Segmentation of Brain Magnetic Resonance Images (MRIs): A Review

Abstract MR imaging modality has assumed an important position in studying the characteristics of soft tissues. Generally, images acquired by using this modality are found to be affected by noise, partial volume effect (PVE) and intensity nonuniformity (INU). The presence of these factors degrades the quality of the image. As a result of which, it becomes hard to precisely distinguish between different neighboring regions constituting an image. To address this problem, various methods have been proposed. To study the nature of various proposed state-of-the-art medical image segmentation methods, a review was carried out. This paper presents a brief summary of this review and attempts to analyze the strength and weaknesses of the proposed methods. The review concludes that unfortunately, none of the proposed methods has been able to independently address the problem of precise segmentation in its entirety. The paper strongly favors the use of some module for restoring pixel intensity value along with a segmentation method to produce efficient results

Combining Evolutionary Algorithms and Average Overlap Metric Rules for Medical Image Segmentation

Abstract: In this paper, we explore a new algorithm based on evolutionary algorithms and fusion concepts for improving medical image segmentation. The proposed approach starts by finding seeds that cover the image using genetic algorithm (GA). This initial partition is used as the seed to a computationally efficient region growing method to produce the closed regions. The average overlap metric (AOM) is used to classify these regions into groups based on the similarity criterion. The fusion modules are applied to each group to find the points that label the suite membership values. The different fusion rules will be applied to these groups to produce a set of chromosomes to select the best data in each chromosome to represent the final segment. To prove the efficiency of the proposed algorithm, the proposed algorithm will be applied to challenging applications: MRI datasets, 3D simulated MRIs, and gray matter/white matter of brain segmentations

Construction and validation of a database of head models for functional imaging of the neonatal brain

The neonatal brain undergoes dramatic structural and functional changes over the last trimester of gestation. The accuracy of source localisation of brain activity recorded from the scalp therefore relies on accurate age-specific head models. Although an age-appropriate population-level atlas could be used, detail is lost in the construction of such atlases, in particular with regard to the smoothing of the cortical surface, and so such a model is not representative of anatomy at an individual level. In this work, we describe the construction of a database of individual structural priors of the neonatal head using 215 individual-level datasets at ages 29-44 weeks postmenstrual age from the Developing Human Connectome Project. We have validated a method to segment the extra-cerebral tissue against manual segmentation. We have also conducted a leave-one-out analysis to quantify the expected spatial error incurred with regard to localising functional activation when using a best-matching individual from the database in place of a subject-specific model; the median error was calculated to be 8.3 mm (median absolute deviation 3.8 mm). The database can be applied for any functional neuroimaging modality which requires structural data whereby the physical parameters associated with that modality vary with tissue type and is freely available at www.ucl.ac.uk/dot-hub

A Decade of Neural Networks: Practical Applications and Prospects

The Jet Propulsion Laboratory Neural Network Workshop, sponsored by NASA and DOD, brings together sponsoring agencies, active researchers, and the user community to formulate a vision for the next decade of neural network research and application prospects. While the speed and computing power of microprocessors continue to grow at an ever-increasing pace, the demand to intelligently and adaptively deal with the complex, fuzzy, and often ill-defined world around us remains to a large extent unaddressed. Powerful, highly parallel computing paradigms such as neural networks promise to have a major impact in addressing these needs. Papers in the workshop proceedings highlight benefits of neural networks in real-world applications compared to conventional computing techniques. Topics include fault diagnosis, pattern recognition, and multiparameter optimization

Aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica

Nowadays, artificial neural networks and deep learning, are two of the most powerful machine learning tools, which aim to develop systems that learn automatically, recognize patterns, predict behaviors and generalize information from data sets. These two tools have become a potential field of research with applications to engineering, with biomedical engineering not being the exception. This article presents an updated review of the main applications of neural networks and deep learning in the areas of omics, imaging, brain-machine and body-machine interfaces, and the management and administration of public health; these areas extend from the study of processes at molecular level, to processes that involve large populations.Hoy en día, las redes neuronales artificiales y el deep learning, son dos de las herramientas más poderosas del aprendizaje de máquina, que tienen por objetivo desarrollar sistemas que aprenden automáticamente, reconocen patrones, predicen comportamientos y generalizan información a partir de conjuntos de datos.  Estas dos herramientas se han convertido en un potencial campo de investigación con aplicaciones a la ingeniería, no siendo la ingeniería biomédica la excepción. En este artículo se presenta una revisión actualizada de las principales aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica en las ramas de la ómica, la imagenología, las interfaces cerebro-máquina y hombre-máquina, y la gestión y administración de la salud pública; ramas que se extienden desde el estudio de procesos a nivel molecular, hasta procesos que involucran grandes poblaciones

