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https://www.readbyqxmd.com/read/28333649/multi-scale-rotation-invariant-convolutional-neural-networks-for-lung-texture-classification
#1
Qiangchang Wang, Yuanjie Zheng, Gongping Yang, Weidong Jin, Xinjian Chen, Yilong Yin
We propose a new Multi-scale Rotation-invariant Convolutional Neural Network (MRCNN) model for classifying various lung tissue types on high-resolution computed tomography (HRCT). MRCNN employs Gabor-local binary pattern (Gabor-LBP) which introduces a good property in image analysis - invariance to image scales and rotations. In addition, we offer an approach to deal with the problems caused by imbalanced number of samples between different classes in most of the existing works, accomplished by changing the overlapping size between the adjacent patches...
March 21, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28325447/statistical-tools-for-the-temporal-analysis-and-classification-of-lung-lesions
#2
Stelmo Magalhães Barros Netto, Aristófanes Corrêa Silva, Hélio Lopes, Anselmo Cardoso de Paiva, Rodolfo Acatauassú Nunes, Marcelo Gattass
BACKGROUND AND OBJECTIVE: Lung cancer remains one of the most common cancers globally. Temporal evaluation is an important tool for analyzing the malignant behavior of lesions during treatment, or of indeterminate lesions that may be benign. This work proposes a methodology for the analysis, quantification, and visualization of small (local) and large (global) changes in lung lesions. In addition, we extract textural features for the classification of lesions as benign or malignant. METHODS: We employ the statistical concept of uncertainty to associate each voxel of a lesion to a probability that changes occur in the lesion over time...
April 2017: Computer Methods and Programs in Biomedicine
https://www.readbyqxmd.com/read/28297322/evaluation-of-the-tool-reg-refine-for-user-guided-deformable-image-registration
#3
Perry B Johnson, Kyle R Padgett, Kuan L Chen, Nesrin Dogan
"Reg Refine" is a tool available in the MIM Maestro v6.4.5 platform (www.mimsoftware.com) that allows the user to actively participate in the deformable image registration process. The purpose of this work was to evaluate the efficacy of this tool and investigate strategies for how to apply it effectively. This was done by performing DIR on two publicly available ground-truth models, the Pixel-based Breathing Thorax Model (POPI) for lung, and the Deformable Image Registration Evaluation Project (DIREP) for head and neck...
May 2016: Journal of Applied Clinical Medical Physics
https://www.readbyqxmd.com/read/28295386/an-integrated-segmentation-and-shape-based-classification-scheme-for-distinguishing-adenocarcinomas-from-granulomas-on-lung-ct
#4
Mehdi Alilou, Niha Beig, Mahdi Orooji, Anant Madabhushi, Prabhakar Rajiah, Michael Yang, Robert Gilkeson, Philip Linden, Vamsidhar Velcheti, Sagar Rakshit, Niyoti Reddy, Frank Jacono
PURPOSE: Distinguishing between benign granulmoas and adenocarcinomas is confounded by their similar visual appearance on routine CT scans. Unfortunately, owing to the inability to discriminate these lesions radigraphically, many patients with benign granulomas are subjected to unnecessary surgical wedge resections and biopsies for pathologic confirmation of cancer presence or absence. This suggests the need for improved computerized characterization of these nodules in order to distinguish between these two classes of lesions on CT scans...
