Computer aided diagnosis in mammography with content-based image retrieval

Computer-aided diagnosis (CAD) for breast cancer, a common form of cancer in women, has been an active research area. This work aims to investigate and develop CAD techniques for clustered microcalcifications (MCCs), which can be an important early sign of breast cancer. The contributions of this work include development of a database of cancer cases and algorithms for detection and classification of MCCs. First, a database consisting of a large number of cases is built from different sources. To support the merging of cases from different data sources, a feature comparison study is conducted between mammograms from screen film and full field digital mammography (FFDM) systems. It is demonstrated that the features extracted from film and FFDM are highly correlated and there is no adverse effect on a CAD task of classification when used together. Second, a spatial point process (SPP) approach is proposed to exploit the spatial distribution among different MCs in a mammogram directly during the detection process. This is different from the conventional approach in which detection algorithms are employed to first identify individual MCs in a mammogram, which are subsequently grouped into clusters by a clustering algorithm. The performance of the proposed approach is demonstrated to be superior to an existing method based on the support vector machine (SVM). Third, in observation of the emerging of large databases from the picture archiving and communication (PAC) systems in the clinics, a retrieval driven approach is proposed for classification of MCCs. In this approach, for a case to be diagnosed (i.e., query), a set of similar cases is retrieved from a database and subsequently is used to train an adaptive classifier specifically for the query case using the technique of logistic regression. The proposed approach is demonstrated to lead to significant improvement in classification accuracy. Moreover, the proposed adaptive classification approach is further developed using regularization techniques, where a prior is first derived from a baseline classifier and then used to regularize the adaptive classifier trained with the retrieved cases. The regularized adaptive classifier can be more computationally efficient, and is demonstrated to achieve further improvement in performance.

[1]  Robert M. Nishikawa,et al.  A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications , 2005, IEEE Transactions on Medical Imaging.

[2]  Robert Tibshirani,et al.  Discriminant Adaptive Nearest Neighbor Classification , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Robert M. Nishikawa,et al.  RETRIEVAL-DRIVEN MICROCALCIFICATION CLASSIFICATION FOR BREAST CANCER DIAGNOSIS , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[4]  E. Burnside,et al.  A logistic regression model based on the national mammography database format to aid breast cancer diagnosis. , 2009, AJR. American journal of roentgenology.

[5]  Paul Sajda,et al.  Learning contextual relationships in mammograms using a hierarchical pyramid neural network , 2002, IEEE Transactions on Medical Imaging.

[6]  Berkman Sahiner,et al.  Similarity evaluation in a content-based image retrieval (CBIR) CADx system for characterization of breast masses on ultrasound images. , 2011, Medical physics.

[7]  Mia K Markey,et al.  Breast cancer CADx based on BI-RAds descriptors from two mammographic views. , 2006, Medical physics.

[8]  David Gur,et al.  A permutation test sensitive to differences in areas for comparing ROC curves from a paired design , 2005, Statistics in medicine.

[9]  J. Boone,et al.  Dedicated breast CT: radiation dose and image quality evaluation. , 2001, Radiology.

[10]  Nikolas P. Galatsanos,et al.  A support vector machine approach for detection of microcalcifications , 2002, IEEE Transactions on Medical Imaging.

[11]  C. Floyd,et al.  Case-based reasoning computer algorithm that uses mammographic findings for breast biopsy decisions. , 2000, AJR. American journal of roentgenology.

[12]  T. M. Kolb,et al.  Comparison of the performance of screening mammography, physical examination, and breast US and evaluation of factors that influence them: an analysis of 27,825 patient evaluations. , 2002, Radiology.

[13]  J. Elmore,et al.  Ten-year risk of false positive screening mammograms and clinical breast examinations. , 1998, The New England journal of medicine.

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

[15]  Yongyi Yang,et al.  Learning of perceptual similarity from expert readers for mammogram retrieval , 2009, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[16]  Berkman Sahiner,et al.  Optimal neural network architecture selection: improvement in computerized detection of microcalcifications. , 2002, Academic radiology.

