The thalamus is an internal structure of the brain whose changes are related to diseases such as multiple sclerosis and Parkinson’s disease. Thus, the thalamus segmentation is an important step in studies and applications related to these disorders, for example, for shape measuring and surgical planning. The most used software and tools for brain structures segmentation employ atlas-based algorithms that usually require long processing times and sometimes lead to inaccurate results on sub-cortical structures. New methods, that minimize those problems, using deep learning for segmenting brain structures have been recently proposed. However, for some structures such as the thalamus, these methods still tend to have unsatisfactory results since they rely only on T1w images, where the contrast can be low or absent. Aiming to overcome these issues, we proposed a Convolutional Neural Network (CNN) trained with multi-modal data (structural and diffusion MRI) and the use of silver standard masks created from multiple automatic segmentations. Results on a subset of 190 subjects from the Human Connectome Project (HCP) showed an improvement in segmentation quality, confirming the effectiveness of diffusion data in differentiating tissues due to measured micro-structural properties.
KEYWORDS: Magnetic resonance imaging, Image segmentation, Tissues, Diffusion tensor imaging, Tumors, Electroluminescent displays, Data modeling, Algorithm development, Computer simulations, Signal to noise ratio
It has been shown that the combination of multi-modal MRI images improve the discrimination of diseased tissue. However the fusion of dissimilar imaging data for classification and segmentation purposes is not a trivial task, there is an inherent difference in information domains, dimensionality and scales. This work proposes a multiview consensus clustering methodology for the integration of multi-modal MR images into a unified segmentation of tumoral lesions for heterogeneity assessment. Using a variety of metrics and distance functions this multi-view imaging approach calculates multiple vectorial dissimilarity-spaces for each one of the MRI modalities and makes use of the concepts behind cluster ensembles to combine a set of base unsupervised segmentations into an unified partition of the voxel-based data. The methodology is specially designed for combining DCE-MRI and DTI-MR, for which a manifold learning step is implemented in order to account for the geometric constrains of the high dimensional diffusion information.
In this paper, we propose a fully automatic system for analyzing ecographic movies of flow-mediated dilation.
Our approach uses a spline-based active contour (deformable template) to follow artery boundaries during the
FMD procedure. A number of preprocessing steps (grayscale conversion, contrast enhancing, sharpening) are
used to improve the visual quality of frames coming from the echographic acquisition. Our system can be used
in real-time environments due to the high speed of edge recognition which iteratively minimizes fitting errors on
endothelium boundaries. We also implemented a fully functional GUI which permits to interactively follow the
whole recognition process as well as to reshape the results. The system accuracy and reproducibility has been
validated with extensive in vivo experiments.
KEYWORDS: Image segmentation, Image processing, Image compression, Medical imaging, Diagnostics, 3D image processing, 3D imaging standards, Computing systems, Magnetic resonance imaging, Image processing algorithms and systems
Medical imaging poses the great challenge of having compression algorithms that are lossless for diagnostic and
legal reasons and yet provide high compression rates for reduced storage and transmission time. The images
usually consist of a region of interest representing the part of the body under investigation surrounded by a
"background", which is often noisy and not of diagnostic interest. In this paper, we propose a ROI-based 3D
coding system integrating both the segmentation and the compression tools. The ROI is extracted by an atlas
based 3D segmentation method combining active contours with information theoretic principles, and the resulting
segmentation map is exploited for ROI based coding. The system is equipped with a GUI allowing the medical
doctors to supervise the segmentation process and eventually reshape the detected contours at any point. The
process is initiated by the user through the selection of either one pre-de.ned reference image or one image of
the volume to be used as the 2D "atlas". The object contour is successively propagated from one frame to the
next where it is used as the initial border estimation. In this way, the entire volume is segmented based on a
unique 2D atlas. The resulting 3D segmentation map is exploited for adaptive coding of the different image
regions. Two coding systems were considered: the JPEG3D standard and the 3D-SPITH. The evaluation of the
performance with respect to both segmentation and coding proved the high potential of the proposed system in
providing an integrated, low-cost and computationally effective solution for CAD and PAC systems.
In this paper, we propose a compression scheme that is tailored for stereo-laparoscope sequences. The inter-frame correlation
is modeled by the deformation field obtained by elastic registration between two subsequent frames and exploited
for prediction of the left sequence. The right sequence is lossy encoded by prediction from the corresponding left images.
Wavelet-based coding is applied to both the deformation vector fields and residual images. The resulting system supports
spatio temporal scalability, while providing lossless performance. The implementation of the wavelet transform by integer
lifting ensures a low computational complexity, thus reducing the required run-time memory allocation and on line implementation.
Extensive psychovisual tests were performed for system validation and characterization with respect to the
MPEG4 standard for video coding. Results are very encouraging: the PSVC system features the functionalities making it
suitable for PACS while providing a good trade-off between usability and performance in lossy mode.
