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Suppression of the contrast of ribs in chest radiographs by means of massive training artificial neural network

Kenji Suzuki, Hiroyuki Abe, Feng Li, Kunio Doi, J. Michael Fitzpatrick, Milan Sonka
2004 Medical Imaging 2004: Image Processing  
We developed a method for suppression of the contrast of ribs in chest radiographs by means of a massive training artificial neural network (MTANN).  ...  We used either the soft-tissue image or the bone image obtained by use of a dual-energy subtraction technique as the teacher image for suppression of ribs in chest radiographs.  ...  Our purpose in this study was to develop a method for suppression of the contrast of ribs in chest radiographs by means of a massive training artificial neural network (MTANN).  ... 
doi:10.1117/12.536436 dblp:conf/miip/SuzukiALD04 fatcat:k6imw5ugo5ektaic6p4cfx6uzi

Image-processing technique for suppressing ribs in chest radiographs by means of massive training artificial neural network (MTANN)

K. Suzuki, H. Abe, H. MacMahon, K. Doi
2006 IEEE Transactions on Medical Imaging  
In this paper, we developed an image-processing technique for suppressing the contrast of ribs and clavicles in chest radiographs by means of a multiresolution massive training artificial neural network  ...  Thus, our image-processing technique for rib suppression by means of a multiresolution MTANN would be potentially useful for radiologists as well as for CAD schemes in detection of lung nodules on chest  ...  ACKNOWLEDGMENT The authors are grateful to F. Li for her clinical advice, to J. Shiraishi and Q. Li for their valuable suggestions, and to E. F. Lanzl for improving the manuscript.  ... 
doi:10.1109/tmi.2006.871549 pmid:16608057 fatcat:sda3q2oxjnds5cow76rt4nlzye

Rib suppression in frontal chest radiographs: A blind source separation approach

Tahir Rasheed, Bilal Ahmed, M.A.U. Khan, Maamar Bettayeb, Sungyoung Lee, Tae-Seong Kim
2007 2007 9th International Symposium on Signal Processing and Its Applications  
Chest radiographs play an important role in the diagnosis of lung cancer. Detection of pulmonary nodules in chest radiographs forms the basis of early detection.  ...  Due to its sparse bone structure and overlapping of the nodule with ribs and clavicles the nodule is difficult to detect in conventional chest radiographs.  ...  An attempt was made in [10] where the rib cage was suppressed by the means of Massive Training Artificial Neural Network (MTANN).  ... 
doi:10.1109/isspa.2007.4555516 dblp:conf/isspa/RasheedAKBLK07 fatcat:tackvdutbnaotec52vq2igueby

Machine Learning in Medical Imaging Before and After Introduction of Deep Learning

Kenji SUZUKI
2017 Medical Imaging and Information Sciences  
It started from an event in 2012 when a deep learning approach based on a convolutional neural network(CNN)won an overwhelming victory in the bestknown worldwide computer-vision competition, ImageNet Classification  ...  It is expected that image/pixel-based ML including deep learning will be the mainstream technology in the field of medical imaging in the next few decades.  ...  : contrast of ribs in chest radiographs by means of massive applications of artificial neural networks, Med Phys, 19 training artificial neural network, Proc.  ... 
doi:10.11318/mii.34.14 fatcat:ui5aakxtknac3h2n6fak7chm6q

False-positive reduction in computer-aided diagnostic scheme for detecting nodules in chest radiographs by means of massive training artificial neural network1

Kenji Suzuki, Junji Shiraishi, Hiroyuki Abe, Heber MacMahon, Kunio Doi
2005 Academic Radiology  
We developed a technique that uses a multiple massive-training artificial neural network (multi-MTANN) to reduce the number of false-positive results in a computer-aided diagnostic (CAD) scheme for detecting  ...  To reduce the effects of background levels in chest radiographs, we applied a background-trend-correction technique, followed by contrast normalization, to the input images for the MTANN.  ...  Metz, PhD, for the use of the LABROC5 program; and to Mrs. Elisabeth F. Lanzl for improving the manuscript. K. Doi and H. MacMahon are shareholders in R2 Technology, Inc., Sunnyvale, CA, and K.  ... 
doi:10.1016/j.acra.2004.11.017 pmid:15721596 fatcat:rmhpcxbignelxcpzbw2cxlhinq

