Microcalcification clusters in mammograms are an important early sign of breast cancer. The enhancement of mieroealcifications in mammograms is one of the most important preprocessing techniques for the extraction of ...Microcalcification clusters in mammograms are an important early sign of breast cancer. The enhancement of mieroealcifications in mammograms is one of the most important preprocessing techniques for the extraction of cluster mierocalcifications. In this paper, we present a novel method for the enhancement of microcalcifications. Firstly, the initial microcaleification edges were extracted by using kirsch edge operator, and the diseontinouse edges were linked by employing fi＇aetal teehnique, Then, the continuous closed edges of microcalcifications were filled by using seed filling algorithm. The pixel values of the filled region were replaced by the corresponding pixel values in the original image. Finally, the enhancement of microcalcifications in mammograms was achieved by adding the filled image to the original image. We evaluated the performance of our algorithm by using 50 regions of interesting （ROIs） with microcalcification clusters from DDSM database. The experiment results demonstrate that our CAD system can give better enhancement effect compared with other methods.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
基金National Natural Science Foundation of China grant number: 30971019
文摘Microcalcification clusters in mammograms are an important early sign of breast cancer. The enhancement of mieroealcifications in mammograms is one of the most important preprocessing techniques for the extraction of cluster mierocalcifications. In this paper, we present a novel method for the enhancement of microcalcifications. Firstly, the initial microcaleification edges were extracted by using kirsch edge operator, and the diseontinouse edges were linked by employing fi＇aetal teehnique, Then, the continuous closed edges of microcalcifications were filled by using seed filling algorithm. The pixel values of the filled region were replaced by the corresponding pixel values in the original image. Finally, the enhancement of microcalcifications in mammograms was achieved by adding the filled image to the original image. We evaluated the performance of our algorithm by using 50 regions of interesting （ROIs） with microcalcification clusters from DDSM database. The experiment results demonstrate that our CAD system can give better enhancement effect compared with other methods.
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.
