614 research outputs found
Two-Dimensional Gel Electrophoresis Image Registration Using Block-Matching Techniques and Deformation Models
[Abstract] Block-matching techniques have been widely used in the task of estimating displacement in medical images, and they represent the best approach in scenes with deformable structures such as tissues, fluids, and gels. In this article, a new iterative block-matching techniqueâbased on successive deformation, search, fitting, filtering, and interpolation stagesâis proposed to measure elastic displacements in two-dimensional polyacrylamide gel electrophoresis (2DâPAGE) images. The proposed technique uses different deformation models in the task of correlating proteins in real 2D electrophoresis gel images, obtaining an accuracy of 96.6% and improving the results obtained with other techniques. This technique represents a general solution, being easy to adapt to different 2D deformable cases and providing an experimental reference for block-matching algorithms.Galicia. ConsellerĂa de EconomĂa e Industria; 10MDS014CTGalicia. ConsellerĂa de EconomĂa e Industria; 10SIN105004PRInstituto de Salud Carlos III; PI13/0028
Automated Segmentation of Cerebral Aneurysm Using a Novel Statistical Multiresolution Approach
Cerebral Aneurysm (CA) is a vascular disease that threatens the lives of
many adults. It a ects almost 1:5 - 5% of the general population. Sub-
Arachnoid Hemorrhage (SAH), resulted by a ruptured CA, has high rates of
morbidity and mortality. Therefore, radiologists aim to detect it and diagnose
it at an early stage, by analyzing the medical images, to prevent or reduce its
damages.
The analysis process is traditionally done manually. However, with the
emerging of the technology, Computer-Aided Diagnosis (CAD) algorithms are
adopted in the clinics to overcome the traditional process disadvantages, as
the dependency of the radiologist's experience, the inter and intra observation
variability, the increase in the probability of error which increases consequently
with the growing number of medical images to be analyzed, and the artifacts
added by the medical images' acquisition methods (i.e., MRA, CTA, PET, RA,
etc.) which impedes the radiologist' s work.
Due to the aforementioned reasons, many research works propose di erent
segmentation approaches to automate the analysis process of detecting a CA
using complementary segmentation techniques; but due to the challenging task
of developing a robust reproducible reliable algorithm to detect CA regardless
of its shape, size, and location from a variety of the acquisition methods, a
diversity of proposed and developed approaches exist which still su er from
some limitations.
This thesis aims to contribute in this research area by adopting two promising
techniques based on the multiresolution and statistical approaches in the
Two-Dimensional (2D) domain. The rst technique is the Contourlet Transform
(CT), which empowers the segmentation by extracting features not apparent
in the normal image scale. While the second technique is the Hidden
Markov Random Field model with Expectation Maximization (HMRF-EM),
which segments the image based on the relationship of the neighboring pixels
in the contourlet domain.
The developed algorithm reveals promising results on the four tested Three-
Dimensional Rotational Angiography (3D RA) datasets, where an objective
and a subjective evaluation are carried out. For the objective evaluation, six
performance metrics are adopted which are: accuracy, Dice Similarity Index
(DSI), False Positive Ratio (FPR), False Negative Ratio (FNR), speci city,
and sensitivity. As for the subjective evaluation, one expert and four observers
with some medical background are involved to assess the segmentation visually.
Both evaluations compare the segmented volumes against the ground
truth data
BRAIN MR IMAGE SEGMENTATION BY MODIFIED ACTIVE CONTOURS AND CONTOURLET TRANSFORM
Multiresolution analysis is often used for image representation and processing because it can represent image at the split resolution and scale space. In this paper, a novel medical image segmentation algorithm is proposed that combines contourlet transform and modified active contour model. This method has a new energy formulation by representing the image with the coefficients of a contourlet transform. This results fast and accurate convergence of the contour towards the object boundary. Medical image segmentation using contourlet transforms has shown significant improvement towards the weak and blurred edges of the Magnetic Resonance Image (MRI). Also, the computational complexity is less compared to using traditional level sets and variational level sets for medical image segmentation. The special property of the contourlet transform is that, the directional information is preserved in each sub-band and is captured by computing its energy. This energy is capable of enhancing weak and complex boundaries in details. Performance of medical image segmentation algorithm using contourlet transform is compared with other deformable models in terms of various performance measures
Brain Tumor Detection and Segmentation in Multisequence MRI
