scholarly journals Research on Evolutionary Level Set Method and Gaussian Mixture Model Based Target Shape Design Optimization Problem

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 104096-104107
Author(s):  
Liangyue Jia ◽  
Jia Hao ◽  
Guoxin Wang ◽  
Yan Yan
Keyword(s):  
Design Optimization ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Level Set Method ◽  
Level Set ◽  
Optimization Problem ◽  
Gaussian Mixture ◽  
Shape Design ◽  
Target Shape ◽  
Evolutionary Level

scholarly journals Automated segmentation of intraretinal cystoid macular edema based on Gaussian mixture model

2019 ◽  
Vol 13 (01) ◽  
pp. 1950020
Author(s):  
Jinghong Wu ◽  
Sijie Niu ◽  
Qiang Chen ◽  
Wen Fan ◽  
Songtao Yuan ◽  
...  
Keyword(s):  
Optical Coherence Tomography ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Level Set ◽  
Spatial Information ◽  
High Sensitivity ◽  
Gaussian Mixture ◽  
Automated Segmentation ◽  
Potential Fluid ◽  
Sd Oct

We introduce a method based on Gaussian mixture model (GMM) clustering and level-set to automatically detect intraretina fluid on diabetic retinopathy (DR) from spectral domain optical coherence tomography (SD-OCT) images in this paper. First, each B-scan is segmented using GMM clustering. The original clustering results are refined using location and thickness information. Then, the spatial information among every consecutive five B-scans is used to search potential fluid. Finally, the improved level-set method is used to obtain the accurate boundaries. The high sensitivity and accuracy demonstrated here show its potential for detection of fluid.

Gaussian Mixture Model Clustering with Incomplete Data

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-14
Author(s):  
Yi Zhang ◽  
Miaomiao Li ◽  
Siwei Wang ◽  
Sisi Dai ◽  
Lei Luo ◽  
...  
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Incomplete Data ◽  
Optimization Problem ◽  
Gaussian Mixture ◽  
Practical Applications ◽  
Learning Procedure ◽  
Benchmark Datasets ◽  
Effectiveness And Efficiency ◽  
Mixture Model Clustering

Gaussian mixture model (GMM) clustering has been extensively studied due to its effectiveness and efficiency. Though demonstrating promising performance in various applications, it cannot effectively address the absent features among data, which is not uncommon in practical applications. In this article, different from existing approaches that first impute the absence and then perform GMM clustering tasks on the imputed data, we propose to integrate the imputation and GMM clustering into a unified learning procedure. Specifically, the missing data is filled by the result of GMM clustering, and the imputed data is then taken for GMM clustering. These two steps alternatively negotiate with each other to achieve optimum. By this way, the imputed data can best serve for GMM clustering. A two-step alternative algorithm with proved convergence is carefully designed to solve the resultant optimization problem. Extensive experiments have been conducted on eight UCI benchmark datasets, and the results have validated the effectiveness of the proposed algorithm.

Arbitrary oriented multilingual text detection and segmentation using level set and Gaussian mixture model

2020 ◽  
Author(s):  
H. T. Basavaraju ◽  
V. N. Manjunath Aradhya ◽  
M. S. Pavithra ◽  
D. S. Guru ◽  
Vikrant Bhateja
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Level Set ◽  
Gaussian Mixture ◽  
Text Detection ◽  
Multilingual Text
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