title: Brownian motion curve-based textural classification and its application in cancer diagnosis.
abstract: OBJECTIVE
  To develop an automated diagnostic methodology based on textural features of the oral mucosal epithelium to discriminate normal and oral submucous fibrosis (OSF).
  
  
  METHODS
  A total of 83 normal and 29 OSF images from histopathologic sections of the oral mucosa are considered. The proposed diagnostic mechanism consists of two parts: feature extraction using Brownian motion curve (BMC) and design ofa suitable classifier. The discrimination ability of the features has been substantiated by statistical tests. An error back-propagation neural network (BPNN) is used to classify OSF vs. normal.
  
  
  RESULTS
  In development of an automated oral cancer diagnostic module, BMC has played an important role in characterizing textural features of the oral images. Fisher's linear discriminant analysis yields 100% sensitivity and 85% specificity, whereas BPNN leads to 92.31% sensitivity and 100% specificity, respectively.
  
  
  CONCLUSIONS
  In addition to intensity and morphology-based features, textural features are also very important, especially in histopathologic diagnosis of oral cancer. In view of this, a set of textural features are extracted using the BMC for the diagnosis of OSF. Finally, a textural classifier is designed using BPNN, which leads to a diagnostic performance with 96.43% accuracy. (Anal Quant
authors: School of Medical Science and Technology, Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology, Kharagpur, West Bengal, India. muthuk@iitkgp.ac.in
authors: Muthu Rama Krishnan
authors: MR
authors: Mookiah
authors: Pratik
authors: P
authors: Shah
authors: Chandan
authors: C
authors: Chakraborty
authors: Ajoy K
authors: AK
authors: Ray
mesh_terms: D000328:Adult; D000465:Algorithms; D006801:Humans; D009061:Mouth Mucosa / Q000473:pathology; D009062:Mouth Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000473:pathology