Automatic lung segmentation in CT images using watershed transform

Shojaii, Rushin; Alirezaie, Javad; Babyn, Paul

doi:10.1109/icip.2005.1530294

Cited by 84 publications

(51 citation statements)

References 8 publications

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“…Often, the watershed transform experiences over-segmentation problem resulting in the segmentation of unwanted regions. To overcome this issue, marker based watershed transform is used rather than conventional watershed algorithm [12]. The markers are applied to the gradient image to avoid over-segmentation where it decreases the regional minima connecting them with the region of interest.…”

Section: Watershedmentioning

confidence: 99%

Empirical evaluation of segmentation algorithms for lung modelling

Lee

Kouzani

2008

2008 IEEE International Conference on Systems, Man and Cybernetics

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Abstract-Lung modelling has emerged as a useful method for diagnosing lung diseases. Image segmentation is an important part of lung modelling systems. The ill-defined nature of image segmentation makes automated lung modelling difficult. Also, low resolution of lung images further increases the difficulty of the lung image segmentation. It is therefore important to identify a suitable segmentation algorithm that can enhance lung modelling accuracies. This paper investigates six image segmentation algorithms, used in medical imaging, and also their application to lung modelling. The algorithms are: normalised cuts, graph, region growing, watershed, Markov random field, and mean shift. The performance of the six segmentation algorithms is determined through a set of experiments on realistic 2D CT lung images. An experimental procedure is devised to measure the performance of the tested algorithms. The measured segmentation accuracies as well as execution times of the six algorithms are then compared and discussed.

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Section: Watershedmentioning

confidence: 99%

Empirical evaluation of segmentation algorithms for lung modelling

Lee

Kouzani

2008

2008 IEEE International Conference on Systems, Man and Cybernetics

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“…Sometimes, the use of the watershed over-segmentation results in unwanted regions. To circumvent this problem markers are applied to the image gradient in order to avoid over-segmentation, thus abandoning the conventional watershed algorithm (Shojaii et al, 2005). This operation allows the reduction of regional minima, grouping them in the region of interest.…”

Section: Introductionmentioning

confidence: 99%

Brain Segmentation in Head Ct Images

Torres¹,

Monteiro²

2012

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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Abstract:Brain segmentation in head computed tomography scans is essential for the development of computer-aided diagnostic methods for identifying the brain diseases. In this paper we present a hybrid framework to brain segmentation which joints region-based information based on watershed transform with clustering techniques. A pre-processing step is used to reduce the spatial resolution without losing important image information. An initial partitioning of the image into primitive regions is set by applying a rainfalling watershed algorithm on the image gradient magnitude. This initial partition is the input to a computationally efficient region segmentation process which produces the final segmentation. We have applied our approach on several head CT images and the results reveal the robustness and accuracy of this method.

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“…Often, the watershed transform experiences over-segmentation problem resulting in the segmentation of unwanted regions. To overcome this issue, marker based watershed transform is used rather than conventional watershed algorithm [5]. The markers are applied to the gradient image to avoid over-segmentation where it decreases the regional minima connecting them with the region of interest.…”

Section: Introductionmentioning

confidence: 99%

Region-Based Clustering for Lung Segmentation in Low-Dose CT Images

Monteiro¹

2010

AIP Conference Proceedings

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Abstract. Lung segmentation in thoracic computed tomography scans is essential for the development of computer-aided diagnostic methods for identifying the lung diseases. Low-dose CT scans are increasingly utilized in lung studies, but segmenting them with traditional threshold segmentation algorithms often yields less than satisfying results. In this paper we present a hybrid framework to lung segmentation which joints region-based information based on watershed transform with clustering techniques. The proposed method eliminates the task of finding an optimal threshold and the over-segmentation produced by watershed. We have applied our approach on several pulmonary low-dose CT images and the results reveal the robustness and accuracy of this method.

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Automatic lung segmentation in CT images using watershed transform

Cited by 84 publications

References 8 publications

Empirical evaluation of segmentation algorithms for lung modelling

Empirical evaluation of segmentation algorithms for lung modelling

Brain Segmentation in Head Ct Images

Region-Based Clustering for Lung Segmentation in Low-Dose CT Images

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