[17]  Berkman Sahiner,et al.  Computerized detection and classification of microcalcifications on mammograms , 1995, Medical Imaging.

[18]  Heng-Da Cheng,et al.  Computer-aided detection and classification of microcalcifications in mammograms: a survey , 2003, Pattern Recognit..

[19]  C. D'Orsi,et al.  Diagnostic Performance of Digital Versus Film Mammography for Breast-Cancer Screening , 2005, The New England journal of medicine.

[20]  C. D. Page,et al.  Probabilistic computer model developed from clinical data in national mammography database format to classify mammographic findings. , 2009, Radiology.

[21]  Nikolas P. Galatsanos,et al.  A similarity learning approach to content-based image retrieval: application to digital mammography , 2004, IEEE Transactions on Medical Imaging.

[22]  N. Petrick,et al.  Improvement in radiologists' characterization of malignant and benign breast masses on serial mammograms with computer-aided diagnosis: an ROC study. , 2004, Radiology.

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

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

[25]  A. Mushlin,et al.  Estimating the accuracy of screening mammography: a meta-analysis. , 1998, American journal of preventive medicine.

[26]  R. Nishikawa,et al.  The use of a priori information in the detection of mammographic microcalcifications to improve their classification. , 2003, Medical physics.

[27]  M. Giger,et al.  Improving breast cancer diagnosis with computer-aided diagnosis. , 1999, Academic radiology.

[28]  Robert M. Nishikawa,et al.  Relevance vector machine for automatic detection of clustered microcalcifications , 2005, IEEE Transactions on Medical Imaging.

[29]  Ling Guan,et al.  A CAD System for the Automatic Detection of Clustered Microcalcification in Digitized Mammogram Films , 2000, IEEE Trans. Medical Imaging.

[30]  C. Geyer,et al.  Simulation Procedures and Likelihood Inference for Spatial Point Processes , 1994 .

[31]  Nico Karssemeijer,et al.  Stochastic model for automated detection of calcifications in digital mammograms , 1992, Image Vis. Comput..

[32]  Li Lan,et al.  Classification of breast lesions with multimodality computer-aided diagnosis: observer study results on an independent clinical data set. , 2006, Radiology.

[33]  C Kimme-Smith,et al.  Mammography fixed grid versus reciprocating grid: evaluation using cadaveric breasts as test objects. , 1996, Medical physics.

[34]  Richard H. Moore,et al.  THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY , 2007 .

[35]  Wei Qian,et al.  An improved method of region grouping for microcalcification detection in digital mammograms. , 2002 .

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

[37]  Nico Karssemeijer,et al.  Noise equalization for detection of microcalcification clusters in direct digital mammogram images , 2004, IEEE Transactions on Medical Imaging.

[38]  C D Claussen,et al.  Comparison of full-field digital mammography and film-screen mammography: image quality and lesion detection. , 2005, The British journal of radiology.

[39]  Mitchell D. Feldman,et al.  Full-field digital mammography compared with screen-film mammography in the detection of breast cancer: rays of light through DMIST or more fog? , 2007, Breast Cancer Research and Treatment.

[40]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

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

[42]  A Bazzani,et al.  An SVM classifier to separate false signals from microcalcifications in digital mammograms , 2001, Physics in medicine and biology.

[43]  R M Nishikawa,et al.  Dependence of computer classification of clustered microcalcifications on the correct detection of microcalcifications. , 2001, Medical physics.

[44]  Xavier Descombes,et al.  An object-based approach for detecting small brain lesions: application to Virchow-Robin spaces , 2004, IEEE Transactions on Medical Imaging.

[45]  Dimitrios I. Fotiadis,et al.  Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines , 2005, Artif. Intell. Medicine.

[46]  Nikolas P. Galatsanos,et al.  Content-based image retrieval for digital mammography , 2002 .

