Texture measures combination for improved meningioma classification of histopathological images

TL;DR

This study introduces an optimized texture measure combination technique that enhances meningioma tumor classification accuracy in histopathological images by selecting the best RGB channel and combining multiple texture features.

Contribution

It proposes a novel method of combining texture measures and channel selection to significantly improve meningioma classification accuracy over individual measures.

Findings

Achieved 92.50% classification accuracy with combined texture measures.

Outperformed individual texture measures, which had a maximum of 83.75%.

Demonstrated the effectiveness of feature fusion and channel selection in histopathological image analysis.

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.