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.

scholarly journals scHiCSRS: A Self-Representation Smoothing Method with Gaussian Mixture Model for Imputing single-cell Hi-C Data

2021 ◽  
Author(s):  
Shili Lin ◽  
Qing Xie
Keyword(s):  
Single Cell ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Single Cells ◽  
High Sensitivity ◽  
Gaussian Mixture ◽  
Smoothing Method ◽  
Intermediate Step ◽  
Downstream Analysis ◽  
Structural Zeros

Motivation: Single-cell Hi-C techniques make it possible to study cell-to-cell variability in genomic features. However, excess zeros are commonly seen in single-cell Hi-C (scHi-C) data, making scHi-C matrices extremely sparse and bringing extra difficulties in downstream analysis. The observed zeros are a combination of two events: structural zeros for which the loci never inter- act due to underlying biological mechanisms, and dropouts or sampling zeros where the two loci interact but are not captured due to insufficient sequencing depth. Although quality improvement approaches have been proposed as an intermediate step for analyzing scHi-C data, little has been done to address these two types of zeros. We believe that differentiating between structural zeros and dropouts would benefit downstream analysis such as clustering. Results: We propose scHiCSRS, a self-representation smoothing method that improves the data quality, and a Gaussian mixture model that identifies structural zeros among observed zeros. scHiCSRS not only takes spatial dependencies of a scHi-C 2D data structure into account but also borrows information from similar single cells. Through an extensive set of simulation studies, we demonstrate the ability of scHiCSRS for identifying structural zeros with high sensitivity and for accurate imputation of dropout values in sampling zeros. Downstream analysis for three real datasets show that data improved from scHiCSRS yield more accurate clustering of cells than simply using observed data or improved data from several comparison methods.

Traffic Image Segmentation Based on Gaussian Mixture Model with Spatial Information and Sampling

2013 ◽  
Vol 380-384 ◽  
pp. 3702-3705
Author(s):  
Xiao Na Zhang ◽  
Ming Yao ◽  
Feng Zhu ◽  
Jie Ni
Keyword(s):  
Image Segmentation ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Spatial Information ◽  
Gaussian Mixture ◽  
Sampling Theorem ◽  
Information Function ◽  
Cluster Number ◽  
Minimum Sample ◽  
Image Pixels

The application of classical gaussian mixture model to image segmentation has highly computer complexiton and have not taking into account spatial information except intensity values. A image segmentation based on Gaussian mixture model with sampling and spatially information is proposed in order to solve this problem. First, a spatial information function is defined as the neighbour information weighted class probabilities of very pixels; Secondly, the sampling theorem is given in this paper,and the size of the minimum sample has been derived according to the smallest cluster and cluster number; Finally, image pixels are sampled based on the size of the minimum sample to estimate the parameter of model , which are classifed to different clusters according to bayesian rules. The experimental results show the effectiveness of the algorithm.

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