Diffuse Parenchymal Lung Diseases 3D Automated
Detection in MDCT 1st Edition by Catalin Fetita,
Kuang Che Chang Chien, Pierre Yves Brillet,
Francoise Preteux, Philippe Grenier ISBN
9783540757573 pdf download
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Towards an Identification of Tumor Growth Parameters from Time Series
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Contraction Detection in Small Bowel from an Image Sequence of
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, Diffuse Parenchymal Lung Diseases: 3D
Automated Detection in MDCT
Catalin Fetita1 , Kuang-Che Chang-Chien1,4 , Pierre-Yves Brillet1,2 ,
Françoise Prêteux1, and Philippe Grenier3
1
Dept. ARTEMIS, INT, Groupe des Ecoles des Télécommunications, Evry, France
2
AP-HP, Avicenne Hospital, Bobigny, France
3
Université Paris 6 and AP-HP, Pitié-Salpêtrière Hospital, Paris, France
4
National Chung Cheng University, Chia-yi, Taiwan, R.O.C
Abstract. Characterization and quantification of diffuse parenchymal
lung disease (DPLD) severity using MDCT, mainly in interstitial lung
diseases and emphysema, is an important issue in clinical research for
the evaluation of new therapies. This paper develops a 3D automated
approach for detection and diagnosis of DPLDs (emphysema, fibrosis,
honeycombing, ground glass).The proposed methodology combines multi-
resolution image decomposition based on 3D morphological filtering, and
graph-based classification for a full characterization of the parenchymal
tissue. The very promising results obtained on a small patient database are
good premises for a near implementation and validation of the proposed
approach in clinical routine.
1 Introduction
Diffuse parenchymal lung diseases (DPLDs) include chronic obstructive lung dis-
ease which is defined by lung destruction (emphysema), and idiopathic interstitial
pneumonias which are characterized by lung infiltrates (ground glass) and fibrosis.
High-resolution computed tomography (HRCT) with 1-mm-thick sections ob-
tained at 10-mm intervals has been widely accepted as the imaging reference
standard for assessing DPLDs. Based on HRCT images, the diagnosis is done by
analysing the different patterns of lung texture, and the severity of the disease
can be evaluated by quotation of the extent of lesions. Nowadays, multidetector
row CT (MDCT) generates isotropic volumetric high-resolution data and allows
contiguous visualization of the lung parenchyma. MDCT tends to replace HRCT
examinations since it allows creating three-dimensional reformatted images of
excellent quality and significance.
Unfortunately, pathology features on the MDCT images sometimes would be
subtle, especially in the early stage, and cause inter-observer variability even
among the experienced radiologists. Hence, computer-aided diagnosis (CAD)
is required for objective quantitative assessment of alterations in the lung [1].
Several studies in the medical and technical literature have addressed the classi-
fication problem of the lung parenchyma. Without claiming an exhaustive anal-
ysis, these studies can be roughly divided into two categories, density-based and
N. Ayache, S. Ourselin, A. Maeder (Eds.): MICCAI 2007, Part I, LNCS 4791, pp. 825–833, 2007.
c Springer-Verlag Berlin Heidelberg 2007
Detection in MDCT 1st Edition by Catalin Fetita,
Kuang Che Chang Chien, Pierre Yves Brillet,
Francoise Preteux, Philippe Grenier ISBN
9783540757573 pdf download
https://ebookball.com/product/diffuse-parenchymal-lung-
diseases-3d-automated-detection-in-mdct-1st-edition-by-catalin-
fetita-kuang-che-chang-chien-pierre-yves-brillet-francoise-
preteux-philippe-grenier-isbn-9783540757573-12134/
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Towards an Identification of Tumor Growth Parameters from Time Series
of Images 1st Edition by Ender Konukoglu, Olivier Clatz, Pierre Yves
Bondiau, Maxime Sermesant, Herve Delingette, Nicholas Ayache ISBN
9783540757573
https://ebookball.com/product/towards-an-identification-of-tumor-
growth-parameters-from-time-series-of-images-1st-edition-by-
ender-konukoglu-olivier-clatz-pierre-yves-bondiau-maxime-
sermesant-herve-delingette-nicholas-ayache-isbn/
Environmental Microbiology Fundamentals and Applications microbial
ecology 1st Edition by Jean Claude Bertrand, Pierre Caumette, Philippe
Lebaron, Robert Matheron, Philippe Normand, Télesphore Sime Ngando
ISBN 9789401791182 940179118X
https://ebookball.com/product/environmental-microbiology-
fundamentals-and-applications-microbial-ecology-1st-edition-by-
jean-claude-bertrand-pierre-caumette-philippe-lebaron-robert-
matheron-philippe-normand-ta-c-lesphore-sime-ngan/
False Positive Reduction in Mammographic Mass Detection Using Local
Binary Patterns 1st edition by Arnau Oliver, Xavier Llado, Jordi
Freixenet, Joan Marti ISBN 9783540757573
https://ebookball.com/product/false-positive-reduction-in-