Segmentação Automática de Lesões de Esclerose Múltipla em Imagens de Ressonância Magnética

A esclerose múltipla (EM) é o distúrbio neurológico mais comum diagnosticado em jovens adultos com causas inexplicáveis e grandes repercussões na vida dos pacientes, incitando os investigadores na procura ativa de respostas. Embora a doença não possa ser curada ou prevenida, neste momento, os tratamentos disponíveis permitem apenas reduzir a gravidade da mesma e retardar a sua progressão. Torna-se cada vez mais necessário recorrer a técnicas de imagiologia e de processamento e análise de imagem, para ajudar os médicos a realizar um diagnóstico precoce e iniciar o tratamento adequado a fim de proporcionar uma melhor qualidade de vida ao paciente. Várias abordagens baseadas em segmentação automática de lesões de esclerose múltipla tem sido amplamente investigadas nos últimos anos com esse objetivo.Para o desenvolvimento deste projeto, procurou-se por um lado, o reconhecimento das etapas necessárias para implementação e otimização de uma metodologia de processamento e análise de imagem para segmentação automática de lesões de EM, e por outro, a exploração de técnicas de pré-processamento, segmentação e classificação para caracterização objetiva e quantitativa das lesões. Neste trabalho serão ainda abordados conceitos fundamentais sobre a doença de esclerose múltipla e da técnica de ressonância magnética (RM), bem como o estudo bibliográfico de algumas das metodologias atualmente existentes.A metodologia desenvolvida nesta Dissertação teve como base a implementação de diversos algoritmos de pré-processamento para suavização e remoção de ruído, remoção de tecidos não-cerebrais, correção de contraste e normalização de intensidade das imagens. Para segmentação de lesões foi aplicado o estudo de redes neuronais, uma abordagem bastante promissora e atual para o problema proposto, e para classificação foram extraídas e analisadas algumas características das lesões através da sua forma e tamanho. Pretende-se que esta nova metodologia seja flexível e permita o ensaio e a análise dos resultados.Os resultados obtidos demonstram que as técnicas de pré-processamento se revelam essenciais para as etapas subsequentes permitindo uma melhor qualidade da imagem. A segmentação de lesões através do uso de redes neuronais revelou-se apropriada tal como comprovado pelas métricas analisadas, com índice de similaridade estrutural muito próximo de 1, taxa de erro absoluto médio de 3,8% e coeficiente de Dice de 0,58. Por fim, pelas várias aplicações práticas realizadas, foi possível demonstrar a utilidade e adequação das técnicas de processamento e análise de imagem no estudo e deteção de lesões de esclerose múltipla através de imagens de RM.Multiple sclerosis is the most commonly diagnosed neurological disorder in young adults with unexplained causes and major repercussions in the lives of patients, urging researchers to actively search for answers. Although the disease cannot be cured or prevented, the available treatments nowadays reduce its severity and delay its progression. It is becoming increasingly necessary to use imaging techniques and also image processing and analysis techniques, to help doctors perform an early diagnosis and start appropriate treatment in order to provide a better quality of life for the patient. Several approaches based on automatic segmentation of multiple sclerosis lesions have been extensively investigated in recent years for this purpose.This project was developed, firstly, with the recognition of the steps necessary to implement and optimize an image processing and analysis methodology for automatic segmentation of MS lesions, and secondly, by the exploration of pre-processing, segmentation and classification techniques for objective and quantitative characterization of the lesions. This work will also be discussed basic concepts of multiple sclerosis disease and magnetic resonance imaging (MRI), as well as the bibliographical study of some of the currently existing methodologies.The methodology developed in this dissertation was based on the implementation of several pre-processing algorithms for noise smoothing and removal, non-cerebral tissue removal, contrast correction and normalization of images intensity. For lesion segmentation was applied to the study of neural networks, a very promising and current approach to the proposed problem, and to classify were extracted and analyzed some characteristics of the lesions through shape and size. It is intended that this new methodology is flexible and allow the testing and analysis of the results.The results obtained demonstrate that pre-processing techniques are essential to the subsequent steps allowing better image quality. Segmentation of lesions through the use of neural networks proved to be appropriate for this study as shown by the metrics analyzed, with a structural similarity index very close to 1, mean absolute error rate of 3.8% and Dice coefficient of 0.58. Finally, the various practical applications performed was possible to demonstrate the usefulness and adequacy of image processing and analysis techniques in the study and detection of multiple sclerosis lesions through MR images

