KaydedilenlerDerginizi ekleyin

Dergiler / Istanbul University Journal of Electrical and Electronics Engineering / 2012 / Cilt: 12 - Sayı: 1

3D Lung vessel segmentation ın computed tomography angiography images

Haydar ÖZKAN
Sayfa
1437–1443
Yayın Tarihi
2022-10-18
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The performance of the method for lung vessel segmentation is found to be quite well for radiologists and it gives enough results to the surgeries medically.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:In this paper, a novel lung vessel segmentation method is introduced.
📊
Temel Bulgular & Veri:If there is pulmonary emboli (PE), heart disease, or abnormal tissues, vessel structure doesn t regularly continue and decreases the sensitivity of segmentation.
💡
Sonuç & Katkı:The performance of the method for lung vessel segmentation is found to be quite well for radiologists and it gives enough results to the surgeries medically.
🔬Tür:Araştırma Makalesi
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Alan Parametreleri

Abstract

In this paper, a novel lung vessel segmentation method is introduced. In this method, some Reference Points (RPs) were determined by making use of the properties of unchangeable anatomical structure. Due to these RPs, truncus, left-right pulmonary artery, lobar segment vessels have been segmented and subsegment vessels have been detected by looking at the differences of intensities in lung region. If there is pulmonary emboli (PE), heart disease, or abnormal tissues, vessel structure doesn t regularly continue and decreases the sensitivity of segmentation. Using RPs, vessel structure becomes more definite and sensitivity of the segmentation increases. CTA images belonging 30 patients including different disease are examined and 95% of sensitivity is obtained. The performance of the method for lung vessel segmentation is found to be quite well for radiologists and it gives enough results to the surgeries medically.

🔖

Bu Makaleye Atıf Yap

ÖZKAN, H. (2022). 3D Lung vessel segmentation ın computed tomography angiography images. Istanbul University Journal of Electrical and Electronics Engineering, Cilt: 12 - Sayı: 1, 1437–1443. https://ajindex.com/articles/543686

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (13)

  1. J. M. Remy, L. I. Tillie, D. Szapiro, “CT angiography of pulmonary embolism in patients with underlying respiratory disease: impact of multislice CT on image quality and negative predictive value”. Eur Radiol 12:1971–1978, 2002. J. E. Dalen, “Pulmonary embolism: What have we learned since Virchow”, Chest,122: 1440-1446, 2002.
  2. H. P. Chan, L. Hadjiiski, C. Zhou, et al., “Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography – a review”, Acad Radiol 15:535–555, 2008.
  3. Y. Masutani, H. Macmahon, and K. Doi, “Computer-assisted detection of pulmonary embolism”, In SPIE Medical Imaging 2000,
  4. San Diego, USA, February 2000.
  5. J. N. Kaftan, A. P. Kiraly, A. Bakai, M. Das, C. L. Novak, and T. Aach, “Fuzzy Pulmonary Vessel Segmentation in Contrast Enhanced CT data”, Medical Imaging, February 2008.
  6. S. Ozekes, O. Osman, “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. S. Ozekes, O. Osman, O. N. Ucan, “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.
  8. R. Uppaluri, E. Hoffman, M. Sonka, P. Hartley, “Hunninghake, and G. Mclennan, “Computer recognition of regional lung disease patterns”, Am. J. Respir. Crit. Care Med. 160, 648–654, 1999.
  9. Y. Uchiyama, S. Katsuragawa, H. Abe, J. Shiraishi, F. Li, Q. Li, C. Zhang, K. Suzuki, and K. Doi, “Quantitative computerized analysis of diffuse lung disease in high-resolution computed tomography”, Med. Phys. 30, 2440–2454, 2003.
  10. I. Sluimer, P. Waes, M. Viergever, and B. Ginneken, “Computer aided diagnosis in high resolution CT of the lungs”, Med. Phys. 30, 3081–3090, 2003.
  11. R. Uppaluri, T. Mitsa, M. Sonka, E. Hoffman, and G. McLennan, “Quantification of pulmonary emphysema from lung computed tomography images”, Am. J. Respir. Crit. Care Med. 156, 248–254, 1997.
  12. Y. Xu, M. Sonka, G. McLennan, J. Guo, and E. Hoffman, “MDCTbased 3-D texture classification of emphysema and early smoking related lung pathologies”, IEEE Trans. Med. Imaging 25, 464–475, 2006.
  13. A. P. Kiraly, E. Pichon, D. P. Naidich, and C. L. Novak, “Analysis of arterial subtrees affected by pulmonary emboli”, in SPIE Conference on Medical Imaging, May 2004, vol. 5370, pp. 1720–1729. T. Buelow, R. Wiemker, T. Blaffert, C. Lorenz, and S. Renisch, “Automatic extraction of the