyasi BSeker HLuengo JSedano JGarcía-Vico A(2024)Combining traditional and spiking neural networks for energy-efficient detection of Eimeria parasitesApplied Soft Computing10.1016/j.asoc.2024.111681160:COnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.asoc.2024.111681
Ray KSaharia SSarma N(2024)Segmentation Approaches of Parasite Eggs in Microscopic Images: A SurveySN Computer Science10.1007/s42979-024-02709-45:4Online publication date: 6-Apr-2024
https://dl.acm.org/doi/10.1007/s42979-024-02709-4
Yaokumah WAppati JOwusu EAmmah P(2022)Primary Mobile Image Analysis of Human Intestinal Worm DetectionInternational Journal of System Dynamics Applications10.4018/IJSDA.30263111:1(1-16)Online publication date: 3-Nov-2022
https://dl.acm.org/doi/10.4018/IJSDA.302631
Farfán AScabini LBruno O(2019)A Web-Based System to Assess Texture Analysis Methods and DatasetsComputer Analysis of Images and Patterns10.1007/978-3-030-29891-3_37(425-437)Online publication date: 3-Sep-2019
https://dl.acm.org/doi/10.1007/978-3-030-29891-3_37
Buyukyilmaz MCibikdiken AAbdalla MSeker H(2017)Identification of Chicken Eimeria Species from Microscopic Images by Using MLP Deep Learning AlgorithmProceedings of the International Conference on Video and Image Processing10.1145/3177404.3177445(84-88)Online publication date: 27-Dec-2017
https://dl.acm.org/doi/10.1145/3177404.3177445
Wang HFeng YSa YLu JDing JZhang JHu X(2017)Pattern recognition and classification of two cancer cell lines by diffraction imaging at multiple pixel distancesPattern Recognition10.1016/j.patcog.2016.07.03561:C(234-244)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1016/j.patcog.2016.07.035
Gelzinis AVerikas AVaiciukynas EBacauskiene MSulcius SSimoliunas EStaniulis JPaskauskas R(2015)Automatic detection and morphological delineation of bacteriophages in electron microscopy imagesComputers in Biology and Medicine10.1016/j.compbiomed.2015.06.01564:C(101-116)Online publication date: 1-Sep-2015
https://dl.acm.org/doi/10.1016/j.compbiomed.2015.06.015
Zhang JLin YLiu YLi ZLi ZHu SLiu ZLin DWu Z(2014)Cascaded-Automatic Segmentation for Schistosoma japonicum eggs in images of fecal samplesComputers in Biology and Medicine10.1016/j.compbiomed.2014.05.01252(18-27)Online publication date: 1-Sep-2014
https://dl.acm.org/doi/10.1016/j.compbiomed.2014.05.012
Falvo MFlorindo JBruno O(2011)A method to generate artificial 2D shape contour based in fourier transform and genetic algorithmsProceedings of the 13th international conference on Advanced concepts for intelligent vision systems10.5555/2034246.2034268(207-215)Online publication date: 22-Aug-2011
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