Optimized Gabor features for mass classification in mammography

Display Omitted Key idea is optimizing Gabor filters such that they respond stronger to features that best discriminate normal and abnormal tissues.Contribution is about a strategy based on PSO and incremental clustering for optimizing a Gabor filter bank for accurate detection.Optimized Gabor filter bank is applied on overlapping blocks of ROIs to collect moment-based features from the magnitudes of Gabor responses. Gabor filter bank has been successfully used for false positive reduction problem and the discrimination of benign and malignant masses in breast cancer detection. However, a generic Gabor filter bank is not adapted to multi-orientation and multi-scale texture micro-patterns present in the regions of interest (ROIs) of mammograms. There are two main optimization concerns: how many filters should be in a Gabor filter band and what should be their parameters. Addressing these issues, this work focuses on finding optimizing Gabor filter banks based on an incremental clustering algorithm and Particle Swarm Optimization (PSO). We employ an SVM with Gaussian kernel as a fitness function for PSO. The effect of optimized Gabor filter bank was evaluated on 1024 ROIs extracted from a Digital Database for Screening Mammography (DDSM) using four performance measures (i.e., accuracy, area under ROC curve, sensitivity and specificity) for the above mentioned mass classification problems. The results show that the proposed method enhances the performance and reduces the computational cost. Moreover, the Wilcoxon signed rank test over the significance level of 0.05 reveals that the performance difference between the optimized Gabor filter bank and non-optimized Gabor filter bank is statistically significant.

[1]  D. K. Pratihar,et al.  Particle Swarm Optimization Algorithm vs Genetic Algorithm to Develop Integrated Scheme for Obtaining Optimal Mechanical Structure and Adaptive Controller of a Robot , 2011 .

[2]  N. Riyahi Alam,et al.  Computer-Aided Mass Detection on Digitized Mammograms using a Novel Hybrid Segmentation System , 2010 .

[3]  Licheng Jiao,et al.  Training Support Vector Machines Using Greedy Stagewise Algorithm , 2005, PAKDD.

[4]  Salim Lahmiri,et al.  Hybrid discrete wavelet transform and Gabor filter banks processing for mammogram features extraction , 2011, 2011 IEEE 9th International New Circuits and systems conference.

[5]  Ramin Rajabioun,et al.  Cuckoo Optimization Algorithm , 2011, Appl. Soft Comput..

[6]  Shohreh Kasaei,et al.  Benign and malignant breast tumors classification based on region growing and CNN segmentation , 2015, Expert Syst. Appl..

[7]  Zohreh Azimifar,et al.  Contourlet-based mammography mass classification using the SVM family , 2010, Comput. Biol. Medicine.

[8]  Licheng Jiao,et al.  Training Hard-Margin Support Vector Machines Using Greedy Stagewise Algorithm , 2008, IEEE Transactions on Neural Networks.

[9]  Joni-Kristian Kämäräinen,et al.  Simple Gabor feature space for invariant object recognition , 2004, Pattern Recognit. Lett..

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

[11]  Muhammad Hussain.  False Positive Reduction Using Gabor Feature Subset Selection , 2013, 2013 International Conference on Information Science and Applications (ICISA).

[12]  Galina L. Rogova,et al.  Microcalcification texture analysis in a hybrid system for computer-aided mammography , 1999, Medical Imaging.

[13]  Leslie S. Smith,et al.  Feature subset selection in large dimensionality domains , 2010, Pattern Recognit..

[14]  Guanming Lu,et al.  A novel risk assessment system for port state control inspection , 2008, 2008 IEEE International Conference on Intelligence and Security Informatics.

[15]  F. Wilcoxon.  Individual Comparisons by Ranking Methods , 1945 .

[16]  Chih-Jen Lin,et al.  A Practical Guide to Support Vector Classication , 2008 .

[17]  Wen Gao,et al.  Hierarchical Ensemble of Global and Local Classifiers for Face Recognition , 2009, IEEE Trans. Image Process..

[18]  Muhammad Ghulam,et al.  A Comparison of Different Gabor Features for Mass Classification in Mammography , 2012, 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems.

[19]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[20]  Saudi Arabia,et al.  Effective Extraction of Gabor Features for False Positive Reduction and Mass Classification in Mammography , 2014 .

[21]  Muhammad Ghulam,et al.  Mass Detection in Digital Mammograms Using Optimized Gabor Filter Bank , 2012, ISVC.