mammographic-mass-detection-using-local-binary-patterns-1st-
edition-by-arnau-oliver-xavier-llado-jordi-freixenet-joan-marti-
isbn-9783540757573-13556/
Probabilistic Speckle Decorrelation for 3D Ultrasound 1st Edition by
Catherine Laporte, Tal Arbel ISBN 9783540757573
https://ebookball.com/product/probabilistic-speckle-
decorrelation-for-3d-ultrasound-1st-edition-by-catherine-laporte-
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,Diagnostic Pathology Kidney Diseases 2nd edition by Anthony Chang,
Robert Colvin ISBN 0323377076 ‎ 978-0323377072
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Modeling Glioma Growth and Mass Effect in 3D MR Images of the Brain
1st Edition by Cosmina Hogea, Christos Davatzikos, George Biros ISBN
9783540757573
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effect-in-3d-mr-images-of-the-brain-1st-edition-by-cosmina-hogea-
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Real Time Tracking of the Left Ventricle in 3D Echocardiography Using
a State Estimation Approach 1st Edition by Fredrik Orderud, Joger
Hansgard, Stein I Rabben ISBN 9783540757573
https://ebookball.com/product/real-time-tracking-of-the-left-
ventricle-in-3d-echocardiography-using-a-state-estimation-
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rabben-isbn-9783540757573-10226/
Deformable Density Matching for 3D Non rigid Registration of Shapes
1st Edition by Arunabha S Roy, Ajay Gopinath, Anand Rangarajan ISBN
9783540757573
https://ebookball.com/product/deformable-density-matching-
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s-roy-ajay-gopinath-anand-rangarajan-isbn-9783540757573-13520/
Contraction Detection in Small Bowel from an Image Sequence of
Wireless Capsule Endoscopy 1st Edition by Hai Vu, Tomio Echigo,
Ryusuke Sagawa, Keiko Yagi, Masatsugu Shiba, Kazuhide Higuchi, Tetsuo
Arakawa, Yasushi Yagi ISBN 9783540757573
https://ebookball.com/product/contraction-detection-in-small-
bowel-from-an-image-sequence-of-wireless-capsule-endoscopy-1st-
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masatsugu-shiba-kazuhide-higuchi-tetsuo-arakawa-y/
, Diffuse Parenchymal Lung Diseases: 3D
Automated Detection in MDCT
Catalin Fetita1 , Kuang-Che Chang-Chien1,4 , Pierre-Yves Brillet1,2 ,
Françoise Prêteux1, and Philippe Grenier3
1
Dept. ARTEMIS, INT, Groupe des Ecoles des Télécommunications, Evry, France
2
AP-HP, Avicenne Hospital, Bobigny, France
3
Université Paris 6 and AP-HP, Pitié-Salpêtrière Hospital, Paris, France
4
National Chung Cheng University, Chia-yi, Taiwan, R.O.C
Abstract. Characterization and quantification of diffuse parenchymal
lung disease (DPLD) severity using MDCT, mainly in interstitial lung
diseases and emphysema, is an important issue in clinical research for
the evaluation of new therapies. This paper develops a 3D automated
approach for detection and diagnosis of DPLDs (emphysema, fibrosis,
honeycombing, ground glass).The proposed methodology combines multi-
resolution image decomposition based on 3D morphological filtering, and
graph-based classification for a full characterization of the parenchymal
tissue. The very promising results obtained on a small patient database are
good premises for a near implementation and validation of the proposed
approach in clinical routine.
1 Introduction
Diffuse parenchymal lung diseases (DPLDs) include chronic obstructive lung dis-
ease which is defined by lung destruction (emphysema), and idiopathic interstitial
pneumonias which are characterized by lung infiltrates (ground glass) and fibrosis.
High-resolution computed tomography (HRCT) with 1-mm-thick sections ob-
tained at 10-mm intervals has been widely accepted as the imaging reference
standard for assessing DPLDs. Based on HRCT images, the diagnosis is done by
analysing the different patterns of lung texture, and the severity of the disease
can be evaluated by quotation of the extent of lesions. Nowadays, multidetector
row CT (MDCT) generates isotropic volumetric high-resolution data and allows
contiguous visualization of the lung parenchyma. MDCT tends to replace HRCT
examinations since it allows creating three-dimensional reformatted images of
excellent quality and significance.
Unfortunately, pathology features on the MDCT images sometimes would be
subtle, especially in the early stage, and cause inter-observer variability even
among the experienced radiologists. Hence, computer-aided diagnosis (CAD)
is required for objective quantitative assessment of alterations in the lung [1].
Several studies in the medical and technical literature have addressed the classi-
fication problem of the lung parenchyma. Without claiming an exhaustive anal-
ysis, these studies can be roughly divided into two categories, density-based and
N. Ayache, S. Ourselin, A. Maeder (Eds.): MICCAI 2007, Part I, LNCS 4791, pp. 825–833, 2007.
c Springer-Verlag Berlin Heidelberg 2007
