NDLI: Textural analysis of hyperspectral images for improving contaminant detection accuracy

Content Provider	Springer Nature Link
Author	Park, Bosoon Kise, Michio Windham, William R. Lawrence, Kurt C. Yoon, Seung C.
Copyright Year	2008
Abstract	Previous studies demonstrated a hyperspectral imaging system has a potential for poultry fecal contaminant detection by measuring reflectance intensity. The simple image ratio at 565 and 517 nm images with optimal thresholding was able to detect fecal contaminants on broiler carcasses with high accuracy. However, differentiating false positives from real contaminants, especially cecal feces were challenging. Further image processing such as textural analysis in the spatial domain was able to reduce false positive errors. In this study, textural analysis of hyperspectral images was conducted to improve detection accuracy by reducing false positives. Specifically, textural analysis with co-occurrence matrix of hyperspectral images performed well to identify “true” contamination. In addition, co-occurrence matrix textural features including average, variance, entropy, contrast, correlation, moment of poultry hyperspectral images were investigated for selecting optimal features to represent contamination. Image pre-processing with co-occurrence textural analysis, specifically mean of co-occurrence textural feature from the matrix (0° angle and distance equals to one) followed by image ratio was able to improve fecal detection accuracy without additional optical filters that increase cost for system hardware of multispectral imaging system for on-line application.
Starting Page	208
Ending Page	214
Page Count	7
File Format	PDF
ISSN	19327587
Journal	Sensing and Instrumentation for Food Quality and Safety
Volume Number	2
Issue Number	3
e-ISSN	19329954
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-06-18
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Imaging spectroscopy Machine vision Image processing Food safety Texture analysis Fecal contamination Poultry inspection Engineering Chemistry/Food Science Food Science
Content Type	Text
Resource Type	Article
Subject	Industrial and Manufacturing Engineering Safety, Risk, Reliability and Quality Chemical Engineering

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