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

Desarrollo de una metodología para la determinación automática de alteraciones estructurales y funcionales en el cerebro mediante el procesamiento de imágenes de resonancia magnética

[ES] El objetivo que se persigue es determinar de manera automática las propiedades estructurales y la conectividad funcional en estado de reposo en el cerebro. Para ello, se propone una metodología basada en el estudio de la morfometría cerebral y de la conectividad funcional a partir del análisis de imágenes de Resonancia Magnética (RM). Para el estudio de la morfometría, se utilizará el método Voxel Based Morphometry (VBM), en el que se normaliza la imagen anatómica del sujeto respecto a una plantilla para analizar la presencia de alteraciones morfológicas locales. En este método, primero se realiza un pre-procesado que trate de eliminar las posibles heterogeneidades presentes en la señal de RM, así como extraer de las imágenes aquellas estructuras de tejido cerebral que puedan influir negativamente en los siguientes procesos (cráneo, vasos, cuero cabelludo, ojos, grasa y músculos). Posteriormente, se llevan a cabo diversos procesos de estandarización y tipificación buscando posicionar todas las imágenes anatómicas que queremos evaluar en un mismo espacio para permitir su análisis estadístico. Estos procesos son la normalización a este espacio común, la segmentación en los diferentes tejidos de interés (sustancia gris, sustancia blanca y líquido cefalorraquídeo) y el suavizado. Una vez realizados estos pasos, se emplea el Modelo Lineal General (MLG) para construir mapas estadísticos paramétricos (SPMs) que permitan el análisis morfológico de las imágenes. Aquellas zonas cerebrales resultantes de este análisis se etiquetarán mediante el atlas Harvard-Oxford (sustancia gris). La segunda parte de la metodología, consistente en el estudio de la conectividad funcional, está basada en la correlación entre las señales BOLD de uno o más vóxels separados anatómicamente. De forma análoga al método anterior, es necesario realizar un pre-procesado previo de las imágenes anatómicas y funcionales antes de comenzar el análisis estadístico: realineamiento, corregistro, normalización y suavizado. Tras completar los pasos anteriores, se generarán SPMs para delimitar aquellas áreas cerebrales activas y se establecen relaciones funcionales entre ellas mediante un análisis ROI to ROI. Los resultados se encuentran etiquetados mediante el atlas Harvard-Oxford. Toda la metodología mostrada se llevará a cabo mediante un software de elaboración propia escrito en lenguaje MATLAB. Este algoritmo empleará paquetes especializados de esta herramienta como SPM12 (Statistical Parametric Mapping) y CONN (functional connectivity toolbox).[EN] The objective is to automatically determine the structural properties and functional connectivity during the resting state. To this end, a methodology based on the study of brain morphometry and functional connectivity from the analysis of magnetic resonance imaging (MRI) is proposed. For the morphometry study, the Voxel Based Morphometry (VBM) method will be used, in which the anatomical image of the subject is normalized with respect to a template in order to analyze the presence of local morphological alterations. In this method, a pre-processing is first performed to eliminate the possible heterogeneities present in the MRI signal, as well as to extract from the images those structures of brain tissue that may negatively influence the following processes (skull, vessels, scalp, eyes, fat and muscles). Subsequently, several standardization and typification processes are carried out in order to position all the anatomical images that we want to evaluate in the same space to allow their statistical analysis. These processes are the normalization to this common space, the segmentation in the different tissues of interest (grey matter, white matter and cerebrospinal fluid) and the smoothing. Once these steps are completed, the General Linear Model (GLM) is used to build parametric statistical maps (SPMs) that allow the morphological analysis of the images. Those brain areas resulting from this analysis will be labelled using the Harvard-Oxford (grey matter) and Jülich / Susumu Mori (white matter) atlases. The second part of the methodology, consisting of the study of functional connectivity, is based on the correlation between the BOLD signals of one or more anatomically separated voxels. Similar to the previous method, it is necessary to pre-process the anatomical and functional images before starting the statistical analysis: realignment, co-registration, normalization and smoothing. After finishing the above steps, SPMs are generated to delimit those active brain areas and functional relationships are established between them through a ROI to ROI analysis. Results are labelled using the Harvard-Oxford atlas. All the methodology shown will be carried out by means of an own elaboration software written in MATLAB language. This algorithm will use specialized packages of this tool such as SPM12 (Statistical Parametric Mapping) and CONN (functional connectivity toolbox).Camacho Ramos, EJ. (2018). Desarrollo de una metodología para la determinación automática de alteraciones estructurales y funcionales en el cerebro mediante el procesamiento de imágenes de resonancia magnética. http://hdl.handle.net/10251/110764TFG

Noise Estimation, Noise Reduction and Intensity Inhomogeneity Correction in MRI Images of the Brain

