scholarly journals A Texture Analysis Approach to Corrosion Image Classification

1996 ◽  
Vol 7 (2) ◽  
pp. 143-152 ◽  
Author(s):  
Stefan Livens ◽  
Paul Scheunders ◽  
Gert Van de Wouwer ◽  
Dirk Van Dyck ◽  
Hilde Smets ◽  
...  
Keyword(s):  
Image Classification ◽  
Texture Analysis ◽  
Analysis Approach

A texture analysis approach to quantify ventilation changes in hyperpolarised 3 He MRI of the rat lung in an asthma model

2011 ◽  
Vol 25 (1) ◽  
pp. 131-141 ◽  
Author(s):  
Frank Risse ◽  
Jelena Pesic ◽  
Simon Young ◽  
Lars E. Olsson
Keyword(s):  
Texture Analysis ◽  
Analysis Approach ◽  
Rat Lung ◽  
Asthma Model

Image classification of vaginal microecology detection based on gabor texture and LSTM model

2021 ◽  
pp. 1-18
Author(s):  
Gaoteng Yuan ◽  
Yinping Dong ◽  
Xiaofeng Zhou
Keyword(s):  
Image Classification ◽  
Texture Analysis ◽  
Short Term Memory ◽  
Imbalanced Data ◽  
Region Of Interest ◽  
Texture Features ◽  
Image Features ◽  
Imbalanced Data Sets ◽  
Area Of Interest ◽  
Gynecological Diseases

BACKGROUND: Gynecological diseases threaten women’s health, and vaginal microecological testing is a common method for detecting gynecological diseases. Efficient and accurate microecological testing methods have always been the goal pursued by gynecologists. OBJECTIVE: In order to automatically identify different types of microbial images in vaginal micromorphology detection, this paper proposes a vaginal microecological image recognition method based on Gabor texture analysis combined with long and short-term memory network (LSTM) model. METHOD: Firstly, we denoise the microecological morphological im-ages, which selects the area of interest and sets the label of the microorganism according to the doctors label. Secondly, texture analysis is carried out for the region of interest, which uses Gabor filters with 8 directions and 5 scales to filter the region of interest to extract the texture features on the image. Comparing the differences between different microbial image features, and screening suitable features to reduce the number of features. Then, we design an LSTM model to analyze the relationship of image features in different categories of microorganisms. Finally, we use the full connection layer and Softmax function to realize the automatic recognition of different microbial images. RESULTS: The experimental results show that the image classification accuracy of 8 common microorganisms is 81.26%. CONCLUSION: Texture analysis combined with LSTM network strategy can identify different kinds of vaginal micro ecological images. Gabor-LSTM model has better classification effect on imbalanced data sets.

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