Registration and Deformable Model-Based Neck Muscles Segmentation and 3D Reconstruction

Whiplash is a very common ailment encountered in clinical practice that is usually a result of vehicle accidents but also domestic activities and sports injuries. It is normally caused when neck organs (specifically muscles) are impaired. Whiplash-associated disorders include acute headaches, neck pain, stiffness, arm dislocation, abnormal sensations, and auditory and optic problems, the persistence of which may be chronic or acute. Insurance companies compensate almost fifty percent of claims lodged due to whiplash injury through compulsory third party motor insurance. The morphological structures of neck muscles undergo hypertrophy or atrophy following damage caused to them by accidents. Before any medical treatment is applied , any such change needs to be known which requires 3D visualization of the neck muscles through a proper segmentation of them because the neck contains many other sensitive organs such as nerves, blood vessels, the spinal cord and trachea. The segmentation of neck muscles in medical images is a more challenging task than those of other muscles and organs due to their similar densities and compactness, low resolutions and contrast in medical images, anatomical variabilities among individuals, noise, inhomogeneity of medical images and false boundaries created by intra-muscular fat. Traditional segmentation algorithms, such as those used in thresholding and clustering-based methods, are not applicable in this project and also not suitable for medical images. Although there are some techniques available in clinical research for segmenting muscles such as thigh, tongue, leg, hip and pectoral ones, to the best of author's knowledge, there are no methods available for segmenting neck muscles due to the challenges described above. In the first part of this dissertation, an atlas-based method for segmenting MR images, which uses linear and non-linear registration frameworks, is proposed, with output from the registration process further refined by a novel parametric deformable model. The proposed method is tested on real clinical data of both healthy and non-healthy individuals. During the last few decades, registration- and deformable model-based segmentation methods have been very popular for medical image segmentation due to their incorporation of prior information. While registration-based segmentation techniques can preserve topologies of objects in an image, accuracy of atlas-based segmentation depends mainly on an effective registration process. In this study, the registration framework is designed in a novel way in which images are initially registered by a distinct 3D affine transformation and then aligned by a local elastic geometrical transformation based on discrete cosines and registered firstly slice-wise and then block-wise. The numbers of motion parameters are changed in three different steps per frame. This proposed registration framework can handle anatomical variabilities and pathologies by confining its parameters in local regions. Also, as warping of the framework relies on number of motion parameters, similarities between two images, gradients of floating image and coordinate mesh grid values, it can easily manage pathological and anatomical variabilities using a hierarchical parameter scheme. The labels transferred from atlas can be improved by deformable model-based segmentation. Although geometric deformable models have been widely used in many biomedical applications over recent years, they cannot work in the context of neck muscles segmentation due to noise, background clutter and similar objects touching each other. Another important drawback of geometric deformable models is that they are many times slower than parametric deformable ones. Therefore, the segmentation results produced by the registration process are ameliorated using a multiple-object parametric deformable model which is discussed in detail in the second part of this thesis. This algorithm uses a novel Gaussian potential energy distribution which can adapt to topological changes and does not require re-parameterization. Also, it incorporates a new overlap removal technique which ensures that there are no overlaps or gaps inside an object. Furthermore, stopping criteria of vertices are designed so that difference between boundaries of the deformable model and actual object is minimal. The multiple-object parametric deformable model is also applied in a template contours propagation-based segmentation technique, as discussed in the third part of this dissertation. This method is semi-automatic, whereby a manual delineation of middle image in a MRI data set is required. It can handle anatomical variabilities more easily than atlas-based segmentation because it can segment any individual's data irrespective of his/her age, weight and height with low computational complexity and it does not depend on other data as it operates semi-automatically. In it, initial model contour resides close to the object's boundary, with degree of closeness dependent on slice thicknesses and gaps between the slices

