Automatic glioma characterization from dynamic susceptibility contrast imaging: Brain tumor segmentation using knowledge‐based fuzzy clustering

To assess whether glioma volumes from knowledge‐based fuzzy c‐means (FCM) clustering of multiple MR image classes can provide similar diagnostic efficacy values as manually defined tumor volumes when characterizing gliomas from dynamic susceptibility contrast (DSC) imaging.

[1]  Edward J. Dropcho,et al.  Low-grade gliomas in adults , 2004, Current treatment options in neurology.

[2]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[3]  Edward E Graves,et al.  Metabolic imaging of low-grade gliomas with three-dimensional magnetic resonance spectroscopy. , 2002, International journal of radiation oncology, biology, physics.

[4]  Søren Christensen,et al.  Automatic selection of arterial input function using cluster analysis , 2006, Magnetic resonance in medicine.

[5]  Glyn Johnson,et al.  Comparison of region‐of‐interest analysis with three different histogram analysis methods in the determination of perfusion metrics in patients with brain gliomas , 2007, Journal of magnetic resonance imaging : JMRI.

[6]  Barry T. Thomas,et al.  Using Neural Networks to Automatically Detect Brain Tumours in MR Images , 1997, Int. J. Neural Syst..

[7]  G Johnson,et al.  Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas. , 2007, AJNR. American journal of neuroradiology.

[8]  B. Rosen,et al.  High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: Mathematical approach and statistical analysis , 1996, Magnetic resonance in medicine.

[9]  P Abdolmaleki,et al.  Neural networks analysis of astrocytic gliomas from MRI appearances. , 1997, Cancer letters.

[10]  Lawrence O. Hall,et al.  Automatic tumor segmentation using knowledge-based techniques , 1998, IEEE Transactions on Medical Imaging.

[11]  R. Kikinis,et al.  Automated segmentation of MR images of brain tumors. , 2001, Radiology.

[12]  C R Bird,et al.  Gliomas: classification with MR imaging. , 1990, Radiology.

[13]  Lawrence O. Hall,et al.  Automatic segmentation of non-enhancing brain tumors in magnetic resonance images , 2001, Artif. Intell. Medicine.

[14]  Lucila Ohno-Machado,et al.  Supratentorial low-grade glioma resectability: statistical predictive analysis based on anatomic MR features and tumor characteristics. , 2006, Radiology.

[15]  T. Taxt,et al.  Multispectral analysis of multimodal images , 2009, Acta oncologica.

[16]  T. Carpenter,et al.  Improved delineation of glioma margins and regions of infiltration with the use of diffusion tensor imaging: an image-guided biopsy study. , 2006, AJNR. American journal of neuroradiology.

[17]  G Johnson,et al.  Glial neoplasms: dynamic contrast-enhanced T2*-weighted MR imaging. , 1999, Radiology.

[18]  A. Bjørnerud,et al.  Glioma grading by using histogram analysis of blood volume heterogeneity from MR-derived cerebral blood volume maps. , 2008, Radiology.

[19]  S.M. Krishnan,et al.  Extraction of Brain Tumor from MR Images Using One-Class Support Vector Machine , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[20]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[21]  J. Ashburner,et al.  Multimodal Image Coregistration and Partitioning—A Unified Framework , 1997, NeuroImage.

[22]  Cheuk Y. Tang,et al.  Quantification of human atherosclerotic plaques using spatially enhanced cluster analysis of multicontrast‐weighted magnetic resonance images , 2004, Magnetic resonance in medicine.

[23]  R. Velthuizen,et al.  Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. , 2004, International journal of radiation oncology, biology, physics.

[24]  B. D. Ward,et al.  Characterization of a first-pass gradient-echo spin-echo method to predict brain tumor grade and angiogenesis. , 2004, AJNR. American journal of neuroradiology.

[25]  Glyn Johnson,et al.  Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. , 2003, AJNR. American journal of neuroradiology.

[26]  B. Rosen,et al.  Perfusion imaging with NMR contrast agents , 1990, Magnetic resonance in medicine.

[27]  Michael H Lev,et al.  Dynamic magnetic resonance perfusion imaging of brain tumors. , 2004, The oncologist.

[28]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[29]  C. Meltzer,et al.  Brain tumor volume measurement: comparison of manual and semiautomated methods. , 1999, Radiology.

[30]  L O Hall,et al.  Medical image analysis with fuzzy models , 1997, Statistical methods in medical research.

[31]  P. Kelly,et al.  Perfusion Magnetic Resonance Imaging Predicts Patient Outcome as an Adjunct to Histopathology: A Second Reference Standard in the Surgical and Nonsurgical Treatment of Low-grade Gliomas , 2006, Neurosurgery.

[32]  R. L. Butterfield,et al.  Multispectral analysis of magnetic resonance images. , 1985, Radiology.

[33]  Glyn Johnson,et al.  Low-grade gliomas: dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging--prediction of patient clinical response. , 2006, Radiology.

[34]  Glyn Johnson,et al.  Relative cerebral blood volume measurements in intracranial mass lesions: interobserver and intraobserver reproducibility study. , 2002, Radiology.

[35]  D. Cavouras,et al.  An image-analysis system based on support vector machines for automatic grade diagnosis of brain-tumour astrocytomas in clinical routine , 2005, Medical informatics and the Internet in medicine.

[36]  R M Weisskoff,et al.  Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. , 2006, AJNR. American journal of neuroradiology.

[37]  Michael H Lev,et al.  Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas [corrected]. , 2004, AJNR. American journal of neuroradiology.

[38]  B. Drayer,et al.  Human cerebral gliomas: correlation of postmortem MR imaging and neuropathologic findings. , 1989, Radiology.