Wavelets as chromatin texture descriptors for the automated identification of neoplastic nuclei

Chromatin distribution reflects the organization of the DNA of a nucleus and contains important cellular diagnostic and prognostic information. Feulgen staining of breast tissue enables the chromatin distribution of the nucleus to be visualized in the form of texture. Describing texture in an objective and quantitative way by means of a set of texture parameters, combined with the study of the relationship of such parameters to the pathobiological cell properties, is useful both for reduction of the subjectivity inherently coupled to visual observation and for more accurate prognosis or diagnosis.

[1]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[2]  B Palcic,et al.  Quantitative evaluation of malignant potential of early breast cancer using high resolution image cytometry , 1993, Journal of cellular biochemistry. Supplement.

[3]  Paul Scheunders,et al.  A Texture Analysis Approach to Corrosion Image Classification , 1996 .

[4]  E. J. Breen,et al.  Regression methods for automated colour image classification and thresholding , 1994 .

[5]  J. Barba,et al.  Nuclear diffuseness as a measure of texture: Definition and application to the computer‐assisted diagnosis of parathyroid adenoma and carcinoma , 1994, Journal of microscopy.

[6]  Paul Scheunders,et al.  Wavelet correlation signatures for color texture characterization , 1999, Pattern Recognit..

[7]  Josef Kittler,et al.  Floating search methods in feature selection , 1994, Pattern Recognit. Lett..

[8]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Anil K. Jain,et al.  Feature Selection: Evaluation, Application, and Small Sample Performance , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  J. Epstein,et al.  Comparison of DNA ploidy and nuclear size, shape and chromatin irregularity in tissue sections and smears of prostatic carcinoma. , 1990, Analytical and quantitative cytology and histology.

[11]  N. J. Pressman Markovian analysis of cervical cell images. , 1976, The journal of histochemistry and cytochemistry : official journal of the Histochemistry Society.

[12]  D Seigneurin,et al.  The value of DNA image cytometry for the cytological diagnosis of well-differentiated breast carcinomas and benign lesions. , 1994, Analytical cellular pathology : the journal of the European Society for Analytical Cellular Pathology.

[13]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[14]  W. N. Street,et al.  Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates. , 1994, Cancer letters.

[15]  L P Clarke,et al.  Tree-structured non-linear filter and wavelet transform for microcalcification segmentation in digital mammography. , 1994, Cancer letters.

[16]  M Petein,et al.  Computer‐assisted chromatin texture characterization of Feulgen‐stained nuclei in a series of 331 transitional bladder cell carcinomas , 1994, The Journal of pathology.

[17]  Stéphane Mallat,et al.  Characterization of Signals from Multiscale Edges , 2011, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  S Marcelja,et al.  Mathematical description of the responses of simple cortical cells. , 1980, Journal of the Optical Society of America.

[19]  H Guski,et al.  Chromatin structure analysis based on a hierarchic texture model. , 1995, Analytical and quantitative cytology and histology.

[20]  J. M. Hans du Buf,et al.  A review of recent texture segmentation and feature extraction techniques , 1993 .

[21]  Josef Kittler,et al.  Pattern recognition : a statistical approach , 1982 .

[22]  H. Bloom,et al.  Histological Grading and Prognosis in Breast Cancer , 1957, British Journal of Cancer.

[23]  André Gagalowicz,et al.  A New Method for Texture Fields Synthesis: Some Applications to the Study of Human Vision , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  C.-C. Jay Kuo,et al.  Texture analysis and classification with tree-structured wavelet transform , 1993, IEEE Trans. Image Process..

[25]  Peter E. Hart,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[26]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[27]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[28]  G. MallatS. A Theory for Multiresolution Signal Decomposition , 1989 .

[29]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[30]  Paul Scheunders,et al.  Wavelet-FILVQ classifier for speech analysis , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[31]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[32]  A F Laine,et al.  A wavelet-based metric for visual texture discrimination with applications in evolutionary ecology. , 1995, Mathematical biosciences.