Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Segmentation of brain tumor images based on atlas-registration combined with a Markov-Random-Field lesion growth model

We present an automatic method to segment brain tissues from volumetric MRI brain tumor images. The method is based on non-rigid registration of an average atlas in combination with a biomechanically justified tumor growth model to simulate soft-tissue deformations caused by the tumor mass-effect. The tumor growth model, which is formulated as a mesh-free Markov Random Field energy minimization problem, ensures correspondence between the atlas and the patient image, prior to the registration step. The method is non-parametric, simple and fast compared to other approaches while maintaining similar accuracy. It has been evaluated qualitatively and quantitatively with promising results on eight datasets comprising simulated images and real patient data.

The word-length effect in acquired alexia, and real and virtual hemianopia

A word-length effect is often described in pure alexia, with reading time proportional to the number of letters in a word. Given the frequent association of right hemianopia with pure alexia, it is uncertain whether and how much of the word-length effect may be attributable to the hemifield loss. To isolate the contribution of the visual field defect, we simulated hemianopia in healthy subjects with a gaze-contingent paradigm during an eye-tracking experiment. We found a minimal word-length effect of 14 ms/letter for full-field viewing, which increased to 38 ms/letter in right hemianopia and to 31 ms/letter in left hemianopia. We found a correlation between mean reading time and the slope of the word-length effect in hemianopic conditions. The 95% upper prediction limits for the word-length effect were 51 ms/letter in subjects with full visual fields and 161 ms/letter with simulated right hemianopia. These limits, which can be considered diagnostic criteria for an alexic word-length effect, were consistent with the reading performance of six patients with diagnoses based independently on perimetric and imaging data: two patients with probable hemianopic dyslexia, and four with alexia and lesions of the left fusiform gyrus, two with and two without hemianopia. Two of these patients also showed reduction of the word-length effect over months, one with and one without a reading rehabilitation program. Our findings clarify the magnitude of the word-length effect that originates from hemianopia alone, and show that the criteria for a word-length effect indicative of alexia differ according to the degree of associated hemifield loss.