Near regular textures feature a relatively high degree of regularity. They can be conveniently modeled by the
combination of a suitable set of textons and a placement rule. The main issues in this respect are the selection of
the minimum set of textons bringing the variability of the basic patterns; the identification and positioning of the
generating lattice; and the modelization of the variability in both the texton structure and the deviation from
periodicity of the lattice capturing the naturalness of the considered texture. In this contribution, we provide
a fully automatic solution to both the analysis and the synthesis issues leading to the generation of textures
samples that are perceptually indistinguishable from the original ones. The definition of an ad-hoc periodicity
index allows to predict the suitability of the model for a given texture. The model is validated through psychovisual
experiments providing the conditions for subjective equivalence among the original and synthetic textures,
while allowing to determine the minimum number of textons to be used to meet such a requirement for a given
texture class. This is of prime importance in model-based coding applications, as is the one we foresee, as it
allows to minimize the amount of information to be transmitted to the receiver.
Human perception of image distortions has been widely explored in recent years, however, research has not dealt
with distortions due to geometric operations. In this paper, we present the results we obtained by means of
psychovisual experiments aimed at evaluating the way the human visual system perceives geometric distortions
in images. A mathematical model of the geometric distortions is first introduced, then the impact of the model
parameters on the visibility of the distortion is measured by means of both objective metrics and subjective
tests.
In this paper we propose a new steganographic algorithm based on Matching Pursuit image decomposition. Many modern approaches to detect the presence of hidden messages are based on statistical analysis, preferably on the analysis of higher-order statistical regularities. The idea behind this work is to adaptively choose the elements of a redundant basis to represent the host image. In this way, the image is expressed as the composition of a set of structured elements resembling basic image structures such as lines, corners, and flat regions. We argue that embedding the watermark at this, more
semantic, level results in a lower modification of the low-level statistical properties of the image, and hence in a lower detectability of the presence of the hidden message.
KEYWORDS: Digital watermarking, Haptic technology, Visualization, 3D modeling, Human-machine interfaces, 3D displays, Data modeling, Virtual reality, Visual process modeling, Interfaces
Usage of 3D data and models is receiving a growing interest for several applications, like training, museum displays, multimodal interfaces, aid for impaired people. In such a framework the need will soon raise to protect 3D data from misuse. Among the technologies that can be used to this aim, digital watermarking has a prominent role, due to its versatility and non-obtrusiveness. A basic requirement of any watermarking scheme is that the embedded code is invisible, or non-perceivable, by the end user of the data. This requirement also holds for 3D objects, it is then necessary that the human ability of perceiving a signal
hidden in a 3D object is studied. In this paper we present a preliminary analysis aiming at comparing the perceptibility of the hidden signal when the 3D model is sensed through different
senses, namely vision (through common rendering techniques and subsequent display on a monitor) and touch (through a haptic interface). Specifically our investigation aimed at assessing
whether ensuring watermark invisibility is sufficient to ensure that the watermark presence can not be felt haptically. The answer stemming from our preliminary analysis seems to be a clear no, even if further studies will be necessary before a definitive answer can be given.
We propose a wavelet-based texture classification system. Texture descriptors are local energy measures within the feature images obtained by projecting the samples on Dyadic Frames of Directional Wavelets. Rotation invariant features are obtained by taking the Fourier expansion of the subsets of components of the original feature vectors concerning each considered scale separately. Three different classification schemes have been compared: the Euclidean, the weighted Euclidean and the KNN classifiers. Performances have been evaluated on a set of 13 Brodatz textures, from which both a training set and a test set have been extracted. Results are present in the form of confusion matrices. The KNN classifier provides the globally best performance, with an average recognition rate around the 96 percent for the original non-rotated test set, and 88 percent when the rotated versions are considered. Its simplicity and accuracy renders the proposed method highly suited for multimedia applications, as content-based image retrieval.
Using the lifting step approach for wavelet decomposition, Sweldens has recently introduced a fully integer based filtering method. There are several advantages to such an approach, one of the most interesting is the possibility to use wavelets for efficient lossless coding. However, this scheme is also interesting in case of lossy compression, especially for 'real-time' or 'low-cost' applications. In the PC based world, integer operations are more efficient than their floating-point counterparts, allowing much faster processing. In case of hardware implementations, integer based arithmetic units are much cheaper than those capable of handling floating points. In terms of memory usage, integer decomposition reduces the demands on the system by at least a factor two. For these reasons, we are interested in considering integer based filtering for lossy image compression as well. This raises an important question: what additional losses, if any, occur when using integer based wavelet decompositions in place of the usual floating point approach? First we compare the compressed images using standard SNR and other simple metrics. Next we evaluate our results using visually weighted objective metrics. This allows us to fully evaluate integer wavelet decomposition when applied to lossy image compression across a range of bit rates, filter characteristics and image types.
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