Tato prĂĄce se zabĂœvĂĄ detekcĂ a segmentacĂ mozkovĂ©ho nĂĄdoru v multisekvenÄnĂch MR obrazech se zamÄĆenĂm na gliomy vysokĂ©ho a nĂzkĂ©ho stupnÄ malignity. Jsou zde pro tento ĂșÄel navrĆŸeny tĆi metody. PrvnĂ metoda se zabĂœvĂĄ detekcĂ prezence ÄĂĄstĂ mozkovĂ©ho nĂĄdoru v axiĂĄlnĂch a koronĂĄrnĂch Ćezech. JednĂĄ se o algoritmus zaloĆŸenĂœ na analĂœze symetrie pĆi rĆŻznĂœch rozliĆĄenĂch obrazu, kterĂœ byl otestovĂĄn na T1, T2, T1C a FLAIR obrazech. DruhĂĄ metoda se zabĂœvĂĄ extrakcĂ oblasti celĂ©ho mozkovĂ©ho nĂĄdoru, zahrnujĂcĂ oblast jĂĄdra tumoru a edĂ©mu, ve FLAIR a T2 obrazech. Metoda je schopna extrahovat mozkovĂœ nĂĄdor z 2D i 3D obrazĆŻ. Je zde opÄt vyuĆŸita analĂœza symetrie, kterĂĄ je nĂĄsledovĂĄna automatickĂœm stanovenĂm intenzitnĂho prahu z nejvĂce asymetrickĂœch ÄĂĄstĂ. TĆetĂ metoda je zaloĆŸena na predikci lokĂĄlnĂ struktury a je schopna segmentovat celou oblast nĂĄdoru, jeho jĂĄdro i jeho aktivnĂ ÄĂĄst. Metoda vyuĆŸĂvĂĄ faktu, ĆŸe vÄtĆĄina lĂ©kaĆskĂœch obrazĆŻ vykazuje vysokou podobnost intenzit sousednĂch pixelĆŻ a silnou korelaci mezi intenzitami v rĆŻznĂœch obrazovĂœch modalitĂĄch. JednĂm ze zpĆŻsobĆŻ, jak s touto korelacĂ pracovat a pouĆŸĂvat ji, je vyuĆŸitĂ lokĂĄlnĂch obrazovĂœch polĂ. PodobnĂĄ korelace existuje takĂ© mezi sousednĂmi pixely v anotaci obrazu. Tento pĆĂznak byl vyuĆŸit v predikci lokĂĄlnĂ struktury pĆi lokĂĄlnĂ anotaci polĂ. Jako klasifikaÄnĂ algoritmus je v tĂ©to metodÄ pouĆŸita konvoluÄnĂ neuronovĂĄ sĂĆ„ vzhledem k jejĂ znĂĄme schopnosti zachĂĄzet s korelacĂ mezi pĆĂznaky. VĆĄechny tĆi metody byly otestovĂĄny na veĆejnĂ© databĂĄzi 254 multisekvenÄnĂch MR obrazech a byla dosĂĄhnuta pĆesnost srovnatelnĂĄ s nejmodernÄjĆĄĂmi metodami v mnohem kratĆĄĂm vĂœpoÄetnĂm Äase (v ĆĂĄdu sekund pĆi pouĆŸitĂœ CPU), coĆŸ poskytuje moĆŸnost manuĂĄlnĂch Ășprav pĆi interaktivnĂ segmetaci.This work deals with the brain tumor detection and segmentation in multisequence MR images with particular focus on high- and low-grade gliomas. Three methods are propose for this purpose. The first method deals with the presence detection of brain tumor structures in axial and coronal slices. This method is based on multi-resolution symmetry analysis and it was tested for T1, T2, T1C and FLAIR images. The second method deals with extraction of the whole brain tumor region, including tumor core and edema, in FLAIR and T2 images and is suitable to extract the whole brain tumor region from both 2D and 3D. It also uses the symmetry analysis approach which is followed by automatic determination of the intensity threshold from the most asymmetric parts. The third method is based on local structure prediction and it is able to segment the whole tumor region as well as tumor core and active tumor. This method takes the advantage of a fact that most medical images feature a high similarity in intensities of nearby pixels and a strong correlation of intensity profiles across different image modalities. One way of dealing with -- and even exploiting -- this correlation is the use of local image patches. In the same way, there is a high correlation between nearby labels in image annotation, a feature that has been used in the ``local structure prediction'' of local label patches. Convolutional neural network is chosen as a learning algorithm, as it is known to be suited for dealing with correlation between features. All three methods were evaluated on a public data set of 254 multisequence MR volumes being able to reach comparable results to state-of-the-art methods in much shorter computing time (order of seconds running on CPU) providing means, for example, to do online updates when aiming at an interactive segmentation.
Multiplicative Noise Removal Using L1 Fidelity on Frame Coefficients
We address the denoising of images contaminated with multiplicative noise,
e.g. speckle noise. Classical ways to solve such problems are filtering,
statistical (Bayesian) methods, variational methods, and methods that convert
the multiplicative noise into additive noise (using a logarithmic function),
shrinkage of the coefficients of the log-image data in a wavelet basis or in a
frame, and transform back the result using an exponential function. We propose
a method composed of several stages: we use the log-image data and apply a
reasonable under-optimal hard-thresholding on its curvelet transform; then we
apply a variational method where we minimize a specialized criterion composed
of an data-fitting to the thresholded coefficients and a Total
Variation regularization (TV) term in the image domain; the restored image is
an exponential of the obtained minimizer, weighted in a way that the mean of
the original image is preserved. Our restored images combine the advantages of
shrinkage and variational methods and avoid their main drawbacks. For the
minimization stage, we propose a properly adapted fast minimization scheme
based on Douglas-Rachford splitting. The existence of a minimizer of our
specialized criterion being proven, we demonstrate the convergence of the
minimization scheme. The obtained numerical results outperform the main
alternative methods
Algorithmic Analysis Techniques for Molecular Imaging
This study addresses image processing techniques for two medical imaging
modalities: Positron Emission Tomography (PET) and Magnetic Resonance
Imaging (MRI), which can be used in studies of human body functions and
anatomy in a non-invasive manner.
