scholarly journals Segmentation of Lung Images using Region based Neural Networks

2018 ◽  
Vol 11 (4) ◽  
pp. 2037-2042 ◽  
Author(s):  
Z. Faizal Khan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Tomography ◽  
Region Growing ◽  
Ct Images ◽  
Second Step ◽  
Lung Region ◽  
Segmentation Accuracy ◽  
Segmentation Approach ◽  
Lung Ct

In this article, a neural network-based segmentation approach for CT lung images was proposed using the combination of Neural Networks and region growing which combines the regions of different pixels. The proposed approach expresses a method for segmenting the lung region from lung Computer Tomography (CT) images. This method is proposed to obtain an optimal segmented region. The first step begins by the process of finding the area which represents the lung region. In order to achieve this, the regions of all the pixel present in the entire image is grown. Second step is, the grown region values are given as input to the Echo state neural networks in order to obtain the segmented lung region. The proposed algorithm is trained and tested for 1,361 CT lung slices for the process of evaluating segmentation accuracy. An average of 98.50% is obtained as the segmentation accuracy for the input lung CT images.

Automated vessel segmentation in lung CT and CTA images via deep neural networks

2021 ◽  
pp. 1-15
Author(s):  
Wenjun Tan ◽  
Luyu Zhou ◽  
Xiaoshuo Li ◽  
Xiaoyu Yang ◽  
Yufei Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Vessel Segmentation ◽  
Multi Scale ◽  
Vascular Segmentation ◽  
Study Results ◽  
Lung Ct

BACKGROUND: The distribution of pulmonary vessels in computed tomography (CT) and computed tomography angiography (CTA) images of lung is important for diagnosing disease, formulating surgical plans and pulmonary research. PURPOSE: Based on the pulmonary vascular segmentation task of International Symposium on Image Computing and Digital Medicine 2020 challenge, this paper reviews 12 different pulmonary vascular segmentation algorithms of lung CT and CTA images and then objectively evaluates and compares their performances. METHODS: First, we present the annotated reference dataset of lung CT and CTA images. A subset of the dataset consisting 7,307 slices for training and 3,888 slices for testing was made available for participants. Second, by analyzing the performance comparison of different convolutional neural networks from 12 different institutions for pulmonary vascular segmentation, the reasons for some defects and improvements are summarized. The models are mainly based on U-Net, Attention, GAN, and multi-scale fusion network. The performance is measured in terms of Dice coefficient, over segmentation ratio and under segmentation rate. Finally, we discuss several proposed methods to improve the pulmonary vessel segmentation results using deep neural networks. RESULTS: By comparing with the annotated ground truth from both lung CT and CTA images, most of 12 deep neural network algorithms do an admirable job in pulmonary vascular extraction and segmentation with the dice coefficients ranging from 0.70 to 0.85. The dice coefficients for the top three algorithms are about 0.80. CONCLUSIONS: Study results show that integrating methods that consider spatial information, fuse multi-scale feature map, or have an excellent post-processing to deep neural network training and optimization process are significant for further improving the accuracy of pulmonary vascular segmentation.

Region-growing fully convolutional neural network interactive segmentation of liver CT images

2021 ◽  
Vol 36 (9) ◽  
pp. 1294-1304
Author(s):  
Li-juan ZHANG ◽  
◽  
Run ZHANG ◽  
Dong-ming LI ◽  
Yang LI ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Region Growing ◽  
Ct Images ◽  
Interactive Segmentation ◽  
Liver Ct

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%.

scholarly journals Computer Tools to Analyze Lung CT Changes after Radiotherapy

2021 ◽  
Vol 11 (4) ◽  
pp. 1582
Author(s):  
Marek Konkol ◽  
Konrad Śniatała ◽  
Paweł Śniatała ◽  
Szymon Wilk ◽  
Beata Baczyńska ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Digital Imaging ◽  
Input Data ◽  
Ct Images ◽  
Comprehensive Analysis ◽  
Distribution Models ◽  
Computer Tool ◽  
Computer Tools ◽  
Lung Ct

The paper describes a computer tool dedicated to the comprehensive analysis of lung changes in computed tomography (CT) images. The correlation between the dose delivered during radiotherapy and pulmonary fibrosis is offered as an example analysis. The input data, in DICOM (Digital Imaging and Communications in Medicine) format, is provided from CT images and dose distribution models of patients. The CT images are processed using convolution neural networks, and next, the selected slices go through the segmentation and registration algorithms. The results of the analysis are visualized in graphical format and also in numerical parameters calculated based on the images analysis.

scholarly journals Segmentation of Lung Ct Images using Cascaded Fully Convolutional Neural Networks

2019 ◽  
Vol 8 (2) ◽  
pp. 5472-5474
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Convolutional Neural Networks ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Fully Convolutional Neural Networks ◽  
Proposal A ◽  
Fully Automatic ◽  
Lung Ct

Interpretation of CT Lung images by the radiologist can be enhanced to a greater extent by automatic segmentation of nodules. The efficiency of this interpretation depends on the completeness and non-ambiguousness of the CT Lung images. Here, a fully automatic cascaded basis was proposed for CT Lung image segmentation. In this proposal a customized FCN was used feature extractions exploration from many visual scales and differentiate anatomy with a thick forecast map. Widespread experimental outcomes demonstrate that this technique can address the incompleteness in boundary and this technique can achieve best accuracy in segmentation of Lung CT Images when compared to other techniques which address the same area

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