Automatic classification of clustered microcalcifications by a multiple expert system

Abstract Mammography is a not invasive diagnostic technique widely used for early cancer detection in women breast. A significant visual clue of the disease is the presence of clusters of microcalcifications. The automatic recognition of malignant clusters of microcalcifications, which could be very helpful for diagnostic purposes, is a very difficult task because of the small size of the microcalcifications and of the poor quality of the mammographic images. In this paper we propose a novel approach for classifying clusters of microcalcifications, based on a Multiple Expert System; such system aggregates several experts, some of which are devoted to classify the single microcalcifications while others are aimed to classify the cluster considered as a whole. The final output results from the suitable combination of the two groups of experts. The tests performed on a standard database of 40 mammographic images have confirmed the effectiveness of the approach.

[1]  R. McLelland,et al.  Screening for breast cancer: opportunities, status and challenges. , 1990, Recent results in cancer research. Fortschritte der Krebsforschung. Progres dans les recherches sur le cancer.

[2]  C. Metz,et al.  Maximum likelihood estimation of receiver operating characteristic (ROC) curves from continuously-distributed data. , 1998, Statistics in medicine.

[3]  Y H Chang,et al.  Identification of clustered microcalcifications on digitized mammograms using morphology and topography-based computer-aided detection schemes. A preliminary experiment. , 1998, Investigative radiology.

[4]  E. Thurfjell,et al.  Benefit of independent double reading in a population-based mammography screening program. , 1994, Radiology.

[5]  P. G. Tahoces,et al.  A wavelet-based algorithm for detecting clustered microcalcifications in digital mammograms. , 1999, Medical physics.

[6]  Josef Kittler,et al.  A Framework for Classifier Fusion: Is It Still Needed? , 2000, SSPR/SPR.

[7]  H Yoshida,et al.  Optimally weighted wavelet transform based on supervised training for detection of microcalcifications in digital mammograms. , 1998, Medical physics.

[8]  Rangaraj M. Rangayyan,et al.  Application of shape analysis to mammographic calcifications , 1994, IEEE Trans. Medical Imaging.

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

[10]  Robin N. Strickland,et al.  Wavelet transforms for detecting microcalcifications in mammograms , 1996, IEEE Trans. Medical Imaging.

[11]  M. Giger,et al.  Malignant and benign clustered microcalcifications: automated feature analysis and classification. , 1996, Radiology.

[12]  Heng-Da Cheng,et al.  A novel approach to microcalcification detection using fuzzy logic technique , 1998, IEEE Transactions on Medical Imaging.

[13]  Fuad Rahman,et al.  A new multi-expert decision combination algorithm and its application to the detection of circumscribed masses in digital mammograms , 2001, Pattern Recognit..

[14]  Nico Karssemeijer,et al.  ADAPTIVE NOISE EQUALIZATION AND RECOGNITION OF MICROCALCIFICATION CLUSTERS IN MAMMOGRAMS , 1993 .

[15]  Nicolaos B. Karayiannis,et al.  Detection of microcalcifications in digital mammograms using wavelets , 1998, IEEE Transactions on Medical Imaging.

[16]  J A Swets,et al.  Measuring the accuracy of diagnostic systems. , 1988, Science.

[17]  R. Ansari,et al.  Detection of microcalcifications in mammograms using higher order statistics , 1997, IEEE Signal Processing Letters.

[18]  Sargur N. Srihari,et al.  Decision Combination in Multiple Classifier Systems , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  H Pujol,et al.  Isolated clustered microcalcifications: diagnostic value of mammography--series of 400 cases with surgical verification. , 1994, Radiology.

[20]  K Doi,et al.  Analysis of methods for reducing false positives in the automated detection of clustered microcalcifications in mammograms. , 1998, Medical physics.

[21]  Dr. med Marton Lanyi.  Diagnosis and Differential Diagnosis of Breast Calcifications , 1987, Springer Berlin Heidelberg.

[22]  F. Shtern.  Digital mammography and related technologies: a perspective from the National Cancer Institute. , 1992, Radiology.

[23]  Atam P. Dhawan,et al.  Analysis of mammographic microcalcifications using gray-level image structure features , 1996, IEEE Trans. Medical Imaging.

[24]  Y H Chang,et al.  Computer-aided detection of clustered microcalcifications in digitized mammograms. , 1995, Academic radiology.

[25]  R. Zuurbier,et al.  Classification of microcalcifications in radiographs of pathologic specimens for the diagnosis of breast cancer. , 1995 .

[26]  Hyun Wook Park,et al.  Statistical Textural Features for Detection of Microcalcifications in Digitized Mammograms , 1999, IEEE Trans. Medical Imaging.

[27]  Matthew T. Freedman,et al.  Classification of microcalcifications in digital mammograms using trend-oriented radial basis function neural network , 1999, Pattern Recognit..

[28]  C. Metz.  ROC Methodology in Radiologic Imaging , 1986, Investigative radiology.

[29]  Y. Wu,et al.  Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. , 1993, Radiology.

[30]  J Roehrig,et al.  The promise of computer aided detection in digital mammography. , 1999, European journal of radiology.

[31]  R Lederman,et al.  Analysis of clustered microcalcifications by using a single numeric classifier extracted from mammographic digital images. , 1998, Academic radiology.

[32]  Lars Kai Hansen,et al.  Neural Network Ensembles , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[33]  K Doi,et al.  An improved shift-invariant artificial neural network for computerized detection of clustered microcalcifications in digital mammograms. , 1996, Medical physics.

[34]  Atam P. Dhawan,et al.  ARTIFICIAL NEURAL NETWORK BASED CLASSIFICATION OF MAMMOGRAPHIC MICROCALCIFICATIONS USING IMAGE STRUCTURE FEATURES , 1993 .

[35]  Etta D. Pisano,et al.  Image Processing and Computer Aided Diagnosis in Digital Mammography: A Clinical Perspective , 1993 .

[36]  Ching Y. Suen,et al.  A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Atam P. Dhawan,et al.  Fuzzy convex set-based pattern classification for analysis of mammographic microcalcifications , 2001, Pattern Recognit..

[38]  W A Murphy,et al.  Isolated clustered microcalcifications in the breast: radiologic-pathologic correlation. , 1978, Radiology.

[39]  Baoyu Zheng,et al.  Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcifications , 1996, IEEE Trans. Medical Imaging.

[40]  N. Petrick,et al.  Improvement of radiologists' characterization of mammographic masses by using computer-aided diagnosis: an ROC study. , 1999, Radiology.

[41]  Adam Krzyżak,et al.  Methods of combining multiple classifiers and their applications to handwriting recognition , 1992, IEEE Trans. Syst. Man Cybern..

[42]  C J Vyborny,et al.  Can computers help radiologists read mammograms? , 1994, Radiology.