scholarly journals The Research of Feature Extraction Method of Liver Pathological Image Based on Multispatial Mapping and Statistical Properties

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Huiling Liu ◽  
Huiyan Jiang ◽  
Bingbing Xia ◽  
Dehui Yi
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Texture Features ◽  
Classification Performance ◽  
Statistical Properties ◽  
Feature Extraction Method ◽  
The Difference ◽  
New Feature ◽  
Value Changes ◽  
Pathological Image

We propose a new feature extraction method of liver pathological image based on multispatial mapping and statistical properties. For liver pathological images of Hematein Eosin staining, the image of R and B channels can reflect the sensitivity of liver pathological images better, while the entropy space and Local Binary Pattern (LBP) space can reflect the texture features of the image better. To obtain the more comprehensive information, we map liver pathological images to the entropy space, LBP space, R space, and B space. The traditional Higher Order Local Autocorrelation Coefficients (HLAC) cannot reflect the overall information of the image, so we propose an average correction HLAC feature. We calculate the statistical properties and the average gray value of pathological images and then update the current pixel value as the absolute value of the difference between the current pixel gray value and the average gray value, which can be more sensitive to the gray value changes of pathological images. Lastly the HLAC template is used to calculate the features of the updated image. The experiment results show that the improved features of the multispatial mapping have the better classification performance for the liver cancer.

Simulation Research of Feature Extraction Method Based on Nonlinear Manifold Learning

2014 ◽  
Vol 533 ◽  
pp. 247-251
Author(s):  
Hai Bing Xiao ◽  
Xiao Peng Xie
Keyword(s):  
Feature Extraction ◽  
Manifold Learning ◽  
Extraction Method ◽  
Classification Performance ◽  
Locally Linear Embedding ◽  
Simulation Research ◽  
Feature Extraction Method ◽  
Linear Embedding ◽  
Nonlinear Feature Extraction ◽  
Nonlinear Feature

This paper deals with the study of Locally Linear Embedding (LLE) and Hessian LLE nonlinear feature extraction for high dimensional data dimension reduction. LLE and Hessian LLE algorithm which reveals the characteristics of nonlinear manifold learning were analyzed. LLE and Hessian LLE algorithm simulation research was studied through different kinds of sample for dimensionality reduction. LLE and Hessian LLE algorithm’s classification performance was compared in accordance with MDS. The simulation experimental results show that LLE and Hessian LLE are very effective feature extraction method for nonlinear manifold learning.

scholarly journals A new feature extraction method for signal classification applied to cord dorsum potential detection

2012 ◽  
Vol 9 (5) ◽  
pp. 056009 ◽  
Author(s):  
D Vidaurre ◽  
E E Rodríguez ◽  
C Bielza ◽  
P Larrañaga ◽  
P Rudomin
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Signal Classification ◽  
Feature Extraction Method ◽  
New Feature

A new feature extraction method for odour classification

2011 ◽  
Vol 158 (1) ◽  
pp. 75-88 ◽  
Author(s):  
Bernd Ehret ◽  
Konstantin Safenreiter ◽  
Frank Lorenz ◽  
Joachim Biermann
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Feature Extraction Method ◽  
New Feature

scholarly journals Near-field Sensors with Machine Learning for Breast Tumor Detection

2018 ◽  
Vol 4 (1) ◽  
pp. 3
Author(s):  
Maged A. Aldhaeebi ◽  
Thamer S. Almoneef ◽  
Omar M. Ramahi
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Breast Tumor ◽  
Extraction Method ◽  
Near Field ◽  
Tumor Detection ◽  
Electromagnetic Properties ◽  
Feature Extraction Method ◽  
The Difference ◽  
Breast Tissues

In this work, we propose the use of an electrically small novel antenna as a probe combined with a classification algorithm for nearfield microwave breast tumor detection. The resonant probe ishighly sensitive to the changes in the electromagnetic properties of the breast tissues such that the presence of the tumor is estimatedby determining the changes in the magnitude and phase responseof the reflection coefficient of the sensor. The Principle Component placed at the middle of the probe as shown in Fig. 1. The mainAnalysis (PCA) feature extraction method is applied to emphasize the difference in the probe responses for both the healthy and thetumourous cases . We show that when a numerical realistic breast with and without tumor cells is placed in the near field of the probe, the probe is capable of distinguishing between healthy and tumorous tissue. In addition, the probe is able to identify tumors with various sizes placed in single locations.

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