TY  - JOUR
T1  - An Improved Local Ternary Pattern Based Tumour Classification of MRI of Brain
AU - Meenakshi, R. AU - Anandhakumar, P. 
JO  - Research Journal of Applied Sciences
VL  - 9
IS  - 2
SP  - 99
EP  - 103
PY  - 2014
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2014.99.103
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2014.99.103
KW  - Tumor
KW  -neoplasm
KW  -Magnetic Resonance Imaging (MRI)
KW  -diagnosis
KW  -existence
AB  - A tumor (American English) or tumour (British English) is 
  commonly used as a synonym for a neoplasm that appears enlarged in size. Neoplasm 
  is an abnormal mass of tissue as a result of abnormal growth or division of 
  cells. This tumor may be solid or fluid-filled. It can occur even in the brain. 
  As the brain is well protected by the skull, early and in-depth detection techniques 
  are needed for the identification of brain tumors which is one of the challenging 
  tasks. Magnetic Resonance Imaging (MRI) technique is mainly used for analyzing 
  the brain as the images produced are of high precision and applicability. Most 
  of the tumour identification methods make use of different machine learning 
  and segmentation techniques to provide improved detection accuracy. The challenge 
  lies in accurate diagnosis in spite of improved existing techniques. The objective 
  of the proposed research is to classify the brain MRI dataset for the existence 
  or non existence of tumors. The proposed method uses Multilevel Local Ternary 
  Pattern (MLTP) for pattern string generation. This is grouped for faster processing 
  and for further classification using Support Vector Machine (SVM). The generation 
  of pattern string gives the classification accuracy of 96% when compared to 
  the existing classification techniques.
ER  -