کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
2450703 | 1554097 | 2008 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Development of a hybrid image processing algorithm for automatic evaluation of intramuscular fat content in beef M. longissimus dorsi
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
دانش تغذیه
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
An automatic method for estimating the content of intramuscular fat (IMF) in beef M. longissimus dorsi (LD) was developed using a sequence of image processing algorithm. To extract IMF particles within the LD muscle from structural features of intermuscular fat surrounding the muscle, three steps of image processing algorithm were developed, i.e. bilateral filter for noise removal, kernel fuzzy c-means clustering (KFCM) for segmentation, and vector confidence connected and flood fill for IMF extraction. The technique of bilateral filtering was firstly applied to reduce the noise and enhance the contrast of the beef image. KFCM was then used to segment the filtered beef image into lean, fat, and background. The IMF was finally extracted from the original beef image by using the techniques of vector confidence connected and flood filling. The performance of the algorithm developed was verified by correlation analysis between the IMF characteristics and the percentage of chemically extractable IMF content (PÂ <Â 0.05). Five IMF features are very significantly correlated with the fat content (PÂ <Â 0.001), including count densities of middle (CDMiddle) and large (CDLarge) fat particles, area densities of middle and large fat particles, and total fat area per unit LD area. The highest coefficient is 0.852 for CDLarge.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Meat Science - Volume 80, Issue 4, December 2008, Pages 1231-1237
Journal: Meat Science - Volume 80, Issue 4, December 2008, Pages 1231-1237
نویسندگان
Cheng-Jin Du, Da-Wen Sun, Patrick Jackman, Paul Allen,