scholarly journals A minimum spanning forest based classification method for dedicated breast CT images

2015 ◽  
Vol 42 (11) ◽  
pp. 6190-6202 ◽  
Author(s):  
Robert Pike ◽  
Ioannis Sechopoulos ◽  
Baowei Fei
Keyword(s):  
Ct Images ◽  
Classification Method ◽  
Breast Ct ◽  
Spanning Forest ◽  
Dedicated Breast Ct ◽  
Minimum Spanning Forest

scholarly journals Non-rigid registration of serial dedicated breast CT, longitudinal dedicated breast CT and PET/CT images using the diffeomorphic demons method

2014 ◽  
Vol 30 (6) ◽  
pp. 713-717 ◽  
Author(s):  
Jonathan Santos ◽  
Abhijit J. Chaudhari ◽  
Anand A. Joshi ◽  
Andrea Ferrero ◽  
Kai Yang ◽  
...  
Keyword(s):  
Ct Images ◽  
Breast Ct ◽  
Rigid Registration ◽  
Pet Ct ◽  
Dedicated Breast Ct

scholarly journals Cupping artifact correction and automated classification for high-resolution dedicated breast CT images

2012 ◽  
Vol 39 (10) ◽  
pp. 6397-6406 ◽  
Author(s):  
Xiaofeng Yang ◽  
Shengyong Wu ◽  
Ioannis Sechopoulos ◽  
Baowei Fei
Keyword(s):  
High Resolution ◽  
Ct Images ◽  
Automated Classification ◽  
Artifact Correction ◽  
Breast Ct ◽  
Dedicated Breast Ct

scholarly journals Experimental measurement of radiation dose in a dedicated breast CT system

2014 ◽  
Vol 38 (3) ◽  
pp. 038201
Author(s):  
Shan-Wei Shen ◽  
Yan-Fang Wang ◽  
Hang Shu ◽  
Xiao Tang ◽  
Cun-Feng Wei ◽  
...  
Keyword(s):  
Radiation Dose ◽  
Experimental Measurement ◽  
Breast Ct ◽  
Dedicated Breast Ct ◽  
Ct System

scholarly journals ConnectIt

2020 ◽  
Vol 14 (4) ◽  
pp. 653-667
Author(s):  
Laxman Dhulipala ◽  
Changwan Hong ◽  
Julian Shun
Keyword(s):  
Experimental Evaluation ◽  
Comprehensive Evaluation ◽  
State Of The Art ◽  
Graph Connectivity ◽  
Connected Components ◽  
Sampling Strategies ◽  
Spanning Forest ◽  
Speed Up ◽  
Minimum Spanning Forest ◽  
Edge Sampling

Connected components is a fundamental kernel in graph applications. The fastest existing multicore algorithms for solving graph connectivity are based on some form of edge sampling and/or linking and compressing trees. However, many combinations of these design choices have been left unexplored. In this paper, we design the ConnectIt framework, which provides different sampling strategies as well as various tree linking and compression schemes. ConnectIt enables us to obtain several hundred new variants of connectivity algorithms, most of which extend to computing spanning forest. In addition to static graphs, we also extend ConnectIt to support mixes of insertions and connectivity queries in the concurrent setting. We present an experimental evaluation of ConnectIt on a 72-core machine, which we believe is the most comprehensive evaluation of parallel connectivity algorithms to date. Compared to a collection of state-of-the-art static multicore algorithms, we obtain an average speedup of 12.4x (2.36x average speedup over the fastest existing implementation for each graph). Using ConnectIt, we are able to compute connectivity on the largest publicly-available graph (with over 3.5 billion vertices and 128 billion edges) in under 10 seconds using a 72-core machine, providing a 3.1x speedup over the fastest existing connectivity result for this graph, in any computational setting. For our incremental algorithms, we show that our algorithms can ingest graph updates at up to several billion edges per second. To guide the user in selecting the best variants in ConnectIt for different situations, we provide a detailed analysis of the different strategies. Finally, we show how the techniques in ConnectIt can be used to speed up two important graph applications: approximate minimum spanning forest and SCAN clustering.

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