Combining texture features from the MLO and CC views for mammographic CADx

The purpose of this study was to investigate approaches for combining information from the MLO and CC mammographic views for Computer-aided Diagnosis (CADx) algorithms. Feature level and classifier output level combinations were explored. Linear discriminant analysis (LDA) with step-wise feature selection from a set of Haralick's texture features was used to develop classifiers for distinguishing between benign and malignant mammographic lesions. The effect of correlation between features from the two views on the performance of classifiers was investigated. The single view models included: (a) an LDA model with stepwise selection based on the MLO view only (MLO-Only) and similarly (b) a CC-Only LDA model. The feature-level combination models included: (a) LDA based on concatenation of feature sets selected independently from the two views (FEAT_CON), (b) LDA based on the concatenated feature sets along with the corresponding value of each feature from the opposite view (FEAT_COR_CON) if the correlation was below a threshold, (c) LDA based on the average of the MLO and CC feature values (FEAT_AVG). The classifier output level combination models investigated included: (a) average of the outputs of the MLO-Only and CC-Only classifiers (OUTPUT_AVG), (b) maximum of the outputs of the MLO-Only and CC-Only classifiers (OUTPUT_MAX), (c) minimum of the outputs of the MLO-Only and CC-Only classifiers (OUTPUT_MIN), (d) a second level LDA classifier on the outputs of the MLO-Only and CC-Only classifiers (OUTPUT_LDA), (e) product of the output values of the two classifiers (OUTPUT_PROD). The performance of the models was assessed and compared using the ROC methodology to determine if combination models performed better than the single-view models.

[1]  Lubomir M. Hadjiiski,et al.  Improvement of mammographic mass characterization using spiculation meausures and morphological features. , 2001, Medical physics.

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

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

[4]  B Sahiner,et al.  False-positive reduction technique for detection of masses on digital mammograms: global and local multiresolution texture analysis. , 1997, Medical physics.

[5]  Subhash Sharma.  Applied multivariate techniques , 1995 .

[6]  J. J. Gisvold,et al.  Likelihood of malignant disease for various categories of mammographically detected, nonpalpable breast lesions. , 1993, Mayo Clinic proceedings.

[7]  Rangaraj M. Rangayyan,et al.  Gradient and texture analysis for the classification of mammographic masses , 2000, IEEE Transactions on Medical Imaging.

[8]  Berkman Sahiner,et al.  Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images , 1996, IEEE Trans. Medical Imaging.

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

[10]  Rangaraj M. Rangayyan,et al.  Classification of tumors and masses in mammograms using neural networks with shape and texture features , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[11]  Yulei Jiang,et al.  Effect of correlation on combining diagnostic information from two images of the same patient. , 2005, Medical physics.

[12]  David G. Stork,et al.  Pattern classification, 2nd Edition , 2000 .

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

[14]  N. Petrick,et al.  Design of a high-sensitivity classifier based on a genetic algorithm: application to computer-aided diagnosis , 1998, Physics in medicine and biology.

[15]  Mia K Markey,et al.  Correspondence in texture features between two mammographic views. , 2005, Medical physics.

[16]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[17]  N. Petrick,et al.  Computerized characterization of masses on mammograms: the rubber band straightening transform and texture analysis. , 1998, Medical physics.

[18]  N. Petrick,et al.  Classification of mass and normal breast tissue on digital mammograms: multiresolution texture analysis. , 1995, Medical physics.

[19]  L W Bassett,et al.  Breast cancer detection: one versus two views. , 1987, Radiology.

[20]  Luisa P. Wallace,et al.  Changes in breast cancer detection and mammography recall rates after the introduction of a computer-aided detection system. , 2004, Journal of the National Cancer Institute.

[21]  U. G. Dailey.  Cancer,Facts and Figures about. , 2022, Journal of the National Medical Association.

[22]  Richard H. Moore,et al.  Current Status of the Digital Database for Screening Mammography , 1998, Digital Mammography / IWDM.

[23]  C A Roe,et al.  Statistical Comparison of Two ROC-curve Estimates Obtained from Partially-paired Datasets , 1998, Medical decision making : an international journal of the Society for Medical Decision Making.

[24]  K C Young,et al.  Influence of number of views and mammographic film density on the detection of invasive cancers: results from the NHS Breast Screening Programme. , 1997, The British journal of radiology.

[25]  N Karssemeijer,et al.  Automated classification of clustered microcalcifications into malignant and benign types. , 2000, Medical physics.

[26]  R. Given-Wilson,et al.  Observer variability in cancer detection during routine repeat (incident) mammographic screening in a study of two versus one view mammography , 1999, Journal of medical screening.

[27]  H P Chan,et al.  Automated detection of breast masses on mammograms using adaptive contrast enhancement and texture classification. , 1996, Medical physics.

[28]  Maryellen L. Giger,et al.  Computerized analysis of multiple-mammographic views: potential usefulness of special view mammograms in computer-aided diagnosis , 2001, IEEE Transactions on Medical Imaging.

[29]  D. Kopans.  The positive predictive value of mammography. , 1992, AJR. American journal of roentgenology.

[30]  B. Stewart,et al.  World Cancer Report , 2003 .

[31]  D. Petitti,et al.  Saving Women's Lives: Strategies for Improving Breast Cancer Detection and Diagnosis , 2005 .

[32]  C Kimme-Smith,et al.  Using tissue texture surrounding calcification clusters to predict benign vs malignant outcomes. , 1996, Medical physics.

[33]  Yulei Jiang,et al.  An ROC comparison of four methods of combining information from multiple images of the same patient. , 2004, Medical physics.

[34]  K L Lam,et al.  Computerized classification of malignant and benign microcalcifications on mammograms: texture analysis using an artificial neural network. , 1997, Physics in medicine and biology.

[35]  H P Chan,et al.  Combined adaptive enhancement and region-growing segmentation of breast masses on digitized mammograms. , 1999, Medical physics.

[36]  N. Wald,et al.  UKCCCR multicentre randomised controlled trial of one and two view mammography in breast cancer screening , 1995, BMJ.

[37]  R. Bird,et al.  Analysis of cancers missed at screening mammography. , 1992, Radiology.

[38]  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.

[39]  Stefano Ciatto,et al.  Comparison of two commercial systems for computer-assisted detection (CAD) as an aid to interpreting screening mammograms. , 2004, La Radiologia medica.

[40]  Rangaraj M. Rangayyan,et al.  Detection of breast masses in mammograms by density slicing and texture flow-field analysis , 2001, IEEE Transactions on Medical Imaging.

[41]  Berkman Sahiner,et al.  Improvement of computerized mass detection on mammograms: fusion of two-view information. , 2002, Medical physics.