[22]  Anselmo Cardoso de Paiva,et al.  Classification of breast regions as mass and non-mass based on digital mammograms using taxonomic indexes and SVM , 2015, Comput. Biol. Medicine.

[23]  Q. D. Truong,et al.  An Alternative Approach to Reduce Massive False Positives in Mammograms Using Block Variance of Local Coefficients Features and Support Vector Machine , 2013, Complex Adaptive Systems.

[24]  Alexandre César Muniz de Oliveira,et al.  Classification of breast tissues using Moran's index and Geary's coefficient as texture signatures and SVM , 2009, Comput. Biol. Medicine.

[25]  Wen Gao,et al.  Hierarchical Ensemble of Global and Local Classifiers for Face Recognition , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[26]  Anselmo Cardoso de Paiva,et al.  Detection of masses in mammogram images using CNN, geostatistic functions and SVM , 2011, Comput. Biol. Medicine.

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

[28]  Arnau Oliver,et al.  A review of automatic mass detection and segmentation in mammographic images , 2010, Medical Image Anal..

[29]  Rangaraj M. Rangayyan,et al.  Directional analysis of images with Gabor wavelets , 2000, Proceedings 13th Brazilian Symposium on Computer Graphics and Image Processing (Cat. No.PR00878).

[30]  Zehang Sun,et al.  Monocular precrash vehicle detection: features and classifiers , 2006, IEEE Transactions on Image Processing.

[31]  Xinbo Gao,et al.  A Feature Analysis Approach to Mass Detection in Mammography Based on RF-SVM , 2007, 2007 IEEE International Conference on Image Processing.

[32]  S. García,et al.  An Extension on "Statistical Comparisons of Classifiers over Multiple Data Sets" for all Pairwise Comparisons , 2008 .

[33]  J. Daugman.  Two-dimensional spectral analysis of cortical receptive field profiles , 1980, Vision Research.

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

[35]  Asoke K. Nandi,et al.  Toward breast cancer diagnosis based on automated segmentation of masses in mammograms , 2009, Pattern Recognit..

[36]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[37]  Uday B. Desai,et al.  An unsupervised scheme for detection of microcalcifications on mammograms , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[38]  Muhammad Hussain,et al.  False-positive reduction in mammography using multiscale spatial Weber law descriptor and support vector machines , 2013, Neural Computing and Applications.

[39]  Xavier Lladó,et al.  A textural approach for mass false positive reduction in mammography , 2009, Comput. Medical Imaging Graph..

[40]  A. K. Barros,et al.  Classification of breast tissue in mammograms using efficient coding , 2011, Biomedical engineering online.

[41]  M. R. Turner,et al.  Texture discrimination by Gabor functions , 1986, Biological Cybernetics.

[42]  Ioan Buciu,et al.  Directional features for automatic tumor classification of mammogram images , 2011, Biomed. Signal Process. Control..

[43]  Anselmo Cardoso de Paiva,et al.  Detection of masses in mammographic images using geometry, Simpson's Diversity Index and SVM , 2010 .

[44]  Houjin Chen,et al.  Texton analysis for mass classification in mammograms , 2015, Pattern Recognit. Lett..

[45]  Anselmo Cardoso de Paiva,et al.  A mass classification using spatial diversity approaches in mammography images for false positive reduction , 2013, Expert Syst. Appl..

[46]  Béla Pataki,et al.  A hybrid system for detecting masses in mammographic images , 2006, IEEE Transactions on Instrumentation and Measurement.

[47]  Muhammad Hussain,et al.  Comparison of Statistical, LBP, and Multi-Resolution Analysis Features for Breast Mass Classification , 2014, Journal of Medical Systems.

[48]  Christopher J. C. Burges,et al.  A Tutorial on Support Vector Machines for Pattern Recognition , 1998, Data Mining and Knowledge Discovery.

[49]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[50]  Yongyi Yang,et al.  Computer-Aided Detection and Diagnosis of Breast Cancer With Mammography: Recent Advances , 2009, IEEE Transactions on Information Technology in Biomedicine.

[51]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[52]  Gavin C. Cawley,et al.  Sparse Multinomial Logistic Regression via Bayesian L1 Regularisation , 2006, NIPS.

[53]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[54]  Loris Nanni,et al.  A very high performing system to discriminate tissues in mammograms as benign and malignant , 2012, Expert Syst. Appl..