Detecting gallbladders in chicken livers using spectral analysis

Anders Jørgensen, Eigil Mølvig Jensen, Thomas B. Moeslund

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

This paper presents a method for detecting gallbladders attached to chicken livers using spectral imaging. Gallbladders can contaminate good livers, making them unfit for human consumption. A data set consisting of chicken livers with and without gallbladders, has been captured using 33 wavelengths within the visible spectrum. This work shows how to reduce the high number of wavelengths while maintaining a high accuracy. A classification tree has be trained to evaluate if a gallbladder is present and whether it is suitable for automatic removal, which could increase profits for the processing plants. As a preliminary study this shows good results with a classification accuracy of 91.7%.
Original languageEnglish
Title of host publicationProceedings of the Machine Vision of Animals and their Behaviour (MVAB)
EditorsT. Amaral, S. Matthews, T. Plötz, S. McKenna, R. Fisher
Number of pages8
PublisherBritish Machine Vision Association
Publication date10 Sep 2015
DOIs
Publication statusPublished - 10 Sep 2015
EventBritish Machine Vision Conference 2015: Machine Vision of Animals and their Behaviour - Swansea University, Swansea, United Kingdom
Duration: 7 Sep 201510 Sep 2015
Conference number: 26

Conference

ConferenceBritish Machine Vision Conference 2015
Number26
LocationSwansea University
CountryUnited Kingdom
CitySwansea
Period07/09/201510/09/2015

Fingerprint

Liver
Spectrum analysis
Wavelength
Profitability
Imaging techniques
Processing

Keywords

  • Chicken
  • Poultry
  • Gall
  • Spectral
  • hyperspectral imaging
  • image analysis

Cite this

Jørgensen, A., Mølvig Jensen, E., & Moeslund, T. B. (2015). Detecting gallbladders in chicken livers using spectral analysis. In T. Amaral, S. Matthews, T. Plötz, S. McKenna, & R. Fisher (Eds.), Proceedings of the Machine Vision of Animals and their Behaviour (MVAB) British Machine Vision Association. https://doi.org/10.5244/C.29.MVAB.2
Jørgensen, Anders ; Mølvig Jensen, Eigil ; Moeslund, Thomas B. / Detecting gallbladders in chicken livers using spectral analysis. Proceedings of the Machine Vision of Animals and their Behaviour (MVAB). editor / T. Amaral ; S. Matthews ; T. Plötz ; S. McKenna ; R. Fisher. British Machine Vision Association, 2015.
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Jørgensen, A, Mølvig Jensen, E & Moeslund, TB 2015, Detecting gallbladders in chicken livers using spectral analysis. in T Amaral, S Matthews, T Plötz, S McKenna & R Fisher (eds), Proceedings of the Machine Vision of Animals and their Behaviour (MVAB). British Machine Vision Association, British Machine Vision Conference 2015, Swansea, United Kingdom, 07/09/2015. https://doi.org/10.5244/C.29.MVAB.2

Detecting gallbladders in chicken livers using spectral analysis. / Jørgensen, Anders; Mølvig Jensen, Eigil; Moeslund, Thomas B.

Proceedings of the Machine Vision of Animals and their Behaviour (MVAB). ed. / T. Amaral; S. Matthews; T. Plötz; S. McKenna; R. Fisher. British Machine Vision Association, 2015.

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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AB - This paper presents a method for detecting gallbladders attached to chicken livers using spectral imaging. Gallbladders can contaminate good livers, making them unfit for human consumption. A data set consisting of chicken livers with and without gallbladders, has been captured using 33 wavelengths within the visible spectrum. This work shows how to reduce the high number of wavelengths while maintaining a high accuracy. A classification tree has be trained to evaluate if a gallbladder is present and whether it is suitable for automatic removal, which could increase profits for the processing plants. As a preliminary study this shows good results with a classification accuracy of 91.7%.

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Jørgensen A, Mølvig Jensen E, Moeslund TB. Detecting gallbladders in chicken livers using spectral analysis. In Amaral T, Matthews S, Plötz T, McKenna S, Fisher R, editors, Proceedings of the Machine Vision of Animals and their Behaviour (MVAB). British Machine Vision Association. 2015 https://doi.org/10.5244/C.29.MVAB.2