March 14, 2017: Medical Physics
https://www.readbyqxmd.com/read/28268558/texton-and-sparse-representation-based-texture-classification-of-lung-parenchyma-in-ct-images
#5
Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine
Automated texture analysis of lung computed tomography (CT) images is a critical tool in subtyping pulmonary emphysema and diagnosing chronic obstructive pulmonary disease (COPD). Texton-based methods encode lung textures with nearest-texton frequency histograms, and have achieved high performance for supervised classification of emphysema subtypes from annotated lung CT images. In this work, we first explore characterizing lung textures with sparse decomposition from texton dictionaries, using different regularization strategies, and then extend the sparsity-inducing constraint to the construction of the dictionaries...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268557/prediction-of-malignant-and-benign-of-lung-tumor-using-a-quantitative-radiomic-method
#6
Jun Wang, Xia Liu, Di Dong, Jiangdian Song, Min Xu, Yali Zang, Jie Tian
Lung cancer is the leading cause of cancer mortality around the world, the early diagnosis of lung cancer plays a very important role in therapeutic regimen selection. However, lung cancers are spatially and temporally heterogeneous; this limits the use of invasive biopsy. But radiomics which refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features has the ability to capture intra-tumoural heterogeneity in a non-invasive way. Here we carry out a radiomic analysis of 150 features quantifying lung tumour image intensity, shape and texture...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28268556/association-between-tumor-heterogeneity-and-progression-free-survival-in-non-small-cell-lung-cancer-patients-with-egfr-mutations-undergoing-tyrosine-kinase-inhibitors-therapy
#7
Jiangdian Song, Di Dong, Yanqi Huang, Yali Zang, Zaiyi Liu, Jie Tian
For non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutations, current staging methods do not accurately predict the risk of disease recurrence after tyrosine kinase inhibitors (TKI) therapy. Developing a noninvasive method to predict whether individual could benefit from TKI therapy has great clinical significance. In this research, a radiomics approach was proposed to determine whether the tumor heterogeneity of NSCLC, which was measured by the texture on computed tomography (CT), could make an independent prediction of progression-free survival (PFS)...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28258739/analysis-of-ct-features-and-quantitative-texture-analysis-in-patients-with-lung-adenocarcinoma-a-correlation-with-egfr-mutations-and-survival-rates
#8
B Sacconi, M Anzidei, A Leonardi, F Boni, L Saba, R Scipione, M Anile, M Rengo, F Longo, M Bezzi, F Venuta, A Napoli, A Laghi, C Catalano
AIM: To investigate the correlation between conventional computed tomography (CT) features, quantitative texture analysis (QTA), epidermal growth factor receptor (EGFR) mutations, and survival rates in patients with lung adenocarcinoma. MATERIALS AND METHODS: Sixty-eight patients were evaluated for conventional CT features and QTA in this retrospective study. A multiple logistic regression analysis and receiver operating characteristics (ROC) curve analysis versus death and EGFR status was performed for CT features and QTA in order to assess correlation between CT features, QTA, EGFR mutations, and survival rates...
February 28, 2017: Clinical Radiology
https://www.readbyqxmd.com/read/28226735/texton-and-sparse-representation-based-texture-classification-of-lung-parenchyma-in-ct-images
#9
Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine, Jie Yang, Xinyang Feng, Elsa D Angelini, Andrew F Laine, Xinyang Feng, Jie Yang, Elsa D Angelini, Andrew F Laine
Automated texture analysis of lung computed tomography (CT) images is a critical tool in subtyping pulmonary emphysema and diagnosing chronic obstructive pulmonary disease (COPD). Texton-based methods encode lung textures with nearest-texton frequency histograms, and have achieved high performance for supervised classification of emphysema subtypes from annotated lung CT images. In this work, we first explore characterizing lung textures with sparse decomposition from texton dictionaries, using different regularization strategies, and then extend the sparsity-inducing constraint to the construction of the dictionaries...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226734/prediction-of-malignant-and-benign-of-lung-tumor-using-a-quantitative-radiomic-method
#10
