Illumination Correction on Biomedical Images

RF-Inhomogeneity Correction (aka bias) artifact is an important research field in Magnetic Resonance Imaging (MRI). Bias corrupts MR images altering their illumination even though they are acquired with the most recent scanners. Homomorphic Unsharp Masking (HUM) is a filtering technique aimed at correcting illumination inhomogeneity, but it produces a halo around the edges as a side effect. In this paper a novel correction scheme based on HUM is proposed to correct the artifact mentioned above without introducing the halo. A wide experimentation has been performed on MR images. The method has been tuned and evaluated using the simulated Brainweb image database. In this framework, the approach has been compared successfully against the Guillemaud filter and the SPM2 method. Moreover, the method has been successfully applied on several real MR images of the brain (0.18 T, 1.5 T and 7 T). The description of the overall technique is reported along with the experimental results that show its effectiveness in different anatomical regions and its ability to compensate both underexposed and overexposed areas. Our approach is also effective on non-radiological images, like retinal ones

Inhomogeneity Correction in High Field Magnetic Resonance Images

Projecte realitzat en col.laboració amb el centre Swiss Federal Institute of Technology (EPFL)Magnetic Resonance Imaging, MRI, is one of the most powerful and harmless ways to study human inner tissues. It gives the chance of having an accurate insight into the physiological condition of the human body, and specially, the brain. Following this aim, in the last decade MRI has moved to ever higher magnetic field strength that allow us to get advantage of a better signal-to-noise ratio. This improvement of the SNR, which increases almost linearly with the field strength, has several advantages: higher spatial resolution and/or faster imaging, greater spectral dispersion, as well as an enhanced sensitivity to magnetic susceptibility. However, at high magnetic resonance imaging, the interactions between the RF pulse and the high permittivity samples, which causes the so called Intensity Inhomogeneity or B1 inhomogeneity, can no longer be negligible. This inhomogeneity causes undesired efects that afects quantitatively image analysis and avoid the application classical intensity-based segmentation and other medical functions. In this Master thesis, a new method for Intensity Inhomogeneity correction at high ¯eld is presented. At high ¯eld is not possible to achieve the estimation and the correction directly from the corrupted data. Thus, this method attempt the correction by acquiring extra information during the image process, the RF map. The method estimates the inhomogeneity by the comparison of both acquisitions. The results are compared to other methods, the PABIC and the Low-Pass Filter which try to correct the inhomogeneity directly from the corrupted data

Enhanced Image Fusion Technique for Segmentation of Tumor using Fuzzy

This paper presents the MRI brain diagnosis support system for structure segmentation and its analysis using spatial fuzzy clustering algorithm. The method is proposed to segment normal tissues such as white Matter, Gray Matter, Cerebrospinal Fluid and abnormal tissue like tumor part from MR images automatically. These MR brain images are often corrupted with Intensity Inhomogeneity artifacts cause unwanted intensity variation due to non- uniformity in RF coils and noise due to thermal vibrations of electrons and ions and movement of objects during acquisition which may affect the performance of image processing techniques used for brain image analysis

Simultaneous MAP estimation of inhomogeneity and segmentation of brain tissues from MR images

Intrascan and interscan intensity inhomogeneities have been identified as a common source of making many advanced segmentation techniques fail to produce satisfactory results in separating brains tissues from multi-spectral magnetic resonance (MR) images. A common solution is to correct the inhomogeneity before applying the segmentation techniques. This paper presents a method that is able to achieve simultaneous semi-supervised MAP (maximum a-posterior probability) estimation of the inhomogeneity field and segmentation of brain tissues, where the inhomogeneity is parameterized. Our method can incorporate any available incomplete training data and their contribution can be controlled in a flexible manner and therefore the segmentation of the brain tissues can be optimised. Experiments on both simulated and real MR images have demonstrated that the proposed method estimated the inhomogeneity field accurately and improved the segmentation

