An Interactive Level Set Approach to Semi-automatic Detection of Features in Food Micrographs

Microscopy is often employed in food research to inspect the microstructural features of food samples. Accurate detection of microscopic features is required for reliable quantitative analysis. We propose a user-assisted approach that can be easily integrated into a graphical interface. The proposed algorithm is based on a fast approximation of the common region-based level set equation, providing interactive computations. Experiments have been run on cheese micrographs acquired with electron and confocal microscopes.

[1]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[2]  Aly A. Farag,et al.  A fast level set algorithm for shape-based segmentation with multiple selective priors , 2008, 2008 15th IEEE International Conference on Image Processing.

[3]  José Miguel Aguilera,et al.  Microstructural principles of food processing and engineering , 1999 .

[4]  Allen R. Tannenbaum,et al.  Localizing Region-Based Active Contours , 2008, IEEE Transactions on Image Processing.

[5]  Bidyut Baran Chaudhuri,et al.  Elliptic fit of objects in two and three dimensions by moment of inertia optimization , 1991, Pattern Recognit. Lett..

[6]  Joachim Weickert,et al.  Scale-Space Theories in Computer Vision , 1999, Lecture Notes in Computer Science.

[7]  Ronald Fedkiw,et al.  Level set methods and dynamic implicit surfaces , 2002, Applied mathematical sciences.

[8]  Guillermo Sapiro,et al.  Geodesic Active Contours , 1995, International Journal of Computer Vision.

[9]  Tim W. Nattkemper,et al.  Automatic segmentation of digital micrographs: A survey , 2004, MedInfo.

[10]  Tony F. Chan,et al.  An Active Contour Model without Edges , 1999, Scale-Space.

[11]  G. Impoco,et al.  Quantitative Analysis of Cheese Microstructure using SEM Imagery , 2007 .

[12]  David W. Stanley,et al.  Image Analysis of Food Microstructure , 2005 .