Segmental and Subsegmental Lung Vessel Segmentation in CTA Images
In this paper, a novel and fast algorithm for segmental
and subsegmental lung vessel segmentation is introduced using
Computed Tomography Angiography images. This process is quite
important especially at the detection of pulmonary embolism, lung
nodule, and interstitial lung disease. The applied method has been
realized at five steps. At the first step, lung segmentation is achieved.
At the second one, images are threshold and differences between the
images are detected. At the third one, left and right lungs are gathered
with the differences which are attained in the second step and Exact
Lung Image (ELI) is achieved. At the fourth one, image, which is
threshold for vessel, is gathered with the ELI. Lastly, identifying and
segmentation of segmental and subsegmental lung vessel have been
carried out thanks to image which is obtained in the fourth step. The
performance of the applied method is found quite well for
radiologists and it gives enough results to the surgeries medically.
[1] A. Kiraly, "A comparison of 2D and 3-D evaluation methods for
pulmonary embolism detection in CT images," in Proc. SPIE Med.
Imag, 2006, vol. 6146.
[2] E. Pichon, C. L. Novak, A. P. Kiraly, and D. P. Naidich, "A novel
method for pulmonary emboli visualization from high-resolution CT
images," in Proc. SPIE Med. Imag., 2004, vol. 5367, pp. 161-170.
[3] Z. Maizlin, P. Vos, M. Gody, and P. Cooperberg, "Computer-aided
detection of pulmonary embolism on CT angiography: Initial
experience," in Proc. Annu. Meeting RSNA, 2006.
[4] C. Zhou et al., "Computerized detection of pulmonary embolism in 3-D
CT images: Vessel tracking and segmentation techniques," in Proc. SPIE
Med. Imag., 2003, vol. 5032, pp. 1613-1620.
[5] Özekes S, Osman O, Ucan O N, "Nodule Detection in the Lung Region,
which is Segmented with Genetic Cellular Neural Networks, Using 3D
Template Matching with Fuzzy Rule Based Thresholding", Korean
Journal of Radiology, Vol.9, No.1, pp.1-9, 2008.
[6] Özekes S, Osman O, "Computerized Lung Nodule Detection Using 3D
Feature Extraction and Learning Based Algorithms", Journal of Medical
Systems, Volume: 34 Issue: 2 Pages: 185-194, APR 2010.
[7] Sluimer I, Waes P, Viergever M, and Ginneken B, "Computeraided
diagnosis in high resolution CT of the lungs", Med. Phys. 30, 3081-
3090, 2003.
[8] Uchiyama Y, Katsuragawa S, Abe H, Shiraishi J, Li F, Li Q, Zhang C,
Suzuki K, and Doi K, "Quantitative computerized analysis of diffuse
lung disease in high-resolution computed tomography", Med. Phys. 30,
2440-2454, 2003.
[9] Uppaluri R, Hoffman E, Sonka M, Hartley P, "Hunninghake, and G.
Mclennan, "Computer recognition of regional lung disease patterns",
Am. J. Respir. Crit. Care Med. 160, 648-654, 1999.
[10] Xu Y, Sonka M, McLennan G, Guo J, and Hoffman E, "MDCTbased 3-
D texture classification of emphysema and early smoking related lung
pathologies", IEEE Trans. Med. Imaging 25, 464-475, 2006
[11] Uppaluri R, Mitsa T, Sonka M, Hoffman E, and McLennan G,
"Quantification of pulmonary emphysema from lung computed
tomography images", Am. J. Respir. Crit. Care Med. 156, 248-254,
1997.
[12] Buelow T, Wiemker R, Blaffert T, Lorenz C, and Renisch S, "Automatic
extraction of the pulmonary artery tree from multi-slice CT data", in
SPIE Medical Imaging, Apr. 2005, vol. 5746, pp. 730-740.
[13] Kiraly A P, Pichon E, Naidich D P, and Novak C L, "Analysis of arterial
subtrees affected by pulmonary emboli", in SPIE Medical Imaging, May
2004, vol. 5370, pp. 1720-1729.
[14] Zhou X, Hayashi T, Hara T, Fujita H, Yokoyama R, Kiryu T, and Hoshi
H, "Automatic segmentation and recognition of anatomical lung
structures from high-resolution chest CT images", Computerized
Medical Imaging and Graphics 30, pp. 299-313, 2006.
[15] Masutani Y, Schiemann T, and Höhne K H, "Vascular shape
segmentation and structure extraction using a shape-based regiongrowing
model", In Medical Image Analysis and Computer Assisted
Intervention (MICCAI), pages 1242-1249, October 1998.
[16] ├ûzkan H., Osman O., ┼×ahin S., Atasoy M. M., Barutca H., Boz A.F.,
Olsun A., "Lung Segmentation Algorithm for CAD System in CTA
Images" World Academy of science Engineering end Technology
(ICBCBBE 2011), July 24- 26, 2011, Paris
[1] A. Kiraly, "A comparison of 2D and 3-D evaluation methods for
pulmonary embolism detection in CT images," in Proc. SPIE Med.
