Name
Title | ||
|---|---|---|
Texture measures combination for improved meningioma classification of histopathological images |
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.patcog.2010.01.005 | Pattern Recognition |
Keywords | Field | DocType |
improved meningioma classification,run-length matrix texture measure,meningioma texture feature,histopathological image,texture measures combination,meningioma tissue,optimum texture measure combination,feature extraction,meningioma tumour,bhattacharyya distance,texture measure,naïve bayesian classifier,different texture measure,histopathological images,overall classification accuracy,coloured texture analysis,best accuracy,meningioma,meningioma subtype discrimination,region of interest,bayesian classifier | Random field,Bhattacharyya distance,Pattern recognition,Naive Bayes classifier,Markov model,Feature extraction,Artificial intelligence,RGB color model,Region of interest,Morphological gradient,Mathematics | Journal |
Volume | Issue | ISSN |
43 | 6 | Pattern Recognition |
Citations | PageRank | References |
21 | 0.95 | 18 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Omar S. Al-Kadi | 1 | 32 | 4.32 |