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.
This paper presents a method for segmenting internal brain structures in MR
images. It introduces prior information in an original way through descriptions
of the spatial arrangement of structures
by means of spatial relations, which are represented in the fuzzy set framework.
The method is hierarchical as the segmentation of a given structure
is based on
the previously segmented ones. The processing of each structure is
decomposed into two stages: an initialization stage which makes extensive
of prior knowledge and a refinement stage using a 3D deformable model.
The deformable model is guided by an external force representing the combination
of a classical data term derived from an edge map and a force corresponding
to a given spatial relation. We propose different ways to compute a force from
a fuzzy set representing a relation or a combination of relations.
Results obtained for
the lateral ventricles, the third ventricle, the caudate nuclei and the thalami are promising.
The proposed combination of spatial relations and deformable models has proved to be very useful to segment
parts of the structures were no visible edges are present, improving the segmentation accuracy.
Conference Committee Involvement (2)
19 February 2019 | San Diego, California, United States