2013 IEEE International Conference on Granular Computing (GrC)最新文献

Predicting tumor location from prone to supine breast MRI using a simulation of breast deformation 利用模拟乳房变形的乳房MRI预测从俯卧到仰卧的肿瘤位置

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740419

Hong Song, Xiangbin Zhu, Xiangfei Cui

{"title":"Predicting tumor location from prone to supine breast MRI using a simulation of breast deformation","authors":"Hong Song, Xiangbin Zhu, Xiangfei Cui","doi":"10.1109/GrC.2013.6740419","DOIUrl":"https://doi.org/10.1109/GrC.2013.6740419","url":null,"abstract":"Breast cancer is one of the biggest killers to women, and early diagnosis is essential for improved prognosis. The shape of the breast varies hugely between the scenarios of magnetic resonance (MR) imaging (patient lies prone, breast hanging down under gravity) and ultrasound (patient lies supine). Matching between such pairs of images is considered essential by radiologists for more reliable diagnosis of early breast cancer. In this paper, a method to predict tumor location by simulating the breast deformation from breast in the prone position to the compressed breast in the supine position was developed, which is based on a 3-D patient-specific breast model constructed from MR images with the use of the finite-element method and nonlinear elasticity. The performance was assessed by the mean distance between corresponding lesion locations for three cases. A mean accuracy of 4.94mm in Euclidean distance was achieved by using the proposed method. Experiments using actual images show that the method gives good predictions which can be used to find exact correspondences between tumors location in prone and supine breast images.","PeriodicalId":415445,"journal":{"name":"2013 IEEE International Conference on Granular Computing (GrC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116987335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Protein function prediction based on physiochemical properties and protein granularity 基于理化性质和蛋白质粒度的蛋白质功能预测

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740433

Wanlu Wang, Xin Zhang, Jun Meng, Yushi Luan

引用次数: 3

Multi-agent comprehensive evaluation on inland port production efficiency based on arrtibute theory 基于属性理论的内陆港口生产效率多主体综合评价

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740386

Xueyan Duan, Siqin Yu, Guanglin Xu

引用次数: 0

Definability of approximations in reflexive relations 自反关系中近似的可定义性

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740421

Yu-Ru Syau, Lixing Jia, E. Lin

引用次数: 1

Knowledge mining in big data — A lesson from algebraic geometry 大数据中的知识挖掘——代数几何的一个教训

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740437

Jun Xie, Zehua Chen, Gang Xie, T. Lin

引用次数: 4

Specific-to-general approach for rule induction using discernibility based dissimilarity 使用基于差异性的可辨性进行规则归纳的从特定到一般的方法

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740403

Y. Kusunoki, T. Tanino

引用次数: 0

GN: A privacy preserving data publishing method based on generalization and noise techniques GN:一种基于泛化和噪声技术的隐私保护数据发布方法

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740411

Yeling Ma, Jiyi Wang, Jianmin Han, Lixia Wang

引用次数: 0

Multi-features prostate tumor aided diagnoses based on ensemble-svm 基于集合支持向量机的前列腺肿瘤多特征辅助诊断

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740425

T. Zhou, Huiling Lu

引用次数: 6

Algorithms of crisp, fuzzy, and probabilistic clustering with semi-supervision or pairwise constraints 具有半监督或成对约束的清晰、模糊和概率聚类算法

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740412

S. Miyamoto, Nobuhiro Obara

引用次数: 2

Scale selection in roughness based color quantization 基于粗糙度颜色量化的尺度选择

2013 IEEE International Conference on Granular Computing (GrC) Pub Date : 2013-12-01 DOI: 10.1109/GrC.2013.6740446

Xiaodong Yue, D. Miao, Yue Wu, Caiming Zhong, Yufei Chen

{"title":"Scale selection in roughness based color quantization","authors":"Xiaodong Yue, D. Miao, Yue Wu, Caiming Zhong, Yufei Chen","doi":"10.1109/GrC.2013.6740446","DOIUrl":"https://doi.org/10.1109/GrC.2013.6740446","url":null,"abstract":"Color quantization is an important operation with many applications in image compression and image analysis. Through color quantization, millions of colors in original images are compressed to a limited palette while guaranteeing the display quality. Synthesizing color spatial distribution into the traditional histogram, rough set theory is utilized to design the roughness measure for color quantization. Although the superiority of the roughness-based color quantization has been proved, the basic roughness measure tends to over focus on the homogeneity of noisy points and is still not precise enough to represent the homogeneous color regions. To weaken the interference of noise, we improve the existing roughness measure through filtering the local color differences with the linear scale-space kernel. The filtering process actually forms a group of multi-scale approximations of color components and leads to the multilevel roughness. Therefore it is required to decide the reasonable scales for roughness-based quantization. A strategy of scale selection based on the information measurement is also proposed in this paper, which uses the change of the generalized entropies in linear scale-spaces to interpret the varying region homogeneity and detect the optimal scales for color quantization. Abundant experimental results demonstrate the validity of the scale selection strategy. Under the selected scales, the color quantization induced from the roughness measure has good performances on most testing images.","PeriodicalId":415445,"journal":{"name":"2013 IEEE International Conference on Granular Computing (GrC)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131323217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0