Background: Despite common endothelial origins, angiosarcoma and Kaposi’ s sarcoma are clinically and histologically distinct vascular proliferations. The development of angiosarcoma in a chronically edematous abdomi...Background: Despite common endothelial origins, angiosarcoma and Kaposi’ s sarcoma are clinically and histologically distinct vascular proliferations. The development of angiosarcoma in a chronically edematous abdominal pannus is extremely uncommon. Similarly, tumors with the combined histologic features of angiosarcoma and Kaposi’ s sarcoma have rarely been described. Methods: We reviewed the literature on angiosarcoma arising in a lymphedematous abdominal pannus and evaluated an 81-year-old morbidly obese woman who had profound, long-standing edema of the lower abdominal wall in which an aggressive vascular tumor developed. Results: Three clinically similar cases were identified in the literature. All patients were women who generally experienced rapid disease progression. In addition, in our patient, sequential cutaneous sampling from different lesional sites demonstrated disparate histologic changes, ranging from those of classic Kaposi’ s sarcoma to high-grade angiosarcoma, to areas with combined features of the two tumors. A polymerase chain reaction performed on lesional tissue was negative for human herpesvirus-8 DNA. Conclusion: It is important to note that angiosarcoma may develop in the abdomen in association with chronic lymphedema, as demonstrated by the cases noted in this report. In addition, our case highlights the difficulty in differentiating histologically angiosarcoma from Kaposi’ s sarcoma in some situations, and demonstrates the value of close clinicopathologic correlation and sequential tissue sampling in evaluating problematic cases.展开更多
[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored...[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.展开更多
Using function approximation technology and principal component analysis method, this paper presents a principal component feature to solve the time alignment problem and to simplify the structure of neural network. I...Using function approximation technology and principal component analysis method, this paper presents a principal component feature to solve the time alignment problem and to simplify the structure of neural network. Its extraction simulates the processing of speech information in human auditory system. The experimental results show that the principal component feature based recognition system outperforms the standard CDHMM and GMDS method in many aspects.展开更多
Variety identification is important for maize breeding, processing and trade. The computer vision technique has been widely applied to maize variety identification. In this paper, computer vision technique has been su...Variety identification is important for maize breeding, processing and trade. The computer vision technique has been widely applied to maize variety identification. In this paper, computer vision technique has been summarized from the following technical aspects including image acquisition, image processing, characteristic parameter extraction, pattern recognition and programming softwares. In addition, the existing problems during the application of this technique to maize variety identification have also been analyzed and its development tendency is forecasted.展开更多
文摘Background: Despite common endothelial origins, angiosarcoma and Kaposi’ s sarcoma are clinically and histologically distinct vascular proliferations. The development of angiosarcoma in a chronically edematous abdominal pannus is extremely uncommon. Similarly, tumors with the combined histologic features of angiosarcoma and Kaposi’ s sarcoma have rarely been described. Methods: We reviewed the literature on angiosarcoma arising in a lymphedematous abdominal pannus and evaluated an 81-year-old morbidly obese woman who had profound, long-standing edema of the lower abdominal wall in which an aggressive vascular tumor developed. Results: Three clinically similar cases were identified in the literature. All patients were women who generally experienced rapid disease progression. In addition, in our patient, sequential cutaneous sampling from different lesional sites demonstrated disparate histologic changes, ranging from those of classic Kaposi’ s sarcoma to high-grade angiosarcoma, to areas with combined features of the two tumors. A polymerase chain reaction performed on lesional tissue was negative for human herpesvirus-8 DNA. Conclusion: It is important to note that angiosarcoma may develop in the abdomen in association with chronic lymphedema, as demonstrated by the cases noted in this report. In addition, our case highlights the difficulty in differentiating histologically angiosarcoma from Kaposi’ s sarcoma in some situations, and demonstrates the value of close clinicopathologic correlation and sequential tissue sampling in evaluating problematic cases.
基金Supported by the National Natural Science Foundation of China(31101085)the Program for Young Core Teachers of Colleges in Henan(2011GGJS-094)the Scientific Research Project for the High Level Talents,North China University of Water Conservancy and Hydroelectric Power~~
文摘[Objective] The aim was to study the feature extraction of stored-grain insects based on ant colony optimization and support vector machine algorithm, and to explore the feasibility of the feature extraction of stored-grain insects. [Method] Through the analysis of feature extraction in the image recognition of the stored-grain insects, the recognition accuracy of the cross-validation training model in support vector machine （SVM） algorithm was taken as an important factor of the evaluation principle of feature extraction of stored-grain insects. The ant colony optimization （ACO） algorithm was applied to the automatic feature extraction of stored-grain insects. [Result] The algorithm extracted the optimal feature subspace of seven features from the 17 morphological features, including area and perimeter. The ninety image samples of the stored-grain insects were automatically recognized by the optimized SVM classifier, and the recognition accuracy was over 95%. [Conclusion] The experiment shows that the application of ant colony optimization to the feature extraction of grain insects is practical and feasible.
文摘Using function approximation technology and principal component analysis method, this paper presents a principal component feature to solve the time alignment problem and to simplify the structure of neural network. Its extraction simulates the processing of speech information in human auditory system. The experimental results show that the principal component feature based recognition system outperforms the standard CDHMM and GMDS method in many aspects.
基金Special Fund for Science & Technology Research of Education Commission,Chongqing(KJ101302)~~
文摘Variety identification is important for maize breeding, processing and trade. The computer vision technique has been widely applied to maize variety identification. In this paper, computer vision technique has been summarized from the following technical aspects including image acquisition, image processing, characteristic parameter extraction, pattern recognition and programming softwares. In addition, the existing problems during the application of this technique to maize variety identification have also been analyzed and its development tendency is forecasted.