Rician noise and intensity inhomogeneity are two common types of image degradation that manifest in the acquisition of magnetic resonance imaging (MRI) system images of the brain. Many noise reduction and intensity inhomogeneity correction algorithms are based on strong parametric assumptions. These parametric assumptions are generic and do not account for salient features that are unique to specific classes and different levels of degradation in natural images. This thesis proposes the 4-neighborhood clique system in a layer-structured Markov random field (MRF) model for noise estimation and noise reduction. When the test image is the only physical system under consideration, it is regarded as a single layer Markov random field (SLMRF) model, and as a double layer MRF model when the test images and classical priors are considered. A scientific principle states that segmentation trivializes the task of bias field correction. Another principle states that the bias field distorts the intensity but not the spatial attribute of an image. This thesis exploits these two widely acknowledged scientific principles in order to propose a new model for correction of intensity inhomogeneity. The noise estimation algorithm is invariant to the presence or absence of background features in an image and more accurate in the estimation of noise levels because it is potentially immune to the modeling errors inherent in some current state-of-the-art algorithms. The noise reduction algorithm derived from the SLMRF model does not incorporate a regularization parameter. Furthermore, it preserves edges, and its output is devoid of the blurring and ringing artifacts associated with Gaussian and wavelet based algorithms. The procedure for correction of intensity inhomogeneity does not require the computationally intensive task of estimation of the bias field map. Furthermore, there is no requirement for a digital brain atlas which will incorporate additional image processing tasks such as image registration

Correction of spatial distortion in magnetic resonance imaging

Dissertation to Obtain the Degree of Master in Biomedical EngineeringMagnetic Resonance Imaging (MRI) has been a major investigation and research focus among scientific and medical communities. So, new hardware with superior magnetic fields and faster sequences has been developed. However, these improvements result in intensity and spatial distortions, particularly in fast sequences, as Echo Plana Imaging (EPI), used in functional and diffusion-weighed MRI (fMRI and DW-MRI). Therefore, correction of spatial distortion is useful to obtain a higher quality in this kind of images. This project contains two major parts. The first part consists in simulating MRI data required for assessing the performance of Registration methods and optimizing parameters. To assess the methods five evaluation metrics were calculated between the corrected data and an undistorted EPI, namely: Root Mean Square (RMS); Normalized Mutual Information (NMI), Squared Correlation Coefficient(SCC); Euclidean Distance of Centres of Mass (CM) and Dice Coefficient of segmented images. In brief, this part validates the applied Registration correction method. The project’s second part includes correction of real images, obtained at a Clinical Partner. Real images are diffusion weighted MRI data with different b-values (gradient strength coefficient), allowing performance assessment of different methods on images with increasing b-values and decreasing SNR. The methods tested on real data were Registration, Field Map correction and a new proposed pipeline, which consists in performing a Field Map correction after a registration process. To assess the accuracy of these methods on real data, we used the same evaluation metrics, as for simulated data, except RMS and Dice Coefficient. At the end, it was concluded that Registration-based methods are better than Field Map, and that the new proposed pipeline produces some improvements in the registration. Regarding the influence of b-value on the correction, it is important to say that the methods performed using images with higher b’s showed more improvements in regarding metric values, but the behaviour is similar for all b-values

Computed tomography image analysis for the detection of obstructive lung diseases

Damage to the small airways resulting from direct lung injury or associated with many systemic disorders is not easy to identify. Non-invasive techniques such as chest radiography or conventional tests of lung function often cannot reveal the pathology. On Computed Tomography (CT) images, the signs suggesting the presence of obstructive airways disease are subtle, and inter- and intra-observer variability can be considerable. The goal of this research was to implement a system for the automated analysis of CT data of the lungs. Its function is to help clinicians establish a confident assessment of specific obstructive airways diseases and increase the precision of investigation of structure/function relationships. To help resolve the ambiguities of the CT scans, the main objectives of our system were to provide a functional description of the raster images, extract semi-quantitative measurements of the extent of obstructive airways disease and propose a clinical diagnosis aid using a priori knowledge of CT image features of the diseased lungs. The diagnostic process presented in this thesis involves the extraction and analysis of multiple findings. Several novel low-level computer vision feature extractors and image processing algorithms were developed for extracting the extent of the hypo-attenuated areas, textural characterisation of the lung parenchyma, and morphological description of the bronchi. The fusion of the results of these extractors was achieved with a probabilistic network combining a priori knowledge of lung pathology. Creating a CT lung phantom allowed for the initial validation of the proposed methods. Performance of the techniques was then assessed with clinical trials involving other diagnostic tests and expert chest radiologists. The results of the proposed system for diagnostic decision-support demonstrated the feasibility and importance of information fusion in medical image interpretation.Open acces