Texture measures combination for improved meningioma classification of histopathological images

Al-Kadi, Omar S. (2010) Texture measures combination for improved meningioma classification of histopathological images. Pattern Recognition, 43 (6). 2043 -2053. ISSN 0031-3203

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Abstract

Providing an improved technique which can assist pathologists in correctly classifying meningioma tumours with a significant accuracy is our main objective. The proposed technique, which is based on optimum texture measure combination, inspects the separability of the RGB colour channels and selects the channel which best segments the cell nuclei of the histopathological images. The morphological gradient was applied to extract the region of interest for each subtype and for elimination of possible noise (e.g. cracks) which might occur during biopsy preparation. Meningioma texture features are extracted by four different texture measures (two model-based and two statistical-based) and then corresponding features are fused together in different combinations after excluding highly correlated features, and a Bayesian classifier was used for meningioma subtype discrimination. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations in terms of quantitatively characterising the meningioma tissue, achieving an overall classification accuracy of 92.50%, improving from 83.75% which is the best accuracy achieved if the texture measures are used individually.

Item Type: Article
Keywords: Coloured texture analysis; Feature extraction; Histopathological images; Meningioma; Naïve Bayesian classifier; Bhattacharyya distance
Schools and Departments: School of Engineering and Informatics > Informatics
Subjects: R Medicine > RB Pathology
Q Science > QA Mathematics > QA0075 Electronic computers. Computer science
Q Science > QA Mathematics > QA0076 Computer software
Depositing User: Omar Al-Kadi
Date Deposited: 08 Apr 2010
Last Modified: 30 Nov 2012 16:53
URI: http://sro.sussex.ac.uk/id/eprint/2294
Google Scholar:10 Citations
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