A 3d model of the human lung with lung regions characterizaiton

Zrimec, Tatjana; Busayarat, Sata; Wilson, Peter J.

doi:10.1109/icip.2004.1419507

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…These bright structures could be detected by a morphological top hat filter, i.e., by subtracting the opened image from the original one. As some severe GGO regions far from the hilum might be mistaken for vessels by this method, we made a central mask to cover the regions near the hilum of the lung, as determined using the lung region segmentation method described by Zrimec et al [20]. Only the vessel candidates in it were reserved.…”

Section: False Positive Reductionmentioning

confidence: 99%

Automatic Segmentation of Ground-Glass Opacities in Lung CT Images by Using Markov Random Field-Based Algorithms

et al. 2011

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Chest radiologists rely on the segmentation and quantificational analysis of ground-glass opacities (GGO) to perform imaging diagnoses that evaluate the disease severity or recovery stages of diffuse parenchymal lung diseases. However, it is computationally difficult to segment and analyze patterns of GGO while compared with other lung diseases, since GGO usually do not have clear boundaries. In this paper, we present a new approach which automatically segments GGO in lung computed tomography (CT) images using algorithms derived from Markov random field theory. Further, we systematically evaluate the performance of the algorithms in segmenting GGO in lung CT images under different situations. CT image studies from 41 patients with diffuse lung diseases were enrolled in this research. The local distributions were modeled with both simple and adaptive (AMAP) models of maximum a posteriori (MAP). For best segmentation, we used the simulated annealing algorithm with a Gibbs sampler to solve the combinatorial optimization problem of MAP estimators, and we applied a knowledge-guided strategy to reduce false positive regions. We achieved AMAP-based GGO segmentation results of 86.94%, 94.33%, and 94.06% in average sensitivity, specificity, and accuracy, respectively, and we evaluated the performance using radiologists' subjective evaluation and quantificational analysis and diagnosis. We also compared the results of AMAP-based GGO segmentation with those of support vector machine-based methods, and we discuss the reliability and other issues of AMAP-based GGO segmentation. Our research results demonstrate the acceptability and usefulness of AMAP-based GGO segmentation for assisting radiologists in detecting GGO in high-resolution CT diagnostic procedures.

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Section: False Positive Reductionmentioning

confidence: 99%

Automatic Segmentation of Ground-Glass Opacities in Lung CT Images by Using Markov Random Field-Based Algorithms

et al. 2011

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“…Initially, the lung fields were automatically extracted using the method explained in [6]. Then, the lung masks were geometrically divided into a 36-region atlas [3,5] derived from the 3D model of the human lung presented by Zrimec et al [17]. For each region r of this atlas two texturebased feature descriptors were extracted: the Fourier histograms of oriented gradients (FHOG) [13] and the locally-oriented 3D Riesz-wavelet transform (3DRiesz) [9].…”

Section: Holistic Graph Model Of the Lungsmentioning

confidence: 99%

From Local to Global: A Holistic Lung Graph Model

Cid

Toro

Platon

et al. 2018

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

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Lung image analysis is an essential part in the assessment of pulmonary diseases. Through visual inspection of CT scans, radiologists detect abnormal patterns in the lung parenchyma, aiming to establish a timely diagnosis and thus improving patient outcome. However, in a generalized disorder of the lungs, such as pulmonary hypertension, the changes in organ tissue can be elusive, requiring additional invasive studies to confirm the diagnosis. We present a graph model that quantifies lung texture in a holistic approach enhancing the analysis between pathologies with similar local changes. The approach extracts local state-of-the-art 3D texture descriptors from an automatically generated geometric parcellation of the lungs. The global texture distribution is encoded in a weighted graph that characterizes the correlations among neighboring organ regions. A data set of 125 patients with suspicion of having a pulmonary vascular pathology was used to evaluate our method. Three classes containing 47 pulmonary hypertension, 31 pulmonary embolism and 47 control cases were classified in a one vs. one setup. An area under the curve of up to 0.85 was obtained adding directionality to the edges of the graph architecture. The approach was able to identify diseased patients, and to distinguish pathologies with abnormal local and global blood perfusion defects.

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“…This reflects how well the method works with structures inside the lung, not only the lung surface. We used the result of an automatic region characterization algorithm proposed in [8] with manual corrections as the ground-truth to calculate the accuracy. An example of the ground-truth is shown in Fig.…”

Section: Discussionmentioning

confidence: 99%

“…We use the image that contains the carina, the first bronchial bifurcation as the representative image to determine the suitable rotation. On the image, sternum and spinal cord are detected using knowledge-driven template matching [8]. Using the sternum and the spinal cord as landmarks (See Fig.1) the rotating angle is calculated so that, after the rotation, the lines between sternum and spinal cord's center points of two registered scans have the same orientations.…”

Section: Rotationmentioning

confidence: 99%

See 1 more Smart Citation

Ray-Tracing Based Registration for HRCT Images of the Lungs

Busayarat

Zrimec

2006

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2006

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Abstract. Image registration is a fundamental problem in medical imaging. It is especially challenging in lung images compared, for example, with the brain. The challenges include large anatomical variations of human lung and a lack of fixed landmarks inside the lung. This paper presents a new method for lung HRCT image registration. It employs a landmark-based global transformation and a novel ray-tracing-based lung surface registration. The proposed surface registration method has two desirable properties: 1) it is fully reversible, and 2) it ensures that the registered lung will be inside the target lung. We evaluated the registration performance by applying it to lung regions mapping. Tested on 46 scans, the registered regions were 89% accurate compared with the ground-truth.

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A 3d model of the human lung with lung regions characterizaiton

Cited by 9 publications

References 6 publications

Automatic Segmentation of Ground-Glass Opacities in Lung CT Images by Using Markov Random Field-Based Algorithms

Automatic Segmentation of Ground-Glass Opacities in Lung CT Images by Using Markov Random Field-Based Algorithms

From Local to Global: A Holistic Lung Graph Model

Ray-Tracing Based Registration for HRCT Images of the Lungs

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