&lt;title&gt;Fast pulmonary contour extraction in x-ray CT images: a methodology and quality assessment&lt;/title&gt;

Silva, Augusto; Silva, José Silvestre; Santos, Beatriz Sousa; Ferreira, Carlos

doi:10.1117/12.428139

Cited by 33 publications

(28 citation statements)

References 15 publications

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“…Santos et al [20] and Silva et al [22] introduced mean distance and Pratt function to determine contour similarity. Hausdorff distance was used for the evaluation of different boundary detection algorithms [1,4].…”

Section: Contour-based Metricsmentioning

confidence: 99%

“…where q is a normalization parameter with a constant value of 1/9 [22]. The values of Pratt function is equal to 1 for total overlap and 0 for complete mismatch.…”

Section: Pratt Functionmentioning

confidence: 99%

“…Accuracy, overlap, sensitivity, and specificity are computed using the definition of Byrd et al [3]. Similarity angle and similarity region are computed using the formula of Santos et al [20,22].…”

Section: Region-based Metricsmentioning

confidence: 99%

“…The similarity angle is computed based on the morphological similarity between two images [20,22]. Each of the following vectors ( − → P and − → Q ) are obtained by transforming an (m × n × p) binary image into a 1D array same number of pixels, and similarity angle is defined as…”

Section: Similarity Anglementioning

confidence: 99%

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A Segmentation Framework of Pulmonary Nodules in Lung CT Images

Mukhopadhyay

2015

J Digit Imaging

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Accurate segmentation of pulmonary nodules is a prerequisite for acceptable performance of computer-aided detection (CAD) system designed for diagnosis of lung cancer from lung CT images. Accurate segmentation helps to improve the quality of machine level features which could improve the performance of the CAD system. The wellcircumscribed solid nodules can be segmented using thresholding, but segmentation becomes difficult for part-solid, non-solid, and solid nodules attached with pleura or vessels. We proposed a segmentation framework for all types of pulmonary nodules based on internal texture (solid/partsolid and non-solid) and external attachment (juxta-pleural and juxta-vascular). In the proposed framework, first pulmonary nodules are categorized into solid/part-solid and non-solid category by analyzing intensity distribution in the core of the nodule. Two separate segmentation methods are developed for solid/part-solid and non-solid nodules, respectively. After determining the category of nodule, the particular algorithm is set to remove attached pleural surface and vessels from the nodule body. The result of segmentation is evaluated in terms of four contour-based metrics and six region-based metrics for 891 pulmonary nodules from Lung Image Database Consortium and Image Database Resource Initiative (LIDC/IDRI) public database. The experimental result shows that the proposed segmentation framework is reliable for segmentation of various types of pulmonary nodules with improved accuracy compared to existing segmentation methods.

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Section: Contour-based Metricsmentioning

confidence: 99%

“…where q is a normalization parameter with a constant value of 1/9 [22]. The values of Pratt function is equal to 1 for total overlap and 0 for complete mismatch.…”

Section: Pratt Functionmentioning

confidence: 99%

Section: Region-based Metricsmentioning

confidence: 99%

Section: Similarity Anglementioning

confidence: 99%

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A Segmentation Framework of Pulmonary Nodules in Lung CT Images

Mukhopadhyay

2015

J Digit Imaging

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“…Most algorithms for segmentation of pulmonary regions are based on intensity discrimination within the Hounsfield scale, however this task may be very complex [1][2][3] . In a previous work 4 we have presented algorithms that generate contours with a variable degree of similarity to those provided by expert imagiologists.…”

Section: Introductionmentioning

confidence: 99%

Comparison of a segmentation algorithm to six expert imagiologists in detecting pulmonary contours on x-ray CT images

Ferreira

Santos

Silva

et al. 2003

Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment

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Quantitative evaluation of the performance of segmentation algorithms on medical images is crucial before their clinical use can be considered. We have quantitatively compared the contours obtained by a pulmonary segmentation algorithm to contours manually-drawn by six expert imaiologists on the same set of images, since the ground truth is unknown.Two types of variability (inter-observer and intra-observer) should be taken into account in the performance evaluation of segmentation algorithms and several methods to do it have been proposed. This paper describes the quantitative evaluation of the performance of our segmentation algorithm using several figures of merit, exploratory and multivariate data analysis and non parametric tests, based on the assessment of the inter-observer variability of six expert imagiologists from three different hospitals and the intra-observer variability of two expert imagiologists from the same hospital.As an overall result of this comparison we were able to claim that the consistency and accuracy of our pulmonary segmentation algorithm is adequate for most of the quantitative requirements mentioned by the imagiologists. We also believe that the methodology used to evaluate the performance of our algorithm is general enough to be applicable to many other segmentation problems on medical images.

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Robust Segmentation of Challenging Lungs in CT Using Multi-stage Learning and Level Set Optimization

Birkbeck

Sofka

Kohlberger

et al. 2013

Computational Intelligence in Biomedical Imaging

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Automatic segmentation of lung tissue in thoracic CT scans is useful for diagnosis and treatment planning of pulmonary diseases. Unlike healthy lung tissue that is easily identifiable in CT scans, diseased lung parenchyma is hard to segment automatically due to its higher attenuation, inhomogeneous appearance, and inconsistent texture. We overcome these challenges through a multi-layer machine learning approach that exploits geometric structures both within and outside the lung (e.g., ribs, spine). In the coarsest layer, a set of stable landmarks on the surface of the lung are detected through a hierarchical detection network (HDN) that is trained on hundreds of annotated CT volumes. These landmarks are used to robustly initialize a coarse statistical model of the lung shape. Subsequently, a region-dependent boundary refinement uses a discriminative appearance classifier to refine the surface, and finally a region-driven level set refinement is used to extract the fine scale detail. Through this approach we demonstrate robustness to a variety of lung pathologies.

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<title>Fast pulmonary contour extraction in x-ray CT images: a methodology and quality assessment</title>

Cited by 33 publications

References 15 publications

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

A Segmentation Framework of Pulmonary Nodules in Lung CT Images

Comparison of a segmentation algorithm to six expert imagiologists in detecting pulmonary contours on x-ray CT images

Robust Segmentation of Challenging Lungs in CT Using Multi-stage Learning and Level Set Optimization

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