[47]  Joachim Dengler,et al.  Segmentation of microcalcifications in mammograms , 1991, IEEE Trans. Medical Imaging.

[48]  Michael Brady,et al.  A biologically inspired algorithm for microcalcification cluster detection , 2006, Medical Image Anal..

[49]  Kerry T. Krugh,et al.  Microcalcification detectability for four mammographic detectors: flat-panel, CCD, CR, and screen/film). , 2002, Medical physics.

[50]  N. Petrick,et al.  Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces. , 1998, Medical physics.

[51]  M. Elter,et al.  CADx of mammographic masses and clustered microcalcifications: a review. , 2009, Medical physics.

[52]  Tongyuan Wang,et al.  Medical image retrieval and registration: towards computer assisted diagnostic approach , 2004, 2004 IDEAS Workshop on Medical Information Systems: The Digital Hospital (IDEAS-DH'04).

[53]  Jacek M Zurada,et al.  Decision optimization of case-based computer-aided decision systems using genetic algorithms with application to mammography , 2008, Physics in medicine and biology.

[54]  Alan C. Bovik,et al.  Computer-Aided Detection and Diagnosis in Mammography , 2005 .

[55]  Yongyi Yang,et al.  Techniques in the detection of microcalcification clusters in digital mammograms , 2005 .

[56]  D. Chakraborty,et al.  Free-response methodology: alternate analysis and a new observer-performance experiment. , 1990, Radiology.

[57]  Stuart G. Baker,et al.  A Proposed Design and Analysis for Comparing Digital and Analog Mammography , 2001 .

[58]  Hamid Soltanian-Zadeh,et al.  Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms , 2004, Pattern Recognit..

[59]  David Gur,et al.  A method to improve visual similarity of breast masses for an interactive computer-aided diagnosis environment. , 2005, Medical physics.

[60]  P. Green Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[61]  Josiane Zerubia,et al.  Marked point process in image analysis , 2002, IEEE Signal Process. Mag..

[62]  John F. Hamilton,et al.  A Free Response Approach To The Measurement And Characterization Of Radiographic Observer Performance , 1977, Other Conferences.

[63]  L Bocchi,et al.  Shape analysis of microcalcifications using Radon transform. , 2007, Medical engineering & physics.

[64]  Ping Zhang,et al.  A neural-genetic algorithm for feature selection and breast abnormality classification in digital mammography , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

[65]  Robert M. Nishikawa,et al.  Method of extracting signal area and signal thickness of microcalcifications from digital mammograms , 1992, Other Conferences.

[66]  G Coppini,et al.  Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks. , 2004, Medical engineering & physics.

[67]  C. Markopoulos,et al.  Use of artificial neural networks (computer analysis) in the diagnosis of microcalcifications on mammography. , 2001, European journal of radiology.

[68]  Antoine Geissbühler,et al.  A Review of Content{Based Image Retrieval Systems in Medical Applications { Clinical Bene(cid:12)ts and Future Directions , 2022 .

[69]  Nico Karssemeijer,et al.  Normalization of local contrast in mammograms , 2000, IEEE Transactions on Medical Imaging.

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

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

[72]  Frank W. Samuelson,et al.  Comparing image detection algorithms using resampling , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[73]  Carey E Floyd,et al.  Development and evaluation of a case-based reasoning classifier for prediction of breast biopsy outcome with BI-RADS lexicon. , 2002, Medical physics.

[74]  Chris C Shaw,et al.  Comparison of full-field digital mammography and screen-film mammography for detection and characterization of simulated small masses. , 2006, AJR. American journal of roentgenology.

[75]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[76]  C. Floyd,et al.  Breast cancer: prediction with artificial neural network based on BI-RADS standardized lexicon. , 1995, Radiology.

[77]  Robin N. Strickland,et al.  Wavelet transform methods for object detection and recovery , 1997, IEEE Trans. Image Process..