Zhilin Feng

A new automatic method for mass detection in mammography with false positives reduction by supported vector machine

Mass localization is important in computer-aided detection (CAD) system for the diagnosis of suspicious regions in mammograms. In this paper, we present an automated classification system for the detection of masses in mammographic images. Suspicious regions are located with an adaptive region growing firstly. Then, the initial regions are further refined with narrow band based active contour, which can improve the segmentation accuracy of masses. CLBP (Complete Local Binary Pattern) texture features are extracted from the ROIs (regions of interest) containing the segmented suspicious regions. Finally, the ROIs are classified by means of support vector machine (SVM), with supervision provided by the radiologists diagnosis. The method was evaluated on a dataset with 231 images, containing 245 masses. Among them, 125 images containing 133 masses are used to optimize the parameters and are used to train SVM. The remaining 106 images are used to test the performance. It obtained 1.36 FPsI at the sensitivity 76.8%. It shows that the proposed method is a promising approach to achieve low FPsI while maintain a high sensitivity.

A multiscale contrast enhancement algorithm for breast cancer detection using Laplacian Pyramid

Mammography is currently regarded as one of the best ways to detect breast cancer in the early stage. However, due to the limitation in imaging condition and the subtleness of the difference between normal and abnormal features, it is generally difficult to interpret the mammograms. Thus, image enhancement techniques have been widely used in screening mammograms. In this paper, a multiscale contrast enhancement algorithm based on Laplacian Pyramid is developed to enhance the contrast of the mammograms and improve the discernibility of the abnormal features. In the proposed algorithm, an image is first decomposed into a multi-level Laplacian Pyramid and then the enhancement is performed in the reconstruction stage. A multiscale contrast measure is used to modify the coefficients iteratively level by level and the enhanced image is obtained at the lowest level. Experiments proved the effectiveness of the proposed algorithm.

Colorization Using Segmentation with Random Walk

Traditional monochrome image colorization techniques require considerable user interaction and a lot of time. The segment-based colorization works fast but at the expense of detail loss because of the large segmentation; while the optimization based method looks much more continuous but takes longer time. This paper proposed a novel approach: Segmentation colorization based on random walks, which is a fast segmentation technique and can naturally handle multi-label segmentation problems. It can maintain smoothness almost everywhere except for the sharp discontinuity at the boundaries in the images. Firstly, with the few seeds of pixels set manually scribbled by the user, a global energy is set up according to the spatial information and statistical grayscale information. Then, with random walks, the global optimal segmentation is obtained fast and efficiently. Finally, a banded graph cut based refine procedure is applied to deal with ambiguous regions of the previous segmentation. Several results are shown to demonstrate the effectiveness of the proposed method.

Mass classification in mammogram with semi-supervised relief based feature selection

Mammogram is currently the best way for early detection of breast cancer. Mass is a typical sign of breast cancer, and the classification of masses as malignant or benign may assist radiologists in reducing the biopsy rate without increasing false negatives. Typically, different geometry and texture features are extracted and utilized to train a classifier to classify a mass. However, not each feature is equally important for a classifier, and some features may indeed decrease the performance of a classifier. In this paper, we investigated the usage of semi-supervised feature selection method for classification. After a mass is extracted from a ROI (region of interest) with level set method. Morphological and texture features are extracted from the segmented regions and surrounding regions. SSLFE (Semi- Supervised Local Feature Extraction, proposed in our previous work) is utilized to select important features for KNN classifier. Mammography images from DDSM were used for experiment. The experimental result shows that by incorporating information embedded in unlabeled data, SSLFE can improve the performance compared to the method without feature selection and traditional Relief method.

A Semi-Supervised Relief Based Feature Extraction Algorithm

Local Feature Extraction (LFE) algorithm is an effective feature extraction method developed in recent years. One of the shortcomings of the current LFE algorithm is that it can only process labeled data, and does not work well when the amount of the labeled data is limited. However, it is usually easy to obtain large amount of unlabeled data but only a few labeled data. In this paper, we propose a new feature extraction algorithm, called Semi-Supervised LFE (SSLFE), which can handle both labeled and unlabeled data to perform feature extraction. In the proposed algorithm, the labeled data are used to maximize the margin and the unlabeled data are used as regulations with respect to the intrinsic geometric structure of the data. The final projection matrix can be obtained by eigenvalue decomposition. Experiments on several datasets demonstrate that SSLFE achieves much higher classification accuracy than LFE.