Automatic segmentation of adipose tissue from thigh magnetic resonance images

Automatic segmentation of adipose tissue in thigh magnetic resonance imaging (MRI) scans is challenging and rarely reported in the literature. To address this problem, we propose a fully automated unsupervised segmentation method involving the use of spatial intensity constraints to guide the segmentation process. The novelty of this method lies in two aspects: firstly, an adaptive distance classifier, incorporating intra-slice spatial continuity, is used for robust region growing and segmentation estimation; secondly, polynomial based intensity inhomogeneity maps are generated to model inter- and intra-slice intensity variation of each pixel class and thus refine the initial classification. Our experimental results have demonstrated the effectiveness of imposing 3D intensity constraints to successfully classify the adipose tissue from muscles in the presence of image noise and considerable amounts of non-uniform MRI intensity. © 2013 Springer-Verlag

Sparse Representation-Based Framework for Preprocessing Brain MRI

This thesis addresses the use of sparse representations, specifically Dictionary Learning and Sparse Coding, for pre-processing brain MRI, so that the processed image retains the fine details of the original image, to improve the segmentation of brain structures, to assess whether there is any relationship between alterations in brain structures and the behavior of young offenders. Denoising an MRI while keeping fine details is a difficult task; however, the proposed method, based on sparse representations, NLM, and SVD can filter noise while prevents blurring, artifacts, and residual noise. Segmenting an MRI is a non-trivial task; because normally the limits between regions in these images may be neither clear nor well defined, due to the problems which affect MRI. However, this method, from both the label matrix of the segmented MRI and the original image, yields a new improved label matrix in which improves the limits among regions.DoctoradoDoctor en Ingeniería de Sistemas y Computació

A robust framework for medical image segmentation through adaptable class-specific representation

Medical image segmentation is an increasingly important component in virtual pathology, diagnostic imaging and computer-assisted surgery. Better hardware for image acquisition and a variety of advanced visualisation methods have paved the way for the development of computer based tools for medical image analysis and interpretation. The routine use of medical imaging scans of multiple modalities has been growing over the last decades and data sets such as the Visible Human Project have introduced a new modality in the form of colour cryo section data. These developments have given rise to an increasing need for better automatic and semiautomatic segmentation methods. The work presented in this thesis concerns the development of a new framework for robust semi-automatic segmentation of medical imaging data of multiple modalities. Following the specification of a set of conceptual and technical requirements, the framework known as ACSR (Adaptable Class-Specific Representation) is developed in the first case for 2D colour cryo section segmentation. This is achieved through the development of a novel algorithm for adaptable class-specific sampling of point neighbourhoods, known as the PGA (Path Growing Algorithm), combined with Learning Vector Quantization. The framework is extended to accommodate 3D volume segmentation of cryo section data and subsequently segmentation of single and multi-channel greyscale MRl data. For the latter the issues of inhomogeneity and noise are specifically addressed. Evaluation is based on comparison with previously published results on standard simulated and real data sets, using visual presentation, ground truth comparison and human observer experiments. ACSR provides the user with a simple and intuitive visual initialisation process followed by a fully automatic segmentation. Results on both cryo section and MRI data compare favourably to existing methods, demonstrating robustness both to common artefacts and multiple user initialisations. Further developments into specific clinical applications are discussed in the future work section

Multimodal image analysis of the human brain

Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

MR coil sensitivity inhomogeneity correction for plaque characterization in carotid arteries

We are involved in a comprehensive program to characterize atherosclerotic disease using multiple MR images having different contrast mechanisms (T1W, T2W, PDW, magnetization transfer, etc.) of human carotid and animal model arteries. We use specially designed intravascular and surface array coils that give high signal-to-noise but suffer from sensitivity inhomogeneity. With carotid surface coils, challenges include: (1) a steep bias field with an 80% change; (2) presence of nearby muscular structures lacking high frequency information to distinguish bias from anatomical features; (3) many confounding zero-valued voxels subject to fat suppression, blood flow cancellation, or air, which are not subject to coil sensitivity; and (4) substantial noise. Bias was corrected using a modification of the adaptive fuzzy c-mean method reported by Pham et al. (IEEE TMI, 18:738-752), whereby a bias field modeled as a mechanical membrane was iteratively improved until cluster means no longer changed. Because our images were noisy, we added a noise reduction filtering step between iterations and used approximate to5 classes. In a digital phantom having a bias field measured from our MR system, variations across an area comparable to a carotid artery were reduced from 50% t