Cell recognition using wavelet templates

Abstract

The paper describes an algorithm to count and classify cells of different geometrical shapes on a given image. The algorithm assumes that it is known a priori the type of geometries to be recognized and it allows for many different geometrical shapes to appear in the same image with different sizes, locations and orientations. The algorithm combines classical tools, mainly the two dimensional Fourier transform, with newly developed tools for edge enhancements as well as the main technical contribution of the present paper, which consists in the definition of an over-complete set of spanning functions. These functions are constructed from geometrical templates of size comparable to the image cells; moreover, the resulting functions are scaled and rotated to assure the recognition of all image cells. We then describe an algorithm that decomposes the image in its most likely elements. The combination of ingredients used by the algorithm provides a cell recognition tool that is very robust, provides high resolution to discern among competing candidate cells and delivers practical computational efficiency.

A 2D/3D image analysis system to track fluorescently labeled structures in rod-shaped cells: application to measure spindle pole asymmetry during mitosis

“…For cell segmentation well-known models range from machine learning-like algorithms [11] to level set methods [12] and texture analysis [13]. Recent developments in wavelet theory have also contributed to the topic of cell segmentation [14,15] as well as to the research in object tracking [16]. Other popular models include graph cuts [17] or approaches based on probabilities [18].…”