Publications by Olivier Cuisenaire

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We propose a deep study on tissue modelization and classification techniques on T1-weighted MR images. Six approaches have been taken into account to perform this validation study. We consider first the Finite Gaussian Mixture model (A-FGMM) and a Bayes classification. Second method is the same as A-FGMM but introducing a Hidden Markov Random Field (HMRF) model to encode spatial information and classification is then performed by Maximum a Posteriori (MAP). Third, we study a method that models mixture tissu

We propose a method for the automatic segmentation, recognition and measurement of neuronal fibers in microscopic images of nerves. This permits a quantitative analysis of the distribution of the areas of the fibers, while nowadays such morphometrical methods are limited by the practical impossibility to process large amounts of fibers in histological routine. First, the image is thresholded to provide a coarse classification between myelin (black) and non-myelin (white) pixels. The resulting binary image i

Validation of image registration algorithms is a difficult task and open-ended problem, usually application-dependent. In this paper we focus on deep brain stimulation (DBS) targeting for the treatment of movement disorders like Parkinson's disease and ess ...

A method for the automatic segmentation, recognition and measurement of neuronal myelinated fibers in nerve histological sections is presented. In this method, the fiber parameters i.e. perimeter, area, position of the fiber and myelin sheath thickness are automatically computed. Obliquity of the sections may be taken into account. First, the image is thresholded to provide a coarse classification between myelin and non-myelin pixels. Next, the resulting binary image is further simplified using connected mo

Brain deformations induced by space-occupying lesions may result in unpredictable position and shape of functionally important brain structures. The aim of this study is to propose a method for segmentation of brain structures by deformation of a segmented ...

We present a 3D geometric flow designed to evolve in Diffusion Tensor Magnetic Resonance Images(DT-MRI) along fiber tracts by measuring the diffusive similarity between voxels. Therefore we define a front propagation speed that is proportional to the similarity between the tensors lying on the surface and its neighbor in the propagation direction. The method is based on the assumption that successive voxels in a tract have similar diffusion properties. The front propagation is implemented using level set me

The k-distance transformation (k-DT) computes the k nearest patterns from each location on a discrete regular grid within a D dimensional volume, which Warfield [Patt. Rec. Letters, 17(1996) 713-721] proposed to implement using 2^D raster scans. We investi ...

This paper presents a reliable, fast and efficient method for measuring the volume density of pancreatic endocrine volume density. The algorithm segments digitized images in three different classes: the endocrine (En), exocrine (Ex) and artifact (At) components. A statistical classifier baased on the k-Nearest Neighbour (k-NN) decision rule in the RGB color space was compared with a standard point counting technique. The k-NN rule classifies other pixels in the class that is mostly respresented among the k

This paper presents a validation study on statistical non-supervised brain tissue classification techniques in MR images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of cla ...

Objects. In functional stereotactic neurosurgery, one of the cornerstones upon which the success and the operating time depends is an accurate targeting. The subthalamic nucleus (STN) is the usual target involved when applying Deep Brain Stimulation (DBS) ...

A sound that we hear in a natural setting allows us to identify the sound source and localize it in space. The two aspects can be disrupted independently as shown in a study of 15 patients with focal right-hemispheric lesions. Four patients were normal in sound recognition but severely impaired in sound localization, whereas three other patients had difficulties in recognizing sounds but localized them well. The lesions involved the inferior parietal and frontal cortices, and the superior temporal gyrus in

We propose a new signed or unsigned Euclidean distance transformation algorithm, based on the local corrections of the well-known 4SED algorithm of Danielsson. Those corrections are only applied to a small neighborhood of a small subset of pixels from the image, which keeps the cost of the operation low. In contrast with all fast algorithms previously published, our algorithm produces perfect Euclidean distance maps in a time linearly proportional to the number of pixels in the image. The computational cost

We propose a new method to compute the morphological dilation of a binary image with a circular structuring element of any given size, on a discrete lattice. The algorithm is equivalent to applying a threshold on an exact Euclidean distance map, but computations are restricted to a minimum number of pixels. The complexity of this dilation algorithm is compared to the complexity of the commonly used approximation of circular structuring elements and found to have a similar cost, while providing better result

