Journal Title : International Journal of Modern Trends in Engineering and Science

Author’s Name : Athul R S | Shally K

Volume 02 Issue 11  Year 2015

ISSN no: 2348-3121

Page no: 44-47

Abstract High-resolution   X-ray    computed    tomography (CT) imaging is widely used for clinical pulmonary applications.  The lung function varies regionally.  Because  diseases in the  pulmonary region  is usually  not  uniformly  distributed in  the  lungs,  it  is efficient while  studying  the  lungs  on  lobe- by-lobe  basis.  Automated extraction of lung lobes provides a foundation for computerized analysis of computed tomography scans   of the   chest.   Segmentation   of the   pulmonary   lobes is relevant in clinical practice   and particularly challenging for cases with some diseases like localized emphysema or incomplete fissures.  A method for automatic segmentation of pulmonary lobes from computed tomography scans of human chest is presented in this paper.   The work starts   with lung segmentation from the CT images based on region growing and standard image processing techniques.  A completely automatic and  faster  method  is  presented to  segment  the  lungs,  lobes and  pulmonary segments  from  chest  CT  scans  in  this  work. A  cost  image  for  the  clustering   is  computed   by  combining information  from  pulmonary  vessels,  bronchi,   and  fissures. A faster segmentation   method is presented which performs Accelerated K-means clustering on the cost image of computed tomography (CT) scans to subdivide the lungs into lobes.

Keywords— Lobe segmentation;  computed tomography;  Accelerated K-means


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