TY  - JOUR
T1  - Enhancing Surgical Visualization by Exploring 3D Volume Reconstruction from 2D Slices of CT Lung
AU - Amutha, A. AU - Wahidabanu, R.S.D. 
JO  - Asian Journal of Information Technology
VL  - 12
IS  - 7
SP  - 217
EP  - 227
PY  - 2013
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2013.217.227
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2013.217.227
KW  - Image segmentation
KW  -active contours
KW  -level set
KW  -3D voxel reconstruction
KW  -India
AB  - In recent years, lung tumor diagnosis and the projection of 
  tumor segmentation in 3D has gained significant momentous in the therapeutic 
  field. Establishing the dissimilarity exists in the three dimensional volume 
  representation of tumor cells affords more information which can sharpen the 
  treatment of a multiplicity of tumors. The volume reconstruction information 
  is indispensable in the case of surgical operations. This process introduces 
  a contour based segmentation algorithm to acquire the appropriate differentiation 
  of pixel boundary that scrutinizes the exact difference between tumor and non 
  tumor cells along the tumor boundary. With the aid of aforementioned formulation, 
  extracted tumor part pixels are reconstructed for the entire 2D slices of the 
  patient data set. Proposal research on 3D voxel reconstruction relies on encountering 
  the isosurfaces. Originally, volume data are subjected to the smoothening process 
  which computes the isosurface data from the smoothened volume data. The generated 
  outcome of this process comprises the vertices and faces of the isosurfaces 
  and directly flows to patch the data. Exploit the 3D reconstructed model to 
  enumerate the voxel damaged by tumor. Proposal research associated with the 
  percentage of damaged voxel along with accurate and reliable perception, simplifies 
  the physician task in lung tumor diagnosis and assist the surgical procedure. 
  Experimental evaluations across the wide range of images show the superiority 
  of the proposed research with the classification accuracy rate of 99.33%.
ER  -