Classification of bladder cancer on radiotherapy planning CT images using textural features

Hanqing Liao*, William H. Nailon, Duncan B. McLaren, Steve McLaughlin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Highly reliable classification of anatomical regions is an important step in the delineation of the gross tumour volume (GTV) in computed tomography (CT) images during radiotherapy planning. In this study pixel-based statistics such as mean and variance were insufficient for classifying the bladder, rectum and a control region. Statistical texture analysis were used to extract features from gray-tone spatial dependence matrices (GTSDM). The features were de-correlated and reduced using principal component analysis (PCA), and the principal components (PC) were classified by a naive Bayes classifier (NBC). The results suggests that the three most significant PC of the 56 features from GTSDM with distances d = 1,2,3,4 give the highest average correct classification percentage.

Original languageEnglish
Title of host publication2010 18th European Signal Processing Conference
Number of pages5
Publication statusPublished - 2010
Event18th European Signal Processing Conference 2010 - Aalborg, Denmark
Duration: 23 Aug 201027 Aug 2010

Publication series

NameEuropean Signal Processing Conference
ISSN (Print)2219-5491


Conference18th European Signal Processing Conference 2010
Abbreviated titleEUSIPCO 2010

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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