Influence of feature set reduction on breast cancer malignancy classification of fine needle aspiration biopsies

Grading of breast cancer malignancy is a key step in its diagnosis, which in turn helps to determine its prognosis and a course of treatment. In this paper, we consider the application of pattern recognition and image processing techniques to perform computer-assisted automatic breast cancer malignancy grading from cytological slides of fine needle aspiration biopsies. To determine a classification of the malignancy of the slide, a feature set is first determined from imagery of the slides. In this paper we investigated the nature of a wide set of features extracted from biopsy images to determine their discriminatory power and cross-correlation. Feature vector reduction is studied using a correlation map of the features, determining discriminatory power using the Kolmogorov-Smirnov test, significant feature selection, and stepwise feature selection. The reduction of the feature vector simplifies the complexity of classification scheme and does not impair the classification accuracy. In some cases a decrease of the error rate is noted. Based on this analysis, we present an improved classification system for cancer malignancy grading.

[1]  Soo-Hong Kim,et al.  Analysis of breast cancer using data mining & statistical techniques , 2005, Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Network.

[2]  T. W. Ridler,et al.  Picture thresholding using an iterative selection method. , 1978 .

[3]  N. Theera-Umpon.  Patch-Based White Blood Cell Nucleus Segmentation Using Fuzzy Clustering , 2005 .

[4]  S Issac Niwas,et al.  Wavelet based feature extraction method for breast cancer cytology images , 2010, 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA).

[5]  Adam Krzyzak,et al.  Classification of Breast Cancer Malignancy Using Cytological Images of Fine Needle Aspiration Biopsies , 2008, Int. J. Appl. Math. Comput. Sci..

[6]  Hiroshi Motoda,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998, The Springer International Series in Engineering and Computer Science.

[7]  W. N. Street,et al.  Xcyt: a System for Remote Cytological Diagnosis and Prognosis of Breast Cancer , 2000 .

[8]  Robert P. W. Duin,et al.  Dimensionality reduction of image features using the canonical contextual correlation projection , 2005, Pattern Recognit..

[9]  Joel Quintanilla-Domínguez,et al.  WBCD breast cancer database classification applying artificial metaplasticity neural network , 2011, Expert Syst. Appl..

[10]  Paul L. Rosin,et al.  A Convexity Measurement for Polygons , 2002, BMVC.

[11]  Gregory W. Corder,et al.  Nonparametric Statistics : A Step-by-Step Approach , 2014 .

[12]  Hala H. Zayed,et al.  Remote Computer-Aided Breast Cancer Detection and Diagnosis System Based on Cytological Images , 2014, IEEE Systems Journal.

[13]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[14]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[15]  Scott E. Umbaugh,et al.  Digital image processing and analysis : human and computer vision applications with CVIPtools , 2011 .

[16]  Rached Tourki,et al.  Automated Breast Cancer Diagnosis Based on GVF-Snake Segmentation, Wavelet Features Extraction and Fuzzy Classification , 2009, J. Signal Process. Syst..

[17]  A. Nicholson,et al.  Prognostic value of cytological grading of fine-needle aspirates from breast carcinomas , 1994, The Lancet.

[18]  George J. Klir,et al.  Fuzzy sets and fuzzy logic - theory and applications , 1995 .

[19]  Antony Browne,et al.  Investigating the influence of feature correlations on automatic relevance determination , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[20]  Adam Krzyzak,et al.  Comparison of Pleomorphic and Structural Features Used for Breast Cancer Malignancy Classification , 2008, Canadian Conference on AI.

[21]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[22]  Andrew H. Beck,et al.  Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival , 2011, Science Translational Medicine.

[23]  O. Mangasarian,et al.  Pattern Recognition Via Linear Programming: Theory and Application to Medical Diagnosis , 1989 .

[24]  W. J. Conover,et al.  Practical Nonparametric Statistics , 1972 .

[25]  David G. Stork,et al.  Pattern Classification , 1973 .

[26]  Roman Monczak,et al.  Computer-Aided Breast Cancer Diagnosis Based on the Analysis of Cytological Images of Fine Needle Biopsies , 2013, IEEE Transactions on Medical Imaging.

[27]  S Friedman,et al.  Prognostic value of histologic grade nuclear components of Scarff‐Bloom‐Richardson (SBR). An improved score modification based on a multivariate analysis of 1262 invasive ductal breast carcinomas , 1989, Cancer.

[28]  O. Mangasarian,et al.  Multisurface method of pattern separation for medical diagnosis applied to breast cytology. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

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

[30]  Elena A. Fedorovskaya,et al.  Digital Image Processing and Analysis , 2010 .

[31]  D. Kleinbaum,et al.  Applied Regression Analysis and Multivariable Methods , 1999 .

[32]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[33]  Jerzy Detyna,et al.  Grading breast cancer malignancy with neural networks , 2011, Bio Algorithms Med Syst..

[34]  Olvi L. Mangasarian,et al.  Nuclear feature extraction for breast tumor diagnosis , 1993, Electronic Imaging.

[35]  Gerald Schaefer,et al.  A hybrid classifier committee for analysing asymmetry features in breast thermograms , 2014, Appl. Soft Comput..

[36]  I. Ellis,et al.  Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. , 2002, Histopathology.

[37]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.