Colorization and Automated Segmentation of Human T2 MR Brain Images for Characterization of Soft Tissues

Characterization of tissues like brain by using magnetic resonance (MR) images and colorization of the gray scale image has been reported in the literature, along with the advantages and drawbacks. Here, we present two independent methods; (i) a novel colorization method to underscore the variability in brain MR images, indicative of the underlying physical density of bio tissue, (ii) a segmentation method (both hard and soft segmentation) to characterize gray brain MR images. The segmented images are then transformed into color using the above-mentioned colorization method, yielding promising results for manual tracing. Our color transformation incorporates the voxel classification by matching the luminance of voxels of the source MR image and provided color image by measuring the distance between them. The segmentation method is based on single-phase clustering for 2D and 3D image segmentation with a new auto centroid selection method, which divides the image into three distinct regions (gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) using prior anatomical knowledge). Results have been successfully validated on human T2-weighted (T2) brain MR images. The proposed method can be potentially applied to gray-scale images from other imaging modalities, in bringing out additional diagnostic tissue information contained in the colorized image processing approach as described

Fast and Sequence-Adaptive Whole-Brain Segmentation Using Parametric Bayesian Modeling

AbstractQuantitative analysis of magnetic resonance imaging (MRI) scans of the brain requires accurate automated segmentation of anatomical structures. A desirable feature for such segmentation methods is to be robust against changes in acquisition platform and imaging protocol. In this paper we validate the performance of a segmentation algorithm designed to meet these requirements, building upon generative parametric models previously used in tissue classification. The method is tested on four different datasets acquired with different scanners, field strengths and pulse sequences, demonstrating comparable accuracy to state-of-the-art methods on T1-weighted scans while being one to two orders of magnitude faster. The proposed algorithm is also shown to be robust against small training datasets, and readily handles images with different MRI contrast as well as multi-contrast data

Methodological aspects for improving clinical value of SPECT and MRI

Image processing methods were developed for SPECT and MR images. The methods were validated in clinical environment. Segmentation of SPECT images for region of interest (ROI) analysis was found to be unreliable without accurate attenuation and scatter correction for the original images. The reliability of ROI analysis of brain SPECT images was enhanced using registration with MRI. The method was based on external markers. The registration error was studied using phantom tests and simulations. It was concluded that the registration accuracy was not the limiting factor in ROI analysis of the registered images provided that the external marker system was properly designed and attached. Quality requirements for MRI data from patients with cerebral infarctions were evaluated in order to make segmentation as automatic as possible. Quantitative information from these images could be extracted with e.g. statistical and neural network classifiers, but required more manual work than expected due to the visible intensity nonuniformity in the images. The third application consisted of developing a registration methodology for ictal and interictal SPECT, MRI and EEG for improved localization of the epileptogenic foci. The methodology was based on SPECT transmission imaging. The accuracy of registration was about 3-5 mm. As a conclusion, improved analysis of SPECT and MR images was obtained with the carefully evaluated methodology presented in the thesis. The registration procedure for brain SPECT and MRI as well as the registration procedure for epilepsy surgery candidates are in clinical use for selected patients in Helsinki University Central Hospital (currently Health Care Region of Helsinki and Uusimaa).reviewe

Kohdennusohjelman optimointi pään magneetti- ja tietokonetomografiakuville

In this thesis work, the aim was to find a robust, optimal rigid registration process to accurately and automatically align computed tomography (CT) and magnetic resonance (MR) images of the brain. For patients undergoing, for example, stereoelectroencephalography (epilepsy patients) or implantation of stimulating electrodes in the brain (Parkinson’s patients), it is crucial to be able to combine information from low-dose CT and MR with great precision. Registration was performed with SimpleITK interface to the image registration framework of the United States National Library of Medicine Insight Segmentation and Registration Toolkit (ITK). In the optimization process an existing SimpleITK example was used as a basis for the registration algorithm, which was then optimized one block at a time beginning with the initial alignment. Registration accuracy was determined by comparing the automatic transform of our registration algorithm to the transform of a semiautomatic registration performed with a semiautomatic ITK based software, ipcWorkstation, which is used and developed in HUS Medical Imaging Center. As a result, a robust rigid registration algorithm was developed. The maximum registration errors with the final algorithm were less than 2 mm for 7 out of 15 and less than 4 mm for 12 out of 15 patients. The algorithm performs registration up to initial rotations of 45 degrees. The fast development of automated registration algorithm presented in this thesis appears promising to be used for other applications as well. This kind of block-wise optimization pattern could be used to optimize the registration either for images of other parts of the body or for other imaging modalities such as positron emission tomography (PET) and MR.Tämän diplomityön tarkoituksena oli löytää optimaalinen ja automaattinen tietokonetomografia- ja magneettikuvien kohdennusmenetelmä. Kohdennus suoritettiin käyttäen hyväksi SimpleITK-ohjelmakirjastoa, joka perustuu ITK kuvakohdennus ohjelmakirjastoon (engl. the United States National Library of Medicine Insight Segmentation and Registration Toolkit). Optimointi aloitettiin SimpleITKesimerkin pohjalta, jonka parametreja optimoitiin osa kerrallaan lähtien liikkeelle kohdennuksen alustuksesta. Kohdennustarkkuus määritettiin vertaamalla optimoidulla kohdennusohjelmalla saatua automaattista muunnosmatriisia puoliautomaattisella menetelmällä saatuun muunnosmatriisiin. Puoliautomaattinen muunnos tehtiin HUS-Kuvantamisessa kehitetyllä ipcWorkstation-ohjelmalla, joka myös perustuu ITK-ohjelmakirjastoon. Työn tuloksena saatiin luotettavasti toimiva jäykän kuvakohdennuksen suorittava algoritmi, joka pohjautuu SimpleITK:n Python-kirjastoon. Seitsemällä 15 potilaasta suurin kohdennusvirhe oli alle 2 mm ja 12:lla 15 potilaasta alle 4 mm. Kohdennus onnistuu jopa 45 asteen lähtökohtaisilla kulmaeroilla. Työssä käytettyä nopeaa algoritmikehitystekniikkaa voitaisiin käyttää optimointiin muillekin sovelluksille. Tulevaisuudessa algoritmioptimointia osa kerrallaan voisi hyödyntää kohdennusparametrien optimointiin jonkin muun vartalon alueen rakenteellisten kuvien kohdennukseen tai eri kuvamodaliteettien kohdennukseen kuten positroniemissiotomografia- ja magneettikuvien kohdennukseen