Jun Wang, Xia Liu, Di Dong, Jiangdian Song, Min Xu, Yali Zang, Jie Tian, Jun Wang, Xia Liu, Di Dong, Jiangdian Song, Min Xu, Yali Zang, Jie Tian, Min Xu, Jiangdian Song, Di Dong, Jie Tian, Jun Wang, Yali Zang, Xia Liu
Lung cancer is the leading cause of cancer mortality around the world, the early diagnosis of lung cancer plays a very important role in therapeutic regimen selection. However, lung cancers are spatially and temporally heterogeneous; this limits the use of invasive biopsy. But radiomics which refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features has the ability to capture intra-tumoural heterogeneity in a non-invasive way. Here we carry out a radiomic analysis of 150 features quantifying lung tumour image intensity, shape and texture...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28226733/association-between-tumor-heterogeneity-and-progression-free-survival-in-non-small-cell-lung-cancer-patients-with-egfr-mutations-undergoing-tyrosine-kinase-inhibitors-therapy
#11
Jiangdian Song, Di Dong, Yanqi Huang, Yali Zang, Zaiyi Liu, Jie Tian, Jiangdian Song, Di Dong, Yanqi Huang, Yali Zang, Zaiyi Liu, Jie Tian, Zaiyi Liu, Yanqi Huang, Yali Zang, Jiangdian Song, Jie Tian, Di Dong
For non-small cell lung cancer (NSCLC) patients with epidermal growth factor receptor (EGFR) mutations, current staging methods do not accurately predict the risk of disease recurrence after tyrosine kinase inhibitors (TKI) therapy. Developing a noninvasive method to predict whether individual could benefit from TKI therapy has great clinical significance. In this research, a radiomics approach was proposed to determine whether the tumor heterogeneity of NSCLC, which was measured by the texture on computed tomography (CT), could make an independent prediction of progression-free survival (PFS)...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28169141/measuring-interobserver-disagreement-in-rating-diagnostic-characteristics-of-pulmonary-nodule-using-the-lung-imaging-database-consortium-and-image-database-resource-initiative
#12
Hongli Lin, Changxing Huang, Weisheng Wang, Jiawei Luo, Xuedong Yang, Yuling Liu
RATIONALE AND OBJECTIVES: The purpose of this study was to measure and analyze interobserver disagreement in rating diagnostic characteristics of pulmonary nodules on computed tomography scans using the Lung Imaging Database Consortium and Image Database Resource Initiative (LIDC/IDRI) database, and then to provide investigators with understanding the variability in rating diagnostic characteristics among radiologists. MATERIALS AND METHODS: A histogram-based accumulated nodule-level approach is proposed to measure interobserver disagreement in rating diagnostic characteristics of pulmonary nodules among radiologists...
February 3, 2017: Academic Radiology
https://www.readbyqxmd.com/read/28166511/a-solitary-feature-based-lung-nodule-detection-approach-for-chest-x-ray-radiographs
#13
Xuechen Li, Linlin Shen, Suhuai Luo
Lung cancer is one of the most deadly diseases. It has a high death rate and its incidence rate has been increasing all over the world. Lung cancer appears as a solitary nodule in chest x-ray radiograph (CXR). Therefore, lung nodule detection in CXR could have a significant impact on early detection of lung cancer. Radiologists define a lung nodule in chest x-ray radiographs as "solitary white nodule-like blob". However, the solitary feature has not been employed for lung nodule detection before. In this paper, a solitary feature-based lung nodule detection method was proposed...
January 31, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28149958/radiomics-of-lung-nodules-a-multi-institutional-study-of-robustness-and-agreement-of-quantitative-imaging-features
#14
Jayashree Kalpathy-Cramer, Artem Mamomov, Binsheng Zhao, Lin Lu, Dmitry Cherezov, Sandy Napel, Sebastian Echegaray, Daniel Rubin, Michael McNitt-Gray, Pechin Lo, Jessica C Sieren, Johanna Uthoff, Samantha K N Dilger, Brandan Driscoll, Ivan Yeung, Lubomir Hadjiiski, Kenny Cha, Yoganand Balagurunathan, Robert Gillies, Dmitry Goldgof
Radiomics is to provide quantitative descriptors of normal and abnormal tissues during classification and prediction tasks in radiology and oncology. Quantitative Imaging Network members are developing radiomic "feature" sets to characterize tumors, in general, the size, shape, texture, intensity, margin, and other aspects of the imaging features of nodules and lesions. Efforts are ongoing for developing an ontology to describe radiomic features for lung nodules, with the main classes consisting of size, local and global shape descriptors, margin, intensity, and texture-based features, which are based on wavelets, Laplacian of Gaussians, Law's features, gray-level co-occurrence matrices, and run-length features...
