Automated Detection of Fish Bones in Salmon Fillets using X-ray Testing

Automated Detection of Fish Bones in Salmon Fillets using X-ray Testing. Mery, D., Lillo, I., Loebel, H., Riffo, V., Soto, A., Cipriano, A., & Aguilera, J. In Proc. of 4th Pacific-Rim Symposium on Image and Video Technology (PSIVT-2010), 2010.

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X-ray testing is playing an increasingly important role in food quality assurance. In the production of fish fillets, however, fish bone detection is performed by human operators using their sense of touch and vision which can lead to misclassification. In countries where fish is often consumed, fish bones are some of the most frequently ingested foreign bodies encountered in foods. Effective detection of fish bones in the quality control process would help avoid this problem. For this reason, we developed an X-ray machine vision approach to au- tomatically detect fish bones in fish fillets. This paper describes our approach and the corresponding validation experiments with salmon fillets. The approach consists of six steps: 1) A digital X-ray image is taken of the fish fillet being tested. 2) The X-ray image is filtered and enhanced to facilitate the detection of fish bones. 3) Potential fish bones in the image are segmented using band pass filtering, thresholding and morphological techniques. 4) Intensity features of the enhanced X-ray image are extracted from small detection windows that are defined in those regions where potential fish bones were segmented. 5) A classifier is used to discriminate between ‘bones’ and ‘no-bones’ classes in the detection windows. 6) Finally, fish bones in the X-ray image are isolated using morphological operations applied on the corresponding segments classified as ‘bones’. In the experiments we used a high resolution flat panel detector with the capacity to capture up to a 6 million pixel digital X-ray image. In the training phase, we analyzed 20 representative salmon fillets, 7700 detection windows (10×10 pixels) and 279 intensity features. Cross validation yielded a detection performance of 95% using a support vector machine classifier with only 24 selected features. We believe that the proposed approach opens new possibilities in the field of automated visual inspection of salmon and other similar fish.

@InProceedings{	  mery-psivt:etal:2010,
  author	= {D. Mery and I. Lillo and H. Loebel and V. Riffo and A.
		  Soto and A. Cipriano and JM. Aguilera},
  title		= {Automated Detection of Fish Bones in Salmon Fillets using
		  X-ray Testing},
  booktitle	= {Proc. of 4th Pacific-Rim Symposium on Image and Video
		  Technology (PSIVT-2010)},
  year		= {2010},
  abstract	= {X-ray testing is playing an increasingly important role in
		  food quality assurance. In the production of fish fillets,
		  however, fish bone detection is performed by human
		  operators using their sense of touch and vision which can
		  lead to misclassification. In countries where fish is often
		  consumed, fish bones are some of the most frequently
		  ingested foreign bodies encountered in foods. Effective
		  detection of fish bones in the quality control process
		  would help avoid this problem. For this reason, we
		  developed an X-ray machine vision approach to au-
		  tomatically detect fish bones in fish fillets. This paper
		  describes our approach and the corresponding validation
		  experiments with salmon fillets. The approach consists of
		  six steps: 1) A digital X-ray image is taken of the fish
		  fillet being tested. 2) The X-ray image is filtered and
		  enhanced to facilitate the detection of fish bones. 3)
		  Potential fish bones in the image are segmented using band
		  pass filtering, thresholding and morphological techniques.
		  4) Intensity features of the enhanced X-ray image are
		  extracted from small detection windows that are defined in
		  those regions where potential fish bones were segmented. 5)
		  A classifier is used to discriminate between ‘bones’
		  and ‘no-bones’ classes in the detection windows. 6)
		  Finally, fish bones in the X-ray image are isolated using
		  morphological operations applied on the corresponding
		  segments classified as ‘bones’. In the experiments we
		  used a high resolution flat panel detector with the
		  capacity to capture up to a 6 million pixel digital X-ray
		  image. In the training phase, we analyzed 20 representative
		  salmon fillets, 7700 detection windows (10×10 pixels) and
		  279 intensity features. Cross validation yielded a
		  detection performance of 95% using a support vector machine
		  classifier with only 24 selected features. We believe that
		  the proposed approach opens new possibilities in the field
		  of automated visual inspection of salmon and other similar
		  fish. },
  url		= {http://saturno.ing.puc.cl/media/papers_alvaro/PSIVT-2010.pdf}
}

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