Bone Suppression in Chest Radiographs by Means of Anatomically Specific Multiple Massive-Training ANNs Combined with Total Variation Minimization Smoothing and Consistency Processing [chapter]

Sheng Chen, Kenji Suzuki
2013 Computational Intelligence in Biomedical Imaging  
Although massive-training artificial neural networks (MTANNs) have been developed for suppression of ribs, they did not suppress rib edges, ribs close to the lung wall, and the clavicles well.  ...  Our purpose was to separate bony structures such as ribs and clavicles from soft tissue in chest radiographs (CXRs).  ...  Suzuki et al. developed a supervised image-processing technique for suppressing ribs in CXRs by means of a multi-resolution MTANN [3] [4] .  ... 
doi:10.1007/978-1-4614-7245-2_9 fatcat:mv6yccevtbatrhq32zkuvkmh5y

Bone Structures Extraction and Enhancement in Chest Radiographs via CNN Trained on Synthetic Data [article]

Ophir Gozes, Hayit Greenspan
2020 arXiv   pre-print
Using HU based segmentation of bone structures in the CT domain, a synthetic 2D "Bone x-ray" DRR is produced and used for training the network.  ...  In this paper, we present a deep learning-based image processing technique for extraction of bone structures in chest radiographs using a U-Net FCNN.  ...  [3] introduced a method for suppression of ribs in chest radiographs by means of Massive Training Artificial Neural Networks (MTANN).  ... 
arXiv:2003.10839v1 fatcat:uwzhzi3eivei7n7xi2xvyuqfhm

Review: On Segmentation of Nodules from Posterior and Anterior Chest Radiographs

S K Chaya Devi, T Satya Savithri
2018 International Journal of Biomedical Imaging  
To help radiologists in diagnosing tumor from PA chest radiographic images range of CAD scheme has been developed for the past three decades.  ...  Lung cancer is one of the major types of cancer in the world. Survival rate can be increased if the disease can be identified early.  ...  Acknowledgments The content expressed in this paper is based on research undergone so far, and we are very thankful to all the researchers who contributed to this work.  ... 
doi:10.1155/2018/9752638 pmid:30498510 pmcid:PMC6220737 fatcat:ixrlpqih3nfdlfngn6ubz6pt34

Deep learning-based bone suppression in chest radiographs using CT-derived features: a feasibility study

Ge Ren, Haonan Xiao, Sai-Kit Lam, Dongrong Yang, Tian Li, Xinzhi Teng, Jing Qin, Jing Cai
2021 Quantitative Imaging in Medicine and Surgery  
The CCNN consists of a bone detection network and a bone suppression network. In external validation, the trained CCNN was evaluated using 30 chest radiographs.  ...  However, the training dataset for bone suppression is limited because of the scarcity of bone-free radiographs.  ...  , HMRF 07183266), the Food and Health Bureau, The Government of the Hong Kong Special Administrative Regions.  ... 
doi:10.21037/qims-20-1230 pmid:34888191 pmcid:PMC8611463 fatcat:z7melz6xszervm6zqn5bgxuulq

The detection of lung cancer using massive artificial neural network based on soft tissue technique

Kishore Rajagopalan, Suresh Babu
2020 BMC Medical Informatics and Decision Making  
Method Such an issue has been resolved by creating MANN (Massive Artificial Neural Network) based soft tissue technique from the lung segmented x-ray image.  ...  Utilizing soft tissue technique, many nodules superimposed by ribs as well as clavicles have identified (sensitivity is 72.85% (102/140) and accuracy is 72.96% at one false positive rate).  ...  Massive artificial neural network size has 9 × 9 pixels. It was enough to wrap rib width in the image.  ... 
doi:10.1186/s12911-020-01220-z pmid:33129343 fatcat:4rog3ykncrcvpfc456nlj2zyju