Brain Tumor Diagnosis Support System: A decision Fusion Framework

An important factor in providing effective and efficient therapy for brain tumors is early and accurate detection, which can increase survival rates. Current image-based tumor detection and diagnosis techniques are heavily dependent on interpretation by neuro-specialists and/or radiologists, making the evaluation process time-consuming and prone to human error and subjectivity. Besides, widespread use of MR spectroscopy requires specialized processing and assessment of the data and obvious and fast show of the results as photos or maps for routine medical interpretative of an exam. Automatic brain tumor detection and classification have the potential to offer greater efficiency and predictions that are more accurate. However, the performance accuracy of automatic detection and classification techniques tends to be dependent on the specific image modality and is well known to vary from technique to technique. For this reason, it would be prudent to examine the variations in the execution of these methods to obtain consistently high levels of achievement accuracy. Designing, implementing, and evaluating categorization software is the goal of the suggested framework for discerning various brain tumor types on magnetic resonance imaging (MRI) using textural features. This thesis introduces a brain tumor detection support system that involves the use of a variety of tumor classifiers. The system is designed as a decision fusion framework that enables these multi-classifier to analyze medical images, such as those obtained from magnetic resonance imaging (MRI). The fusion procedure is ground on the Dempster-Shafer evidence fusion theory. Numerous experimental scenarios have been implemented to validate the efficiency of the proposed framework. Compared with alternative approaches, the outcomes show that the methodology developed in this thesis demonstrates higher accuracy and higher computational efficiency

The development and application of structural priors for diffuse optical imaging in infants from newborn to two years of age

This thesis describes the development and application of age-appropriate structural priors to improve the localisation accuracy of diffuse optical tomography (DOT) approaches in infants aged from birth to two years of age. Knowledge of the target cranial anatomy, known as a structural prior, is required to produce three-dimensional images localising concentration changes to the cortex. A structural prior would ideally be subject-specific, i.e. derived from structural magnetic resonance imaging (MRI) data from each specific subject. Requiring a structural scan from every infant participant, however, is not feasible and undermines many of the benefits of DOT. A review was conducted to catalogue available infant structural MRI data, and selected data was then used to produce structural priors for infants aged 1- to 24-months. Conventional analyses using functional near-infrared spectroscopy (fNIRS) implicitly assume that head size and array position are constant across infants. Using DOT, the validity of assuming these parameters constant in a longitudinal infant cohort was investigated. The results show that this assumption is reasonable at the group-level in infants aged 5- to 12-months but becomes less valid for smaller group sizes. A DOT approach was determined to illicit more subtle effects of activation, particularly for smaller group sizes and expected responses. Using state-of-the-art MRI data from the Developing Human Connectome Project, a database of structural priors of the neonatal head was produced for infants aged pre-term to term-equivalent age. A leave-one-out approach was used to determine how best to find a match between a given infant and a model from the database, and how best to spatially register the model to minimise the anatomical and localisation errors relative to subject-specific anatomy. Model selection based on the 10/20 scalp positions was determined to be the best method (of those based on external features of the head) to minimise these errors

Segmentation of color images : applications to cellular microscopy

A morphological method for the color segmentation of cytological images is presented. This method is mainly based on watershed whose potential function blend local and global informations. The method uses a priori informations for the frame of the method. The paper is based on three parts. In a first part, the frame of a morphological segmentation method is recalled. Secondly, our morphological method of color segmentation is presented and its corresponding methodology of utilization is developped. All importants points of our morphological method are exposed : choice of the color space, choice of the color gradient, etc. Finally, the usefulness of the segmentation method is illustrated on images from serous cytology.Nous proposons une méthode morphologique de segmentation d'images couleur de cytologie. Cette méthode est basée sur la ligne de partage des eaux utilisant une fonction de potentiel couleur combinant informations locale et globale. Cette méthode de segmentation utilise des informations a priori pour élaborer l'utilisation de la méthode. L'article s'articule autour de trois parties. Dans une première partie, nous rappellerons tout d'abord la structure d'une segmentation morphologique couleur. Dans une deuxième partie, nous exposerons notre méthode morphologique de segmentation couleur ainsi que sa méthodologie d'utilisation précisant tous les points importants et leur mise au point (choix de l'espace couleur, choix du gradient, etc.). Dans une dernière partie nous verrons une illustration de la méthode de segmentation sur des images de la cytologie des séreuses