A Pairwise Covariance-Preserving Projection Method for Dimension Reduction

Dimension reduction is critical in many areas of pattern classification and machine learning and many discriminant analysis algorithms have been proposed. In this paper, a Pairwise Covariance-preserving Projection Method (PCPM) is proposed for dimension reduction. PCPM maximizes the class discrimination and also preserves approximately the pairwise class covariances. The optimization involved in PCPM can be solved directly by eigenvalues decomposition. Our theoretical and empirical analysis reveals the relationship between PCPM and Linear Discriminant Analysis (LDA), Sliced Average Variance Estimator (SAVE), Heteroscedastic Discriminant Analysis (HDA) and Covariance preserving Projection Method (CPM). PCPM can utilize class mean and class covariance information at the same time. Furthermore, pairwise weight scheme can be incorporated naturally with the pairwise summarization form. The proposed methods are evaluated by both synthetic and real-world datasets.

Semi-supervised Discriminant Analysis Based on Dependence Estimation

Dimension reduction is very important for applications in data mining and machine learning. Dependence maximization based supervised feature extraction (SDMFE) is an effective dimension reduction method proposed recently. A shortcoming of SDMFE is that it can only use labeled data, and does not work well when labeled data are limited. However, in many applications, it is a common case. In this paper, we propose a novel feature extraction method, called Semi-Supervised Dependence Maximization Feature Extraction (SSDMFE), which can utilize simultaneously both labeled and unlabeled data to perform feature extraction. The labeled data are used to maximize the dependence and the unlabeled data are used as regulations with respect to the intrinsic geometric structure of the data. Experiments on several datasets are presented and the results demonstrate that SSDMFE achieves much higher classification accuracy than SDMFE when the amount of labeled data are limited.

Average neighborhood margin maximization projection with smooth regularization for face recognition

Dimensionality reduction is among the keys in many fields, most of the traditional method can be categorized as local or global ones. In this paper, we consider the dimension reduction problem with prior information is available, namely, semi-supervised dimension reduction. A new dimension reduction method that can explore both the labeled and unlabeled information in the dataset is proposed. The labeled data points are used to maximize the separability between different classes and the unlabeled data points are used to estimate the intrinsic geometric structure of the data. Specifically, we aim to learn a discriminant function which is as smooth as possible on the data manifold. The target optimization problem involved can be solved efficiently with eigenvalue decomposition. Experimental results on several datasets demonstrate the effectiveness of our method.

Mass diagnosis in mammography with mutual information based feature selection and support vector machine

Mass classification is an important problem in breast cancer diagnosis. In this paper, we investigated the classification of masses with feature selection. Based on the initial contour guided by radiologist, level set algorithm is used to deform the contour and achieves the final segmentation. Morphological features are extracted from the boundary of segmented regions. Then, important features are extracted based on mutual information criterion. Linear discriminant analysis and support vector machine are investigated for the final classification. Mammography images from DDSM were used for experiment. The method achieved an accuracy of 86.6% with mutual information based feature selection and SVM classifier. The experimental result shows that mutual information based feature selection is useful for the diagnosis of masses.

Distributed Workflow Service Composition Based on CTR Technology

Recently, WS-BPEL has gradually become the basis of a standard for web service description and composition. However, WS-BPEL cannot efficiently describe distributed workflow services for lacking of special expressive power and formal semantics. This paper presents a novel method for modeling distributed workflow service composition with Concurrent TRansaction logic (CTR). The syntactic structure of WS-BPEL and CTR are analyzed, and new rules of mapping WS-BPEL into CTR are given. A case study is put forward to show that the proposed method is appropriate for modeling workflow business services under distributed environments.