Brownian motion curve-based textural classification and its application in cancer diagnosis

M.R.K. Mookiah, P. Shah, C. Chakraborty, A.K. Ray

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)


Objective: To develop an automated diagnostic methodology based on textural features of the oral mucosal epithelium to discriminate normal and oral submucous fibrosis (OSF).
Study Design: 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 of a 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.
Conclusion: 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 c
Original languageEnglish
Pages (from-to)158-168
Number of pages11
JournalAnalytical and Quantitative Cytology and Histology
Issue number3
Publication statusPublished - Jun 2011


  • back-propagation algorithm
  • Brownian motion
  • computer-aided diagnosis
  • Fisher's linear discriminant function
  • oral mucosa
  • ithm, Brownian motion, computer-aided diagnosis, Fisher�s linear discriminant function, oral mucosa, oral submucous fibrosis
  • quantitative pathology


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