title: Automatic segmentation of colon in 3D CT images and removal of opacified fluid using cascade feed forward neural network.
abstract: OBJECTIVE
  Colon segmentation is an essential step in the development of computer-aided diagnosis systems based on computed tomography (CT) images. The requirement for the detection of the polyps which lie on the walls of the colon is much needed in the field of medical imaging for diagnosis of colorectal cancer.
  
  
  METHODS
  The proposed work is focused on designing an efficient automatic colon segmentation algorithm from abdominal slices consisting of colons, partial volume effect, bowels, and lungs. The challenge lies in determining the exact colon enhanced with partial volume effect of the slice. In this work, adaptive thresholding technique is proposed for the segmentation of air packets, machine learning based cascade feed forward neural network enhanced with boundary detection algorithms are used which differentiate the segments of the lung and the fluids which are sediment at the side wall of colon and by rejecting bowels based on the slice difference removal method. The proposed neural network method is trained with Bayesian regulation algorithm to determine the partial volume effect.
  
  
  RESULTS
  Experiment was conducted on CT database images which results in 98% accuracy and minimal error rate.
  
  
  CONCLUSIONS
  The main contribution of this work is the exploitation of neural network algorithm for removal of opacified fluid to attain desired colon segmentation result.
authors.affiliation: Dr. N. G. P. Institute of Technology, Coimbatore 641048, India.
authors.forename: K
authors.initials: K
authors.lastname: Gayathri Devi
authors.affiliation: Vidhya Mandhir Institute of Technology, Tamilnadu, India.
authors.forename: R
authors.initials: R
authors.lastname: Radhakrishnan
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D003106:Colon / Q000000981:diagnostic imaging / Q000473:pathology; D003113:Colonoscopy / Q000379:methods; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*
references.citation: Comput Med Imaging Graph. 2009 Jun;33(4):325-31
references.pmid: 19304454
references.citation: IEEE Trans Med Imaging. 2006 Nov;25(11):1451-61
references.pmid: 17117774
references.citation: Neuroimage. 2006 Jul 1;31(3):1116-28
references.pmid: 16545965
references.citation: Acad Radiol. 2004 Feb;11(2):178-89
references.pmid: 14974593
references.citation: IEEE Trans Inf Technol Biomed. 2011 Sep;15(5):709-15
references.pmid: 21606039
references.citation: Phys Med Biol. 2010 Sep 21;55(18):5569-84
references.pmid: 20808031
references.citation: Comput Methods Programs Biomed. 2013 Jan;109(1):1-12
references.pmid: 22947429