Lobar Fissure Extraction in Isotropic CT Lung Images - An Application to Cancer Identification

Print
International Journal of Computer Applications
© 2011 by IJCA Journal
Volume 33 - Number 6
Year of Publication: 2011
Authors:
T. Manikandan
Dr. N. Bharathi
10.5120/4024-5735

T Manikandan and Dr. N Bharathi. Article: Lobar Fissure Extraction in Isotropic CT Lung Images - An Application to Cancer Identification. International Journal of Computer Applications 33(6):17-21, November 2011. Full text available. BibTeX

@article{key:article,
	author = {T. Manikandan and Dr. N. Bharathi},
	title = {Article: Lobar Fissure Extraction in Isotropic CT Lung Images - An Application to Cancer Identification},
	journal = {International Journal of Computer Applications},
	year = {2011},
	volume = {33},
	number = {6},
	pages = {17-21},
	month = {November},
	note = {Full text available}
}

Abstract

The lungs are the very important organ for the human beings for respiration. It consists of five distinct lobes which are separated by three fissures (the boundaries of lung lobes are the areas containing fissures and having absence of bronchial trees). They are two oblique fissures (left and right) and one horizontal fissure. The identification of the lobar fissures in isotropic Computed Tomography (CT) image is very difficult even for the experienced surgeons because of its variable shape along with low contrast and high noise association with it. Final stage of treating the lung cancer is surgical removal of the diseased lung. Therefore, it is necessary to identify the cancer location by extracting the lobar fissures before they plan for the surgery. This paper presents an automated method to extract the left and right oblique fissures from the CT lung images by Dual Tree Complex Wavelet Transform (DTCWT). The obtained results show that the DTCWT can help the surgeon to identify the lobar fissures (right oblique and left oblique) in CT images.

Reference

  • http://www.emedicinehealth.com/lung_cancer/article_em.htmung lobes.
  • Kuhnigk, J.M., Hahn, H.K, Hindennach., M. Dicken, V., Krass, S., and Peitgen, H.O. 2003. Lung Lobe Segmentation by Anatomy Guided 3-D Watershed Transform. In proceeding of SPIE (Medical Imaging). 1482-1490.
  • Ukil, S., Hoffman, E.A., and Reinhardt, J.M. 2005. Automatic lung lobe segmentation in X-ray CT images by 3D watershed transform using anatomic information from the segmented airway tree. In proceedings of SPIE (Medical Imaging). 556–567.
  • Qiao, W., Yaoping, H., Gary, G., and MacGregor, J.H. 2007. Segmentation of lung lobes in CT images using Wavelet transformation. In proceedings of 29th international conference of IEEE. 5551-5554.
  • Qiao, W., Yaoping, H., Gary, G., and MacGregor, J.H. 2009. Segmentation of Lung Lobes in High Resolution Isotropic CT Images. In IEEE Transactions on biomedical engineering. 1383-1393.
  • Sonka, M., Hlavac, V., and Boyle, R. 2008. Image processing, Analysis, and Machine Vision. 3rd edition. Thomson Learning.
  • Anitha, S., and Sridher, S. 2010. Segmentation of lung lobes and nodules in CT images. In International journal of signal processing and image processing (SIPIJ). 1-12.
  • Haralick, R.M., Stenberg, S.R., and Zhuang, X. 2007. Image analysis using mathematical morphology. In IEEE Transactions on Pattern Analysis and Machine Intelligence. 532–550.
  • Kumar, S.N., and Kavitha, V. 2011. Automatic segmention of lung lobes and fissures for surgical planning. In Proceedings of ICETECT. 546-550.
  • Aryaz bhadarani, and Runyi, yu. 2007. A Dual Tree Complex Wavelet with application in image denoising. In IEEE International conference on Signal Processing and communications. 1203-1206.