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

Author

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

Author_Institution

Inst. of Digital Commun., Univ. of Edinburgh, Edinburgh, UK

fYear

2010

fDate

23-27 Aug. 2010

Firstpage

284

Lastpage

288

Abstract

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.

Keywords

Bayes methods; cancer; computerised tomography; decorrelation; feature extraction; image classification; image texture; medical image processing; principal component analysis; radiation therapy; tumours; CT images; bladder cancer classification; computed tomography image; feature decorrelation; feature extraction; gray tone spatial dependence matrices; gross tumour volume; naive Bayes classifier; principal component analysis; radiotherapy planning; statistical texture analysis; textural features; Bladder; Cancer; Computed tomography; Feature extraction; Planning; Principal component analysis; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal Processing Conference, 2010 18th European

Conference_Location

Aalborg

ISSN

2219-5491

Type

conf

Filename

7096709