In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.Whe...In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.展开更多
Objective： The aim of this study was to investigate the value of multi-slice spiral CT （MSCT） in the diagnosis of malignant gastrointestinal stromal tumors （GISTs）. Methods： Twenty-seven cases of MSCT images of ...Objective： The aim of this study was to investigate the value of multi-slice spiral CT （MSCT） in the diagnosis of malignant gastrointestinal stromal tumors （GISTs）. Methods： Twenty-seven cases of MSCT images of malignant GIST proved by surgery and pathology were retrospectively analyzed. Both plain and enhanced CT scan was performed and multiplanar reconstruction was made in all cases. Results： The lesions originated from the stomach （n = 11）, small intestine （n = 9）, colon （n = 4）, rectum （n = 1）, and mesentery （n = 2）. The transverse diameters of mass were 4.2-22 cm, the edges clearly （n = 12）, unclearly （n = 15）. The mass were mainly irregular in shape Iobulated （n = 19）. The lesions were mainly heterogeneity on plain scan, moderate to marked enhancement in arterial phase and durative enhanced in venous phase. Cystic necrosis were observed in all the lesions, 9 cases were cystic and solid mixed mass. Hepatic metastases （n = 4）, pulmonary metastasis （n = 1）, lymphatic metastasis （n = 2） were detected. The accuracy rate of MSCT diagnosis for location and pathologic features of GISTs were 85.2% （23/27） and 77.8% （21/27）. Conclusion： Two-phase MSCT examination and axial images combined with multiplanar reconstruction images have important value for diagnosis of malignant GIST.展开更多
These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means （SSKFCM） algorithm is proposed to over...These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means （SSKFCM） algorithm is proposed to overcome these disadvantages of remote sensing image classification in this paper. The SSKFCM algorithm is achieved by introducing a kernel method and semi-supervised learning technique into the standard fuzzy C-means （FCM） algorithm. A set of Beijing-1 micro-satellite＇s multispectral images are adopted to be classified by several algorithms, such as FCM, kernel FCM （KFCM）, semi-supervised FCM （SSFCM） and SSKFCM. The classification results are estimated by corresponding indexes. The results indicate that the SSKFCM algorithm significantly improves the classification accuracy of remote sensing images compared with the others.展开更多
There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling ...There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.展开更多
基金Science and Technology Plan of Gansu Province(No.144NKCA040)
文摘In order to improve the global search ability of biogeography-based optimization(BBO)algorithm in multi-threshold image segmentation,a multi-threshold image segmentation based on improved BBO algorithm is proposed.When using BBO algorithm to optimize threshold,firstly,the elitist selection operator is used to retain the optimal set of solutions.Secondly,a migration strategy based on fusion of good solution and pending solution is introduced to reduce premature convergence and invalid migration of traditional migration operations.Thirdly,to reduce the blindness of traditional mutation operations,a mutation operation through binary computation is created.Then,it is applied to the multi-threshold image segmentation of two-dimensional cross entropy.Finally,this method is used to segment the typical image and compared with two-dimensional multi-threshold segmentation based on particle swarm optimization algorithm and the two-dimensional multi-threshold image segmentation based on standard BBO algorithm.The experimental results show that the method has good convergence stability,it can effectively shorten the time of iteration,and the optimization performance is better than the standard BBO algorithm.
文摘Objective： The aim of this study was to investigate the value of multi-slice spiral CT （MSCT） in the diagnosis of malignant gastrointestinal stromal tumors （GISTs）. Methods： Twenty-seven cases of MSCT images of malignant GIST proved by surgery and pathology were retrospectively analyzed. Both plain and enhanced CT scan was performed and multiplanar reconstruction was made in all cases. Results： The lesions originated from the stomach （n = 11）, small intestine （n = 9）, colon （n = 4）, rectum （n = 1）, and mesentery （n = 2）. The transverse diameters of mass were 4.2-22 cm, the edges clearly （n = 12）, unclearly （n = 15）. The mass were mainly irregular in shape Iobulated （n = 19）. The lesions were mainly heterogeneity on plain scan, moderate to marked enhancement in arterial phase and durative enhanced in venous phase. Cystic necrosis were observed in all the lesions, 9 cases were cystic and solid mixed mass. Hepatic metastases （n = 4）, pulmonary metastasis （n = 1）, lymphatic metastasis （n = 2） were detected. The accuracy rate of MSCT diagnosis for location and pathologic features of GISTs were 85.2% （23/27） and 77.8% （21/27）. Conclusion： Two-phase MSCT examination and axial images combined with multiplanar reconstruction images have important value for diagnosis of malignant GIST.
基金Supported by the National High Technology Research and Development Programme （No.2007AA12Z227） and the National Natural Science Foundation of China （No.40701146）.
文摘These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means （SSKFCM） algorithm is proposed to overcome these disadvantages of remote sensing image classification in this paper. The SSKFCM algorithm is achieved by introducing a kernel method and semi-supervised learning technique into the standard fuzzy C-means （FCM） algorithm. A set of Beijing-1 micro-satellite＇s multispectral images are adopted to be classified by several algorithms, such as FCM, kernel FCM （KFCM）, semi-supervised FCM （SSFCM） and SSKFCM. The classification results are estimated by corresponding indexes. The results indicate that the SSKFCM algorithm significantly improves the classification accuracy of remote sensing images compared with the others.
基金supported by the National Natural Science Foundation of China(Grant Nos.41272359&11001019)the Specialized Research Fund for the Doctoral Program of Higher Education(SRFDP)the Fundamental Research Funds for the Central Universities
文摘There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.