Imag, 2006, vol. 6146.
[2] E. Pichon, C. L. Novak, A. P. Kiraly, and D. P. Naidich, "A novel
method for pulmonary emboli visualization from high-resolution CT
images," in Proc. SPIE Med. Imag., 2004, vol. 5367, pp. 161-170.
[3] Z. Maizlin, P. Vos, M. Gody, and P. Cooperberg, "Computer-aided
detection of pulmonary embolism on CT angiography: Initial
experience," in Proc. Annu. Meeting RSNA, 2006.
[4] C. Zhou et al., "Computerized detection of pulmonary embolism in 3-D
CT images: Vessel tracking and segmentation techniques," in Proc. SPIE
Med. Imag., 2003, vol. 5032, pp. 1613-1620.
[5] Özekes S, Osman O, Ucan O N, "Nodule Detection in the Lung Region,
which is Segmented with Genetic Cellular Neural Networks, Using 3D
Template Matching with Fuzzy Rule Based Thresholding", Korean
Journal of Radiology, Vol.9, No.1, pp.1-9, 2008.
[6] Özekes S, Osman O, "Computerized Lung Nodule Detection Using 3D
Feature Extraction and Learning Based Algorithms", Journal of Medical
Systems, Volume: 34 Issue: 2 Pages: 185-194, APR 2010.
[7] Sluimer I, Waes P, Viergever M, and Ginneken B, "Computeraided
diagnosis in high resolution CT of the lungs", Med. Phys. 30, 3081-
3090, 2003.
[8] Uchiyama Y, Katsuragawa S, Abe H, Shiraishi J, Li F, Li Q, Zhang C,
Suzuki K, and Doi K, "Quantitative computerized analysis of diffuse
lung disease in high-resolution computed tomography", Med. Phys. 30,
2440-2454, 2003.
[9] Uppaluri R, Hoffman E, Sonka M, Hartley P, "Hunninghake, and G.
Mclennan, "Computer recognition of regional lung disease patterns",
Am. J. Respir. Crit. Care Med. 160, 648-654, 1999.
[10] Xu Y, Sonka M, McLennan G, Guo J, and Hoffman E, "MDCTbased 3-
D texture classification of emphysema and early smoking related lung
pathologies", IEEE Trans. Med. Imaging 25, 464-475, 2006
[11] Uppaluri R, Mitsa T, Sonka M, Hoffman E, and McLennan G,
"Quantification of pulmonary emphysema from lung computed
tomography images", Am. J. Respir. Crit. Care Med. 156, 248-254,
1997.
[12] Buelow T, Wiemker R, Blaffert T, Lorenz C, and Renisch S, "Automatic
extraction of the pulmonary artery tree from multi-slice CT data", in
SPIE Medical Imaging, Apr. 2005, vol. 5746, pp. 730-740.
[13] Kiraly A P, Pichon E, Naidich D P, and Novak C L, "Analysis of arterial
subtrees affected by pulmonary emboli", in SPIE Medical Imaging, May
2004, vol. 5370, pp. 1720-1729.
[14] Zhou X, Hayashi T, Hara T, Fujita H, Yokoyama R, Kiryu T, and Hoshi
H, "Automatic segmentation and recognition of anatomical lung
structures from high-resolution chest CT images", Computerized
Medical Imaging and Graphics 30, pp. 299-313, 2006.
[15] Masutani Y, Schiemann T, and Höhne K H, "Vascular shape
segmentation and structure extraction using a shape-based regiongrowing
model", In Medical Image Analysis and Computer Assisted
Intervention (MICCAI), pages 1242-1249, October 1998.
[16] ├ûzkan H., Osman O., ┼×ahin S., Atasoy M. M., Barutca H., Boz A.F.,
Olsun A., "Lung Segmentation Algorithm for CAD System in CTA
Images" World Academy of science Engineering end Technology
(ICBCBBE 2011), July 24- 26, 2011, Paris
@article{"International Journal of Medical, Medicine and Health Sciences:55602", author = "H. Özkan", title = "Segmental and Subsegmental Lung Vessel Segmentation in CTA Images", abstract = "In this paper, a novel and fast algorithm for segmental
and subsegmental lung vessel segmentation is introduced using
Computed Tomography Angiography images. This process is quite
important especially at the detection of pulmonary embolism, lung
nodule, and interstitial lung disease. The applied method has been
realized at five steps. At the first step, lung segmentation is achieved.
At the second one, images are threshold and differences between the
images are detected. At the third one, left and right lungs are gathered
with the differences which are attained in the second step and Exact
Lung Image (ELI) is achieved. At the fourth one, image, which is
threshold for vessel, is gathered with the ELI. Lastly, identifying and
segmentation of segmental and subsegmental lung vessel have been
carried out thanks to image which is obtained in the fourth step. The
performance of the applied method is found quite well for
radiologists and it gives enough results to the surgeries medically.", keywords = "Computed tomography angiography (CTA),
Computer aided detection (CAD), Lung segmentation, Lung vessel
segmentation", volume = "6", number = "5", pages = "147-3", }
