TY  - JOUR
T1  - Semantic Classification and Region Growing of Brain MRI using Canfis Model for Tumor Identification
AU - SelvaBhuvaneswari, K. AU - Geetha, P. 
JO  - Asian Journal of Information Technology
VL  - 13
IS  - 5
SP  - 274
EP  - 281
PY  - 2014
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2014.274.281
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2014.274.281
KW  - Segmentation
KW  -pathological tissue
KW  -artificial neural network
KW  -white matter
KW  -gray matter
KW  -cerebrospinal fluid
KW  -tumor
AB  - Semantic interpretation and understanding of medical images 
  is an important goal of visual recognition and offers a large variety of possible 
  applications. This research involves semantic segmentation for pixel-wise classification 
  of images for tumor identification. Classification of brain MRI is a difficult 
  task for tumor identification due to variance in features. Hence, the exact 
  features that involve in the classification and identification of region of 
  interest have to be identified. Statistical features using wavelet and semantic 
  features using novel method are extracted from the input MRI image. These features 
  are fed into the neurofuzzy classifier for normal and abnormal image identification. 
  Further, the pathological tissue segmentation is done using semantic region 
  growing approach and identification of tumor is done. The results of implementation 
  shows the efficiency of semantic segmentation technique in identifying the pathological 
  tissues accurately from the MRI images. The performance of the segmentation 
  technique is evaluated by performance measures such as accuracy, specificity 
  and sensitivity.
ER  -