Abstract
The analysis of intra-muscular fat (IMF) information in dry-cured ham is a very important step in determining its quality and its final target market. This paper presents a fully automatic method for analyzing the IMF content in slices of dry-cured ham. After pre-processing, the approach obtains an initial IMF segmentation using Bias-corrected Fuzzy C-means (BCFCM) segmentation overcoming the inhomogeneous intensity distribution of ham slices. Subsequently, a volumetric IMF estimation model is proposed based on the distance transform of the segmented slices. Finally, a rule-based labelling is used for grading the fat content in order to assess the importance of the features for IMF estimation. Results obtained in a set of 60 slices show a good correlation (0.92) with a ground truth given by standard but more expensive and time consuming techniques, such as the FoodScan analysis.
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Widiyanto, S., Cufí, X., Rubio, M., Muñoz, I., Fulladosa, E., Martí, R. (2013). Automatic Intra Muscular Fat Analysis on Dry-Cured Ham Slices. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_103
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DOI: https://doi.org/10.1007/978-3-642-38628-2_103
Publisher Name: Springer, Berlin, Heidelberg
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