Mammographic Image Based Breast Tissue Classification with Kernel Self-optimized Fisher Discriminant for Breast Cancer Diagnosis

Breast tissue classification is an important and effective way for computer aided diagnosis of breast cancer with digital mammogram. Current methods endure two problems, firstly pectoral muscle influences the classification performance owing to its texture similar to parenchyma, and secondly classification algorithms fail to deal with the nonlinear problem from the digital mammogram. For these problems, we propose a novel framework of breast tissue classification based on kernel self-optimized discriminant analysis combined with the artifacts and pectoral muscle removal with multi-level segmentation based Connected Component Labeling analysis. Experiments on mini-MIAS database are implemented to testify and evaluate the performance of proposed algorithm.

[1]  Hichem Sahbi,et al.  Kernel PCA for similarity invariant shape recognition , 2007, Neurocomputing.

[2]  Robert Marti,et al.  A Novel Breast Tissue Density Classification Methodology , 2008, IEEE Transactions on Information Technology in Biomedicine.

[3]  Konstantinos N. Plataniotis,et al.  Face recognition using kernel direct discriminant analysis algorithms , 2003, IEEE Trans. Neural Networks.

[4]  Mehdi Parviz,et al.  Boosting Approach for Score Level Fusion in Multimodal Biometrics Based on AUC Maximization , 2011, J. Inf. Hiding Multim. Signal Process..

[5]  Si Wu,et al.  Improving support vector machine classifiers by modifying kernel functions , 1999, Neural Networks.

[6]  Bernhard Schölkopf,et al.  A Direct Method for Building Sparse Kernel Learning Algorithms , 2006, J. Mach. Learn. Res..

[7]  Jeng-Shyang Pan,et al.  Adaptive quasiconformal kernel discriminant analysis , 2008, Neurocomputing.

[8]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[9]  Hong Chang,et al.  Learning the kernel matrix by maximizing a KFD-based class separability criterion , 2007, Pattern Recognit..

[10]  Mohd Saberi Mohamad,et al.  A two-stage method to select a smaller subset of informative genes for cancer classification , 2009 .

[11]  Jian Yang,et al.  KPCA plus LDA: a complete kernel Fisher discriminant framework for feature extraction and recognition , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Michifumi Yoshioka,et al.  Three-stage method for selecting informative genes for cancer classification , 2009 .

[13]  Xiao-Li Yang,et al.  Movement invariants-based algorithm for medical image tilt correction , 2010, Int. J. Autom. Comput..

[14]  Xuelong Li,et al.  Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Mohd Saberi Mohamad,et al.  A cyclic hybrid method to select a smaller subset of informative genes for cancer classification , 2009 .

[16]  B. Chapman,et al.  Automated assessment of the composition of breast tissue revealed on tissue-thickness-corrected mammography. , 2003, AJR. American journal of roentgenology.

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

[18]  Mohd Saberi Mohamad,et al.  A three-stage method to select informative genes for cancer classification , 2009 .

[19]  Reyer Zwiggelaar,et al.  Mammographic Density Classification using Multiresolution Histogram Information , .

[20]  Mahantapas Kundu,et al.  Complementary Features Combined in a MLP-based System to Recognize Handwritten Devnagari Character , 2011, J. Inf. Hiding Multim. Signal Process..

[21]  Ian W. Ricketts,et al.  The Mammographic Image Analysis Society digital mammogram database , 1994 .

[22]  Jian Huang,et al.  Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[23]  Jeng-Shyang Pan,et al.  Kernel optimization-based discriminant analysis for face recognition , 2009, Neural Computing and Applications.

[24]  Jeng-Shyang Pan,et al.  ADAPTIVE DATA-DEPENDENT MATRIX NORM BASED GAUSSIAN KERNEL FOR FACIAL FEATURE EXTRACTION , 2007 .

[25]  Jian-Huang Lai,et al.  Kernel subspace LDA with optimized kernel parameters on face recognition , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[26]  Elna-Marie Larsson,et al.  MR venography using an intravascular contrast agent: results from a multicenter phase 2 study of dosage. , 2003, AJR. American journal of roentgenology.