A method is proposed to avoid complex computation in finding the region of interest （ROI） in a mammogram. In the method, the true negative region （TNR） definitely containing no microcalcification clusters （MCCs）...A method is proposed to avoid complex computation in finding the region of interest （ROI） in a mammogram. In the method, the true negative region （TNR） definitely containing no microcalcification clusters （MCCs） is screened out, thus obtaining ROIs, The strategy consists of three steps： （1） the mammogram is partitioned into a set of non-overlapping blocks with an equal size, and for each block, five statistical features are computed, （2） negative blocks are screened out by the threshold method through rough analyses, （3） the more accurate analysis is done by the cost-sensitive support vector machine to eliminate the block definitely containing no MCCs, Experimental results on real mammograms show that 81.71% of TNRs can be screened out by the proposed method.展开更多
Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonab...Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem.展开更多
文摘A method is proposed to avoid complex computation in finding the region of interest （ROI） in a mammogram. In the method, the true negative region （TNR） definitely containing no microcalcification clusters （MCCs） is screened out, thus obtaining ROIs, The strategy consists of three steps： （1） the mammogram is partitioned into a set of non-overlapping blocks with an equal size, and for each block, five statistical features are computed, （2） negative blocks are screened out by the threshold method through rough analyses, （3） the more accurate analysis is done by the cost-sensitive support vector machine to eliminate the block definitely containing no MCCs, Experimental results on real mammograms show that 81.71% of TNRs can be screened out by the proposed method.
基金Supported by the National Natural Science Foundation of China (No. 60771068)the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2007F248)
文摘Tensor representation is useful to reduce the overfitting problem in vector-based learning algorithm in pattern recognition.This is mainly because the structure information of objects in pattern analysis is a reasonable constraint to reduce the number of unknown parameters used to model a classifier.In this paper, we generalize the vector-based learning algorithm TWin Support Vector Machine(TWSVM) to the tensor-based method TWin Support Tensor Machines(TWSTM), which accepts general tensors as input.To examine the effectiveness of TWSTM, we implement the TWSTM method for Microcalcification Clusters(MCs) detection.In the tensor subspace domain, the MCs detection procedure is formulated as a supervised learning and classification problem, and TWSTM is used as a classifier to make decision for the presence of MCs or not.A large number of experiments were carried out to evaluate and compare the performance of the proposed MCs detection algorithm.By comparison with TWSVM, the tensor version reduces the overfitting problem.