This document presents a brief description of the Medical Image Processing (MIP) library developed by the Brain Group of the Signal Processing Institute (ITS) at Swiss Federal Institute of Technology (EPFL). This library is a result of more than three years of research by Meritxell Bach Cuadra, Torsten Butz, Eduardo Solanas and Olivier Cuisenare, carried out under the supervision of Jean-Philippe Thiran. You can find more detailed information about the library at our web page located at http://ltswww.epfl.c

We propose a new exact Euclidean distance transformation (DT) by propagation, using bucket sorting. A fast but approximate DT is first computed using a coarse neighborhood. A sequence of larger neighborhoods is then used to gradually improve this approximation. Computations are kept short by restricting the use of these large neighborhoods to the tile borders in the Voronoi diagram of the image. We assess the computational cost of this new algorithm and show that it is both smaller and less image-dependent

In this paper, we present a 3D geometric flow designed to segment the main core of fiber tracts in diffusion tensor magnetic resonance images. The fundamental assumption of our fiber segmentation technique is that adjacent voxels in a tract have similar pr ...

In the last five years, Deep Brain Stimulation (DBS) has become the most popular and effective surgical technique for the treatment of Parkinson s disease (PD). The Subthalamic Nucleus (STN) is the usual target involved when applying DBS. Unfortunately, the STN is in general not visible in common medical imaging modalities. Therefore, atlas-based segmentation is commonly considered to locate it in the images. In this paper, we propose a scheme that allows both, to perform a comparison between different regi

We propose a method for the registration of MRI brain images with the physical space. Points on the patient s scalp are acquired using a magnetic-field digitizer. The registration transform is the rigid transform that minimizes the mean square distance between those points and the scalp surface segmented from the MRI. The distance to the scalp surface is effciently computed using a 3d Euclidean distance transformation. We apply this method to the visualization of transcranial magnetic stimulation and show t

We propose a method for brain atlas deformation in presence of large space-occupying tumors or lesions, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method

The signed k-distance transformation (k-DT) computes the k nearest prototypes from each location on a discrete regular grid within a given D dimensional volume. We propose a new k-DT algorithm that divides the problem into D 1-dimensional problems and compare its accuracy and computational complexity to the existing raster-scanning and propagation approaches.

Olivier Cuisenaire

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Validation of Tissue Modelization and Classification Techniques in T1-weighted MR Brain Images

Automatic segmentation and measurement of axones in microscopic images

A Cross Validation Study of Deep Brain Stimulation Targeting: From Experts to Atlas-Based, Segmentation-Based and Automatic Registration Algorithms

Automatic Morphometry of Nerve Histological Sections

Segmentation of Brain Structures in Presence of a Space-Occupying Lesion

White Matter Mapping in DT-MRI Using Geometric Flows

Efficient algorithms for the k-distance transformation

A semi-automatic approach to measurement of pancreatic endocrine volume tissue density

Comparison and Validation of Tissue Modelization and Statistical Classification Methods in T1-weighted MR Brain Images

Validation of Experts Versus Atlas-Based and Automatic Registration Methods for Subthalamic Nucleus Targeting on MRI

What and where in human audition: selective deficits following focal hemispheric lesions

Fast and exact signed Euclidean distance transformation with linear complexity

Fast Euclidean morphological operators using local distance transformation by propagation

MIP: The ITS-EPFL Medical Image Processing Library

Fast Euclidean distance transformations by propagation using multiple neighbourhoods

White matter fiber tract segmentation in DT-MRI using geometric flows

Cross Validation of Experts Versus Registration Methods for Target Localization in Deep Brain Stimulation

Registration and visualization of transcranial magnetic stimulation on magnetic resonance images

Atlas-based Segmentation of Pathological MR Brain Images using a Model of Lesion Growth

The Euclidean k-Distance Transformartion in Arbitrary Dimensions - a Separable Implementation