A nonparametric-based rib suppression method for chest radiographs

Jiann-Shu Lee, Jing-Wein Wang, Hsing-Hsien Wu, Ming-Zheng Yuan
2012 Computers and Mathematics with Applications  
This paper presents an automated and comprehensive system for eliminating rib shadows in chest radiographs, which integrates lung field identification, rib segmentation, rib intensity estimation, and suppression  ...  By considering the anatomical structure of the rib cage, we developed a locale sampling scheme to achieve nonparametric rib modeling.  ...  [16] recently developed an image-processing technique for suppressing rib contrast in chest radiographs by using a multiresolution massive training artificial neural network (MTANN).  ... 
doi:10.1016/j.camwa.2012.03.084 fatcat:lofkxkbxfzfg5fqkst62v3qfti

Lung Nodule Detection and Analysis using VDE Chest Radiographs

Anoop CS, Preeja V
2015 International Journal of Computer Applications  
So as to detect nodules in cost effective way, system uses chest radiographs (CXRs). Major challenge in those systems are the anatomical structures (ribs and clavicles) in the CXRs.  ...  These structures will conceal the nodules behind it. In order to overcome this problem virtual dual energy (VDE) technique has been implemented, which produces ribs and clavicle suppressed CXR image.  ...  A multiple massive-training artificial neural networks (MTANNs) employed to reduce the number of FPs produced by CADe scheme had developed by the Suzuki et al [8] .  ... 
doi:10.5120/20293-2704 fatcat:tgmpcgfhcrcijezk7zvtxqpljy

Computerized Analysis of Pneumoconiosis in Digital Chest Radiography: Effect of Artificial Neural Network Trained with Power Spectra

Eiichiro Okumura, Ikuo Kawashita, Takayuki Ishida
2010 Journal of digital imaging  
Therefore, we have developed a computer-aided diagnosis (CAD) system based on the rule-based plus artificial neural network (ANN) method for distinction between normal and abnormal regions of interest  ...  Our CAD system based on PS would be useful to assist radiologists in the classification of pneumoconiosis.  ...  A multi-massive training artificial neural network (multi-MTANN) reduced the false positive rate of CAD schemes from 4.5 to 1.4 false positives per image at an overall sensitivity of 81.3% [20] .  ... 
doi:10.1007/s10278-010-9357-7 pmid:21153856 pmcid:PMC3222544 fatcat:5lqxsdsanve77jarvkkp4jlhhi

Lung Structures Enhancement in Chest Radiographs via CT Based FCNN Training [chapter]

Ophir Gozes, Hayit Greenspan
2018 Lecture Notes in Computer Science  
In this paper, we present a deep learning based image processing technique for enhancing the contrast of soft lung structures in chest radiographs using Fully Convolutional Neural Networks (FCNN).  ...  The abundance of overlapping anatomical structures appearing in chest radiographs can reduce the performance of lung pathology detection by automated algorithms (CAD) as well as the human reader.  ...  We appreciate valuable suggestions from Avi Ben-Cohen in Tel-Aviv University.  ... 
doi:10.1007/978-3-030-00946-5_16 fatcat:omahw2ojibgrpmbgyygg5srwta

Computerized Detection of Lung Nodules by Means of "Virtual Dual-Energy" Radiography

Sheng Chen, Kenji Suzuki
2013 IEEE Transactions on Biomedical Engineering  
artificial neural networks (MTANNs).  ...  Our purpose in this study was to develop a CADe scheme with improved sensitivity and specificity by use of "virtual dualenergy" (VDE) CXRs where ribs and clavicles are suppressed with massive-training  ...  Suzuki et al. developed a multiple massive-training artificial neural networks (MTANNs) to reduce the number of FPs produced by their CADe scheme [12] . Loog et al.  ... 
doi:10.1109/tbme.2012.2226583 pmid:23193306 pmcid:PMC4283823 fatcat:dzjnncazjrc3deozopxbnqja6q
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