Markovian analysis of cervical cell images.

Markovian analysis is a method to measure optical texture based on gray-level transition probabilities in digitized images. Experiments are described that investigate that classification performance of parameters generated by Markovian analysis. Results using Markov texture parameters show that the selection of a Markov step size strongly affects classification error rates and the number of parameters required to achieve the maximum correct classification rates. Markov texture parameters are shown to achieve high rates of correct classification in discriminating images of normal from abnormal cervical cell nuclei.

[1]  E. Parzen,et al.  Modern Probability Theory and Its Applications , 1960 .

[2]  Kendall Preston,et al.  ADVANTAGES OF TOPOLOGY AS A BASIS FOR AUTOMATIC ANALYSIS OF BLOOD CELL IMAGES * , 1969 .

[3]  P. Bartels,et al.  Cell recognition from line scan transition probability profiles. , 1969, Acta cytologica.

[4]  A. Rosenfeld,et al.  Visual texture analysis , 1970 .

[5]  J. K. Hawkins.  Image Processing Principles and Techniques , 1970 .

[6]  Robert M. Haralick,et al.  Using radar imagery for crop discrimination: a statistical and conditional probability study , 1970 .

[7]  George G. Lendaris,et al.  Diffraction-pattern sampling for automatic pattern recognition , 1970 .

[8]  D. W. Flower,et al.  A note on the automatic generation and recognition of textures , 1971 .

[9]  Ernest L. Hall,et al.  A Survey of Preprocessing and Feature Extraction Techniques for Radiographic Images , 1971, IEEE Transactions on Computers.

[10]  H. C. Andrews.  Orthogonal transforms and feature selection in pattern recognition , 1971 .

[11]  John W. Woods,et al.  Two-dimensional discrete Markovian fields , 1972, IEEE Trans. Inf. Theory.

[12]  Robert S. Ledley.  Texture problems in biomedical pattern recognition , 1972, CDC 1972.

[13]  Ernest L. Hall,et al.  Texture Measures for Automatic Classification of Pulmonary Disease , 1972, IEEE Transactions on Computers.

[14]  Kenneth L. Caspari,et al.  Computer techniques in image processing , 1972 .

[15]  Earl E. Gose,et al.  Leukocyte Pattern Recognition , 1972, IEEE Trans. Syst. Man Cybern..

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

[17]  AUTOMATED DETECTION OF ABNORMAL LUNG FIELDS FROM THE ROUTINE P-A CHEST RADIOGRAPH. , 1973 .

[18]  Brian H. Mayall.  Digital image processing at Lawrence Livermore Laboratory part II — Biomedical applications , 1974, Computer.

[19]  D. L. Vickers,et al.  Pseudo-gray-level images via TMDS and RJET , 1974 .

[20]  H. Ney,et al.  Local Features for Image Classification , .