Characteristics of a massive training artificial neural network in the distinction between lung nodules and vessels in CT images

2004 ◽  
Vol 1268 ◽  
pp. 923-928 ◽  
Author(s):  
Kenji Suzuki ◽  
Kunio Doi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ct Images ◽  
Lung Nodules ◽  
Artificial Neural

Artificial neural network based classification of lung nodules in CT images using intensity, shape and texture features

2019 ◽  
Vol 10 (10) ◽  
pp. 4135-4149 ◽  
Author(s):  
Furqan Shaukat ◽  
Gulistan Raja ◽  
Rehan Ashraf ◽  
Shehzad Khalid ◽  
Mudassar Ahmad ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Texture Features ◽  
Ct Images ◽  
Lung Nodules ◽  
Artificial Neural

scholarly journals Artificial Neural Network Based Lung Cancer Detection for PET/CT Images

2017 ◽  
Vol 10 (42) ◽  
pp. 1-13 ◽  
Author(s):  
Punithavathy Kannuswami ◽  
Sumathi Poobal ◽  
M. M. Ramya ◽  
◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Lung Cancer ◽  
Artificial Neural Network ◽  
Cancer Detection ◽  
Ct Images ◽  
Pet Ct ◽  
Artificial Neural ◽  
Lung Cancer Detection

scholarly journals Detection of pore space in CT soil images using artificial neural networks

2010 ◽  
Vol 7 (4) ◽  
pp. 6173-6205
Author(s):  
M. G. Cortina-Januchs ◽  
J. Quintanilla-Dominguez ◽  
A. Vega-Corona ◽  
A. M. Tarquis ◽  
D. Andina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Large Scale ◽  
Pore Space ◽  
Ct Images ◽  
Solid Soil ◽  
Artificial Neural

Abstract. Computed Tomography (CT) images provide a non-invasive alternative for observing soil structures, particularly pore space. Pore space in soil data indicates empty or free space in the sense that no material is present there except fluids such as air, water, and gas. Fluid transport depends on where pore spaces are located in the soil, and for this reason, it is important to identify pore zones. The low contrast between soil and pore space in CT images presents a problem with respect to pore quantification. In this paper, we present a methodology that integrates image processing, clustering techniques and artificial neural networks, in order to classify pore space in soil images. Image processing was used for the feature extraction of images. Three clustering algorithms were implemented (K-means, fuzzy C-means, and self organizing maps) to segment images. The objective of clustering process is to find pixel groups of a similar grey level intensity and to organise them into more or less homogeneous groups. The segmented images are used for test a classifier. An artificial neural network is characterised by a great degree of modularity and flexibility, and it is very efficient for large-scale and generic pattern recognition applications. For these reasons, an artificial neural network was used to classify soil images into two classes (pore space and solid soil). Our methodology shows an alternative way to detect solid soil and pore space in CT images. The percentages of correct classifications of pore space of the total number of classifications among the tested images were 97.01%, 96.47% and 96.12%.

scholarly journals Detection of pore space in CT soil images using artificial neural networks

2011 ◽  
Vol 8 (2) ◽  
pp. 279-288 ◽  
Author(s):  
M. G. Cortina-Januchs ◽  
J. Quintanilla-Dominguez ◽  
A. Vega-Corona ◽  
A. M. Tarquis ◽  
D. Andina
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Large Scale ◽  
Pore Space ◽  
Ct Images ◽  
Solid Soil ◽  
Artificial Neural

Abstract. Computed Tomography (CT) images provide a non-invasive alternative for observing soil structures, particularly pore space. Pore space in soil data indicates empty or free space in the sense that no material is present there except fluids such as air, water, and gas. Fluid transport depends on where pore spaces are located in the soil, and for this reason, it is important to identify pore zones. The low contrast between soil and pore space in CT images presents a problem with respect to pore quantification. In this paper, we present a methodology that integrates image processing, clustering techniques and artificial neural networks, in order to classify pore space in soil images. Image processing was used for the feature extraction of images. Three clustering algorithms were implemented (K-means, Fuzzy C-means, and Self Organising Maps) to segment images. The objective of clustering process is to find pixel groups of a similar grey level intensity and to organise them into more or less homogeneous groups. The segmented images are used for test a classifier. An Artificial Neural Network is characterised by a great degree of modularity and flexibility, and it is very efficient for large-scale and generic pattern recognition applications. For these reasons, an Artificial Neural Network was used to classify soil images into two classes (pore space and solid soil). Our methodology shows an alternative way to detect solid soil and pore space in CT images. The percentages of correct classifications of pore space of the total number of classifications among the tested images were 97.01%, 96.47% and 96.12%.

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