We present nonparametric methods for segmenting and classifying stem cell nuclei so as to enable the automatic monitoring of stem cell growth and development. The approach is based on combining level set methods, multiresolutionwavelet analysis, and non-parametric estimation of the density functions of the wavelet coefficients from the decomposition. Additionally, to deal with small size textureswhere the largest inscribed rectangular windowmay not contain a sufficient number of pixels for multiresolution analysis, we propose an adjustable windowing method that enables the multiresolution analysis of elongated and irregularly shaped nuclei. We illustrate cases where the adjustable windowing approach combinedwith non-parametric density models yields better classification for cases where parametric densitymodeling of wavelet coefficients may not applicable.
['Nathan Lowry', 'Rami Mangoubi', 'Mukund Desai', 'Paul J. Sammak']
Nonparametric segmentation and classification of small size irregularly shaped stem cell nuclei using adjustable windowing