December 2016: Tomography: a Journal for Imaging Research
https://www.readbyqxmd.com/read/28130689/comparison-of-machine-learning-methods-for-classifying-mediastinal-lymph-node-metastasis-of-non-small-cell-lung-cancer-from-18-f-fdg-pet-ct-images
#15
Hongkai Wang, Zongwei Zhou, Yingci Li, Zhonghua Chen, Peiou Lu, Wenzhi Wang, Wanyu Liu, Lijuan Yu
BACKGROUND: This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from (18)F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network...
December 2017: EJNMMI Research
https://www.readbyqxmd.com/read/28129196/edge-preserving-depth-map-upsampling-by-joint-trilateral-filter
#16
Kai-Han Lo, Yu-Chiang Frank Wang, Kai-Lung Hua
Compared to the color images, their associated depth images captured by the RGB-D sensors are typically with lower resolution. The task of depth map super-resolution (SR) aims at increasing the resolution of the range data by utilizing the high-resolution (HR) color image, while the details of the depth information are to be properly preserved. In this paper, we present a joint trilateral filtering (JTF) algorithm for depth image SR. The proposed JTF first observes context information from the HR color image...
January 24, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28114048/multi-source-transfer-learning-with-convolutional-neural-networks-for-lung-pattern-analysis
#17
Stergios Christodoulidis, Marios Anthimopoulos, Lukas Ebner, Andreas Christe, Stavroula Mougiakakou
Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis (CAD) systems have been developed. These commonly rely on a fixed scale classifier that scans CT images, recognizes textural lung patterns and generates a map of pathologies. In a previous study, we proposed a method for classifying lung tissue patterns using a deep convolutional neural network (CNN), with an architecture designed for the specific problem...
December 7, 2016: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28113928/automatic-scoring-of-multiple-semantic-attributes-with-multi-task-feature-leverage-a-study-on-pulmonary-nodules-in-ct-images
#18
Sihong Chen, Jing Qin, Xing Ji, Baiying Lei, Tianfu Wang, Dong Ni, Jie-Zhi Cheng
The gap between the computational and semantic features is the one of major factors that bottlenecks the computer-aided diagnosis (CAD) performance from clinical usage. To bridge this gap, we exploit three multi-task learning (MTL) schemes to leverage heterogeneous computational features derived from deep learning models of stacked denoising autoencoder (SDAE) and convolutional neural network (CNN), as well as hand-crafted Haar-like and HoG features, for the description of 9 semantic features for lung nodules in CT images...
November 16, 2016: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28097934/automatic-and-quantitative-measurement-of-laryngeal-video-stroboscopic-images
#19
Chung-Feng Jeffrey Kuo, Joseph Kuo, Shang-Wun Hsiao, Chi-Lung Lee, Jih-Chin Lee, Bo-Han Ke
The laryngeal video stroboscope is an important instrument for physicians to analyze abnormalities and diseases in the glottal area. Stroboscope has been widely used around the world. However, without quantized indices, physicians can only make subjective judgment on glottal images. We designed a new laser projection marking module and applied it onto the laryngeal video stroboscope to provide scale conversion reference parameters for glottal imaging and to convert the physiological parameters of glottis. Image processing technology was used to segment the important image regions of interest...
January 2017: Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine
https://www.readbyqxmd.com/read/28068363/quantitative-computed-tomography-features-for-predicting-tumor-recurrence-in-patients-with-surgically-resected-adenocarcinoma-of-the-lung
#20
Hyun Jung Koo, Yu Sub Sung, Woo Hyun Shim, Hai Xu, Chang-Min Choi, Hyeong Ryul Kim, Jung Bok Lee, Mi Young Kim
PURPOSE: The purpose of this study was to determine if preoperative quantitative computed tomography (CT) features including texture and histogram analysis measurements are associated with tumor recurrence in patients with surgically resected adenocarcinoma of the lung. METHODS: The study included 194 patients with surgically resected lung adenocarcinoma who underwent preoperative CT between January 2013 and December 2013. Quantitative CT feature analysis of the lung adenocarcinomas were performed using in-house software based on plug-in package for ImageJ...
2017: PloS One
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