Intensity-vesselness Gaussian mixture model (IVGMM) for 2D + t segmentation of coronary arteries for X-ray angiography image sequences

2015 ◽  
Vol 23 (5) ◽  
pp. 579-592 ◽  
Author(s):  
Hyunna Lee ◽  
Hackjoon Shim ◽  
Hyuk-Jae Chang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Coronary Arteries ◽  
Gaussian Mixture ◽  
Image Sequences ◽  
X Ray

scholarly journals Characterization of hydrothermal alteration along geothermal wells using unsupervised machine-learning analysis of X-ray powder diffraction data

2021 ◽  
Author(s):  
Kazuya Ishitsuka ◽  
Hiroki Ojima ◽  
Toru Mogi ◽  
Tatsuya Kajiwara ◽  
Takeshi Sugimoto ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Hydrothermal Alteration ◽  
Powder Diffraction ◽  
Gaussian Mixture ◽  
Geothermal Field ◽  
Classification Algorithms ◽  
Agglomerative Clustering ◽  
X Ray ◽  
Alteration Minerals

AbstractZonal distribution of hydrothermal alteration in and around geothermal fields is important for understanding the hydrothermal environment. In this study, we assessed the performance of three unsupervised classification algorithms—K-mean clustering, the Gaussian mixture model, and agglomerative clustering—in automated categorization of alteration minerals along wells. As quantitative data for classification, we focused on the quartz indices of alteration minerals obtained from rock cuttings, which were calculated from X-ray powder diffraction measurements. The classification algorithms were first examined by applying synthetic data and then applied to data on rock cuttings obtained from two wells in the Hachimantai geothermal field in Japan. Of the three algorithms, our results showed that the Gaussian mixture model provides classes that are reliable and relatively easy to interpret. Furthermore, an integrated interpretation of different classification results provided more detailed features buried within the quartz indices. Application to the Hachimantai geothermal field data showed that lithological boundaries underpin the data and revealed the lateral connection between wells. The method’s performance is underscored by its ability to interpret multi-component data related to quartz indices.

scholarly journals Classification of pulsars with Dirichlet process Gaussian mixture model

2020 ◽  
Vol 493 (1) ◽  
pp. 713-722
Author(s):  
Fahrettin Ay ◽  
Gökhan İnce ◽  
Mustafa E Kamaşak ◽  
K Yavuz Ekşi
Keyword(s):  
Neutron Stars ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Dirichlet Process ◽  
Binary Systems ◽  
Gamma Ray ◽  
Transverse Velocity ◽  
Gaussian Mixture ◽  
Compact Objects ◽  
X Ray

ABSTRACT Young isolated neutron stars (INSs) most commonly manifest themselves as rotationally powered pulsars that involve conventional radio pulsars as well as gamma-ray pulsars and rotating radio transients. Some other young INS families manifest themselves as anomalous X-ray pulsars and soft gamma-ray repeaters that are commonly accepted as magnetars, i.e. magnetically powered neutron stars with decaying super-strong fields. Yet some other young INSs are identified as central compact objects and X-ray dim isolated neutron stars that are cooling objects powered by their thermal energy. Older pulsars, as a result of a previous long episode of accretion from a companion, manifest themselves as millisecond pulsars and more commonly appear in binary systems. We use Dirichlet process Gaussian mixture model (DPGMM), an unsupervised machine learning algorithm, for analysing the distribution of these pulsar families in the parameter space of period and period derivative. We compare the average values of the characteristic age, magnetic dipole field strength, surface temperature, and transverse velocity of all discovered clusters. We verify that DPGMM is robust and provide hints for inferring relations between different classes of pulsars. We discuss the implications of our findings for the magnetothermal spin evolution models and fallback discs.

Point Cloud Registration Algorithm Combined Gaussian Mixture Model and Point-to-Plane Metric

2018 ◽  
Vol 30 (4) ◽  
pp. 642
Author(s):  
Guichao Lin ◽  
Yunchao Tang ◽  
Xiangjun Zou ◽  
Qing Zhang ◽  
Xiaojie Shi ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Point Cloud ◽  
Gaussian Mixture ◽  
Point Cloud Registration ◽  
Registration Algorithm
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