Automated processing pipeline for texture analysis of childhood brain tumours based on multimodal magnetic resonance imaging

Primary brain tumours are the most common solid tumours found in children and are an important cause of morbidity and mortality. Magnetic resonance imaging (MRI) is commonly used for non-invasive early-detection, diagnosis, delineation of tumours for treatment planning and assessment of post treatment changes. Different MRI modalities provide complementary contrast of tumour tissues, which can have varying degrees of heterogeneity and diffusivity in different tumour types. A variety of texture analysis methods have been shown to reveal tumour histological types. It is hypothesized that textural features, based on conventional and diffusion MRI modalities, would differentiate the characteristics of tumours. Tumour extraction is also a significant procedure needed to obtain a true tumour region. Semi-automated segmentation methods were applied, in comparison with the gold standard of manual segmentation by an expert, in order to speed up a manual segmentation approach and reduce any bias effects. In this study, we present an automatic processing pipeline for the characterization of brain tumours, based on texture analysis. We apply this to a multi-centre dataset of paediatric brain tumours and investigate the accuracy of tumour classification, based on textural features of diffusion and conventional MR images.

[1]  Jianqing Fan,et al.  Sure independence screening for ultrahigh dimensional feature space , 2006, math/0612857.

[2]  G. Sevlever,et al.  Characterization of brain tumors by MRS, DWI and Ki-67 labeling index , 2005, Journal of Neuro-Oncology.

[3]  T. Carpenter,et al.  Predicting patterns of glioma recurrence using diffusion tensor imaging , 2007, European Radiology.

[4]  H. K. Abhyankar,et al.  A Hybrid Segmentation Model based on Watershed and Gradient Vector Flow for the Detection of Brain Tumor 29 , 2009 .

[5]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[6]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[8]  Jerry L. Prince,et al.  Gradient vector flow: a new external force for snakes , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[9]  A. Kassner,et al.  Texture Analysis: A Review of Neurologic MR Imaging Applications , 2010, American Journal of Neuroradiology.

[10]  Stephen M. Smith,et al.  A global optimisation method for robust affine registration of brain images , 2001, Medical Image Anal..

[11]  F. Cendes,et al.  Texture analysis of medical images. , 2004, Clinical radiology.

[12]  Issam Dagher,et al.  WaterBalloons: A Hybrid Watershed Balloon Snake Segmentation , 2007, 2007 International Joint Conference on Neural Networks.

[13]  Chenyang Xu Chapter 10 – Gradient Vector Flow Deformable Models , 2009 .

[14]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[15]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[16]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[17]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[18]  Xiaoou Tang,et al.  Texture information in run-length matrices , 1998, IEEE Trans. Image Process..

[19]  David A Clausi An analysis of co-occurrence texture statistics as a function of grey level quantization , 2002 .

[20]  Mark W. Woolrich,et al.  Bayesian analysis of neuroimaging data in FSL , 2009, NeuroImage.

[21]  Michal Strzelecki,et al.  MaZda - A software package for image texture analysis , 2009, Comput. Methods Programs Biomed..

[22]  Jianbo Shi,et al.  Spectral segmentation with multiscale graph decomposition , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[23]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[24]  Leen-Kiat Soh,et al.  Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices , 1999, IEEE Trans. Geosci. Remote. Sens..

[25]  Kristina Potocki,et al.  The differences of water diffusion between brain tissue infiltrated by tumor and peritumoral vasogenic edema. , 2009, Clinical imaging.

[26]  R. M. Haralick,et al.  Textural features for image classification. IEEE Transaction on Systems, Man, and Cybernetics , 1973 .