In PET, the so-called Partial Volume EïŹect (PVE) is caused by low
spatial resolution of the modality. The eïŹciency of a set of PVE-correction
methods is evaluated in the present study. These methods use information
about tissue borders which have been acquired with the MRI technique. As
another technique, a novel method is proposed for MRI brain image segmen-
tation. A standard way of brain MRI is to use spatial prior information
in image segmentation. While this works for adults and healthy neonates,
the large variations in premature infants preclude its direct application.
The proposed technique can be applied to both healthy and non-healthy
premature infant brain MR images. DiïŹusion Weighted Imaging (DWI) is
a MRI-based technique that can be used to create images for measuring
physiological properties of cells on the structural level. We optimise the
scanning parameters of DWI so that the required acquisition time can be
reduced while still maintaining good image quality.
In the present work, PVE correction methods, and physiological DWI
models are evaluated in terms of repeatabilityof the results. This gives in-
formation on the reliability of the measures given by the methods. The
evaluations are done using physical phantom objects, correlation measure-
ments against expert segmentations, computer simulations with realistic
noise modelling, and with repeated measurements conducted on real pa-
tients. In PET, the applicability and selection of a suitable partial volume
correction method was found to depend on the target application. For MRI,
the data-driven segmentation oïŹers an alternative when using spatial prior is
not feasible. For DWI, the distribution of b-values turns out to be a central
factor aïŹecting the time-quality ratio of the DWI acquisition. An optimal
b-value distribution was determined. This helps to shorten the imaging time
without hampering the diagnostic accuracy.Siirretty Doriast
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3D multiresolution statistical approaches for accelerated medical image and volume segmentation
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Medical volume segmentation got the attraction of many researchers; therefore, many techniques have been implemented in terms of medical imaging including segmentations and other imaging processes. This research focuses on an implementation of segmentation system which uses several techniques together or on their own to segment medical volumes, the system takes a stack of 2D slices or a full 3D volumes acquired from medical scanners as a data input.
Two main approaches have been implemented in this research for segmenting medical volume which are multi-resolution analysis and statistical modeling. Multi-resolution analysis has been mainly employed in this research for extracting the features. Higher dimensions of discontinuity (line or curve singularity) have been extracted in medical images using a modified multi-resolution analysis transforms such as ridgelet and curvelet transforms.
The second implemented approach in this thesis is the use of statistical modeling in medical image segmentation; Hidden Markov models have been enhanced here to segment medical slices automatically, accurately, reliably and with lossless results. But the problem with using Markov models here is the computational time which is too long. This has been addressed by using feature reduction techniques which has also been implemented in this thesis. Some feature reduction and dimensionality reduction techniques have been used to accelerate the slowest block in the proposed system. This includes Principle Components Analysis, Gaussian Pyramids and other methods. The feature reduction techniques have been employed efficiently with the 3D volume segmentation techniques such as 3D wavelet and 3D Hidden Markov models.
The system has been tested and validated using several procedures starting at a comparison with the predefined results, crossing the specialistsâ validations, and ending by validating the system using a survey filled by the end users explaining the techniques and the results. This concludes that Markovian models segmentation results has overcome all other techniques in most patientsâ cases. Curvelet transform has been also proved promising segmentation results; the end users rate it better than Markovian models due to the long time required with Hidden Markov models
Feature-Based Image Registration
Image registration is the fundamental task used to match two or more partially overlapping images taken, for example, at different times, from different sensors, or from different viewpoints and stitch these images into one panoramic image comprising the whole scene. It is a fundamental image processing technique and is very useful in integrating information from different sensors, finding changes in images taken at different times, inferring three-dimensional information from stereo images, and recognizing model-based objects. Some techniques are proposed to find a geometrical transformation that relates the points of an image to their corresponding points of another image. To register two images, the coordinate transformation between a pair of images must be found. In this thesis, a feature-based method is developed to efficiently estimate an eight-parametric projective transformation model between pairs of images.
The proposed approach applies wavelet transform to extract a number of feature points as the basis for registration. Each selected feature point is an edge point whose edge response is the maximum within a neighborhood. During the real matching process, we check each candidate pair in advance to see if it can possibly become a correct matching pair. Due to this checking, many unnecessary calculations involving cross-correlations can be screened in advance. Therefore, the search time for obtaining correct matching pairs is reduced significantly. Finally, based on the set of correctly matched feature point pairs, the transformation between two partially overlapping images can be decided
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