A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Statistical texture characterization from discrete wavelet representations
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
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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, multiresolution wavelet analysis, and non-parametric estimation of the density functions of the wavelet coefficients from the decomposition. Additionally, to deal with small size textures where the largest inscribed rectangular window may 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 combined with non-parametric density models yields better classification for cases where parametric density modeling of wavelet coefficients may not applicable.