Cellular outline segmentation using fractal estimators
Keywords:Image Processing, Segmentation, Imunofluorescence Microscopy Images, Fractal Sets
Segmentation in biological images is essential for the determination of biological parameters that allow the construction of models of several biological problems. This helps to establish clear relationships between those models and the parameter estimation, and for elaboration of key experiments that give support to biological theories. Segmentation is the process of qualitative or quantitative information extraction (shape, texture, physical and geometric properties, among others). These quantities are needed to compute the biological descriptors for further classification (v.g., cell counting, development stage assessment, and many others). This process is almost always supervised (i.e., human assisted), since the quality of the images that are produced with classic microscopy technologies have defects that in general disallow the application of unsupervised segmentation techniques. In this paper we investigate the use of the a local fractal dimension estimation as an image descriptor for microscopy images. This local descriptor appears to be robust enough to perform unsupervised or semisupervised segmentations, specifically in our study. We applied this technique on microscopy images of amphibian embryos' skin in which, using immunofluorescence techniques, we have labeled the cell adhesion molecule E-Cadherin. This molecule is one of the key factors of the Ca2+- dependent cell-cell adhesion. Segmentation of the cellular outlines was performed using a processing workflow, which can be repeatedly applied to a set of similar images, from which information is extracted for characterization and eventual quantification purposes.
 C. Delrieux and R. Katz. Hybrid image recognition architecture. In SPIE’s 16th Annual International Symposium on Aerospace/Defense Sensing, Simulation, and Controls, Florida USA, 2002. SPIE-The International Society for Optical Engineering.
 C. Delrieux and R. Katz. Image Segmentation Through Automatic Fractal Dimension Classification. In Argentine Symposium on Computing Technology, Buenos Aires, 2003. 32 JAIIO, Jornadas Argentinas de Informática e Investigación Operativa,.
 Rafael González and Richard Woods. Digital Image Processing. Addison-Wesley, Wilmington, USA, 1996.
 K.L. Gosner. A Simplified Table for Staging Anuran Embryos and Larvae. Herpetologica, 16:183–190, 1960.
 R. Katz and C. Delrieux. Boundary Extraction Through Gradient-Based Evolutionary Algorithm. Journal of Computer Science and Technology, 3:7–12, 2003.
 Adams C. L., Chen Y. T., Smith S. J., and Nelson W. J. Mechanisms of Epithelial Cell-Cell Adhesion and Cell Compaction Revealed by High-resolution Tracking of E-Cadherin-Green Fluorescent Protein. The Journal of Cell Biology, 142(4), 1998.
 B. Mandelbrot and J. van Ness. Fractional Brownian Motion, fractional noises and applications. SIAM Review, 10(4):422–437, 1968.
 J. Park and J. M. Keller. Snakes on the Watershed. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(10):1201–1205, 2001.
 H.-O. Peitgen and D. Saupe. The Science of Fractal Images. Springer-Verlag, New York, 1986.
 J. C. Russ. The Image Processing Handbook. CRC Press, Boca Raton, FL, 1989.
 C. Xu and J. L. Prince. Snakes, Shapes, and Gradient Vector Flow, IEEE Transactions on Image Processing, 28(3):359–369, 1998