An Approach for Pulmonary Vascular Extraction from Chest CT Images

Journal of Healthcare Engineering
Wenjun TanXinhui Lv

Abstract

Pulmonary vascular extraction from chest CT images plays an important role in the diagnosis of lung disease. To improve the accuracy rate of pulmonary vascular segmentation, a new pulmonary vascular extraction approach is proposed in this study. First, the lung tissue is extracted from chest CT images by region-growing and maximum between-class variance methods. Then the holes of the extracted region are filled by morphological operations to obtain complete lung region. Second, the points of the pulmonary vascular of the middle slice of the chest CT images are extracted as the original seed points. Finally, the seed points are spread throughout the lung region based on the fast marching method to extract the pulmonary vascular in the gradient image. Results of pulmonary vascular extraction from chest CT image datasets provided by the introduced approach are presented and discussed. Based on the ground truth pixels and the resulting quality measures, it can be concluded that the average accuracy of this approach is about 90%. Extensive experiments demonstrate that the proposed method has achieved the best performance in pulmonary vascular extraction compared with other two widely used methods.

References

Apr 13, 2005·IEEE Transactions on Medical Imaging·Gady AgamChanghua Wu
Oct 13, 2009·Medical Image Analysis·David LesageGareth Funka-Lea
Apr 17, 2010·Medical Image Analysis·Pechin LoMarleen de Bruijne
Oct 19, 2010·Medical Image Analysis·Changyan XiaoBerend C Stoel
Feb 15, 2011·IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society·Panayiotis D KorfiatisLena I Costaridou
Aug 3, 2012·IEEE Transactions on Medical Imaging·Pechin LoMarleen de Bruijne
Sep 13, 2015·Computer Methods and Programs in Biomedicine·Xuanping LiPengbo Zhang
Jul 12, 2018·IEEE Transactions on Medical Imaging·Pietro NardelliRaul San Jose Estepar

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Citations

Dec 4, 2020·Health Information Science and Systems·Wenjun TanYanchun Zhang

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Software Mentioned

VESSEL12
Matlab

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