White matter hyperintensities (WMH), commonly seen on FLAIR images in elderly people, are a risk factor for
dementia onset and have been associated with motor and cognitive deficits. We present here a method to fully
automatically segment WMH from T1 and FLAIR images. Iterative steps of non linear diffusion followed by watershed
segmentation were applied on FLAIR images until convergence. Diffusivity function and associated contrast parameter
were carefully designed to adapt to WMH segmentation. It resulted in piecewise constant images with enhanced contrast
between lesions and surrounding tissues. Selection of WMH areas was based on two characteristics: 1) a threshold
automatically computed for intensity selection, 2) main location of areas in white matter. False positive areas were
finally removed based on their proximity with cerebrospinal fluid/grey matter interface. Evaluation was performed on 67
patients: 24 with amnestic mild cognitive impairment (MCI), from five different centres, and 43 with Cerebral
Autosomal Dominant Arteriopathy with Subcortical Infarcts and Leukoaraiosis (CADASIL) acquired in a single centre.
Results showed excellent volume agreement with manual delineation (Pearson coefficient: r=0.97, p<0.001) and
substantial spatial correspondence (Similarity Index: 72%±16%). Our method appeared robust to acquisition differences
across the centres as well as to pathological variability.
This paper introduces a general framework for spatial prior in SVM-based classification of brain images based on
Laplacian regularization. Most existing methods include spatial prior by adding a feature aggregation step before
the SVM classification. The problem of the aggregation step is that the individual information of each feature
is lost. Our framework enables to avoid this shortcoming by including the spatial prior directly in the SVM.
We demonstrate that this framework can be used to derive embedded regularization corresponding to existing
methods for classification of brain images and propose an efficient way to implement them. This framework is
illustrated on the classification of MR images from 55 patients with Alzheimer's disease and 82 elderly controls
selected from the ADNI database. The results demonstrate that the proposed algorithm enables introducing
straightforward and anatomically consistent spatial prior into the classifier.
The hippocampus (Hc) and the amygdala (Am) are two cerebral structures that play a central role in main cognitive processes. Their segmentation allows atrophy in specific neurological illnesses to be quantified, but is made difficult by the complexity of the structures. In this work, a new algorithm for the simultaneous segmentation of Hc and Am based on competitive homotopic region deformations is presented. The deformations are constrained by relational priors derived from anatomical knowledge, namely probabilities for each structure around automatically retrieved landmarks at the border of the objects. The approach is designed to perform well on data from diseased subjects. The segmentation is initialized by extracting a bounding box and positioning two seeds; total execution time for both sides is between 10 and 15 minutes including initialization for the two structures. We present the results of validation based on comparison with manual segmentation, using volume error, spatial overlap and border distance measures. For 8 young healthy subjects the mean volume error was 7% for Hc and 11% for Am, the overlap: 84% for Hc and 83% for Am, the maximal distance: 4.2mm for Hc and 3.1mm for Am; for 4 Alzheimer's disease patients the mean volume error was 9% for Hc and Am, the overlap: 83% for Hc and 78% for Am, the maximal distance: 6mm for Hc and 4.4mm for Am. We conclude that the performance of the proposed method compares favourably with that of other published approaches in terms of accuracy and has a short execution time.
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