Optimization of Fecal Detection Using Hyperspectral Imaging and Kernel Density Estimation

This article addresses the development of an iterative search algorithm to find an optimal threshold to detect surface contaminants on poultry carcasses for a real-time multispectral imaging application. Previous studies showed that a band-ratio algorithm with a 517 nm band and a 565 nm band could detect contaminants on the surface of poultry carcasses. In this study, thresholding for the band-ratio algorithm was optimized in a statistical sense. A fundamental problem of the thresholding is that there is a theoretical performance bound from the standpoint of statistical hypothesis testing. In a Neyman-Pearson (NP) framework, a lower bound of detection accuracy can be determined for minimizing false positives. An iterative search algorithm was designed to obtain an optimal threshold in the NP framework. For the design of the search algorithm, statistical density distributions of fecal and non-fecal image data were estimated by kernel density estimation, and characterized by edge models on a projection axis perpendicular to a linear decision boundary. Three necessary criteria were investigated for the selection of the optimum threshold of the band-ratio algorithm. Numerical simulations with hyperspectral poultry images showed that the optimum threshold was 1.05.

[1]  Lei Tian,et al.  NARROW-BAND AND DERIVATIVE-BASED VEGETATION INDICES FOR HYPERSPECTRAL DATA , 2004 .

[2]  Dimitris G. Manolakis,et al.  Detection algorithms for hyperspectral imaging applications , 2002, IEEE Signal Process. Mag..

[3]  David A. Landgrebe,et al.  Signal Theory Methods in Multispectral Remote Sensing , 2003 .

[4]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[5]  W. R. Windham,et al.  CALIBRATION OF A PUSHBROOM HYPERSPECTRAL IMAGING SYSTEM FOR AGRICULTURAL INSPECTION , 2003 .

[6]  Kurt C. Lawrence,et al.  MULTISPECTRAL IMAGING SYSTEM FOR FECAL AND INGESTA DETECTION ON POULTRY CARCASSES , 2004 .

[7]  S. Kay.  Fundamentals of statistical signal processing: estimation theory , 1993 .

[8]  B. Ripley.  Modelling Spatial Patterns , 1977 .

[9]  David G. Stork,et al.  Pattern Classification , 1973 .

[10]  Kurt C. Lawrence,et al.  ALGORITHM DEVELOPMENT WITH VISIBLE/NEAR-INFRARED SPECTRA FOR DETECTION OF POULTRY FECES AND INGESTA , 2003 .

[11]  Shufeng Han,et al.  DYNAMIC CALIBRATION AND IMAGE SEGMENTATION METHODS FOR MULTISPECTRAL IMAGING CROP NITROGEN DEFICIENCY SENSORS , 2005 .

[12]  Andrew R. Webb,et al.  Statistical Pattern Recognition , 1999 .

[13]  H. R. Duke,et al.  Remote Sensing of Plant Nitrogen Status in Corn , 1996 .

[14]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[15]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[16]  David C. Slaughter,et al.  FEASIBILITY OF DETECTING SOIL NITRATE CONTENT USING A MID–INFRARED TECHNIQUE , 2001 .

[17]  Xiao-Ping Zhang,et al.  Segmentation of bright targets using wavelets and adaptive thresholding , 2001, IEEE Trans. Image Process..

[18]  Moon S. Kim,et al.  Automated detection of fecal contamination of apples based on multispectral fluorescence image fusion , 2005 .

[19]  Y. R. Chen,et al.  Multispectral detection of fecal contamination on apples based on hyperspectral imagery: Part II. Application of hyperspectral fluorescence imaging , 2002 .

[20]  W. R. Windham,et al.  Hyperspectral Imaging for Detecting Fecal and Ingesta Contaminants on Poultry Carcasses , 2002 .

[21]  Kurt C. Lawrence,et al.  Improved imaging system for fecal detection , 2004, SPIE Optics East.

[22]  Roberto Oberti,et al.  OPTICAL TECHNIQUES TO ESTIMATE THE RIPENESS OF RED-PIGMENTED FRUITS , 2004 .

[23]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[24]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[25]  Kurt C. Lawrence,et al.  VISIBLE/NIR SPECTROSCOPY FOR CHARACTERIZING FECAL CONTAMINATION OF CHICKEN CARCASSES , 2003 .