Intelligent support system for CVA diagnosis by cerebral computerized tomography

The Cerebral Vascular Accident (CVA) is one of the major causes of death in USA and developed countries, immediately following cardiac diseases and tumors. The increasing number of CVA’s and the requirement of short time diagnosis to minimize morbidity and mortality encourages the development of computer aided diagnosis systems. Early stages of CVA are often undetected by human eye observation of Computer Tomographic (CT) images, thus incorporation of intelligent based techniques on such systems is expected to highly improve their performance. This thesis presents a Radial Basis Functions Neural Network (RBFNN) based diagnosis system for automatic identification of CVA through analysis of CT images. The research hereby reported included construction of a database composed of annotated CT images, supported by a web-based tool for Neuroradiologist registration of his/her normal or abnormal interpretation of each CT image; in case of an abnormal identification the medical doctor was indicted by the software application to designate the lesion type and to identify the abnormal region on each CT’s slice image. Once provided the annotated database each CT image processing considered a pre-processing stage for artefact removal and tilted images’ realignment followed by a feature extraction stage. A large number of features was considered, comprising first and second order pixel intensity statistics as well as symmetry/asymmetry information with respect to the ideal mid-sagittal line of each image. The policy conducted during the intelligent-driven image processing system development included the design of a neural network classifier. The architecture was determined by a Multi Objective Genetic Algorithm (MOGA) where the classifier structure, parameters and image features (input features) were chosen based on the use of different (often conflicting) objectives, ensuring maximization of the classification precision and a good generalization of its performance for unseen data Several scenarios of choosing proper MOGA’s data sets were conducted. The best result was obtained from the scenario where all boundary data points of an enlarged dataset were included in the MOGA training set. Confronted with the NeuroRadiologist annotations, specificity values of 98.01% and sensitivity values of 98.22% were obtained by the computer aided system, at pixel level. These values were achieved when an ensemble of non-dominated models generated by MOGA in the best scenario, was applied to a set of 150 CT slices (1,867,602 pixels). Present results show that the MOGA designed RBFNN classifier achieved better classification results than Support Vector Machines (SVM), despite the huge difference in complexity of the two classifiers. The proposed approach compares also favorably with other similar published solutions, both at lesion level specificity and at the degree of coincidence of marked lesions

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

Automated segmentation and characterisation of white matter hyperintensities

Neuroimaging has enabled the observation of damage to the white matter that occurs frequently in elderly population and is depicted as hyperintensities in specific magnetic resonance images. Since the pathophysiology underlying the existence of these signal abnormalities and the association with clinical risk factors and outcome is still investigated, a robust and accurate quantification and characterisation of these observations is necessary. In this thesis, I developed a data-driven split and merge model selection framework that results in the joint modelling of normal appearing and outlier observations in a hierarchical Gaussian mixture model. The resulting model can then be used to segment white matter hyperintensities (WMH) in a post-processing step. The validity of the method in terms of robustness to data quality, acquisition protocol and preprocessing and its comparison to the state of the art is evaluated in both simulated and clinical settings. To further characterise the lesions, a subject-specific coordinate frame that divides the WM region according to the relative distance between the ventricular surface and the cortical sheet and to the lobar location is introduced. This coordinate frame is used for the comparison of lesion distributions in a population of twin pairs and for the prediction and standardisation of visual rating scales. Lastly the cross-sectional method is extended into a longitudinal framework, in which a Gaussian Mixture model built on an average image is used to constrain the representation of the individual time points. The method is validated through a purpose-build longitudinal lesion simulator and applied to the investigation of the relationship between APOE genetic status and lesion load progression

Image databases in medical applications

The number of medical images acquired yearly in hospitals increases all the time. These imaging data contain lots of information on the characteristics of anatomical structures and on their variations. This information can be utilized in numerous medical applications. In deformable model-based segmentation and registration methods, the information in the image databases can be used to give a priori information on the shape of the object studied and the gray-level values in the image, and on their variations. On the other hand, by studying the variations of the object of interest in different populations, the effects of, for example, aging, gender, and diseases on anatomical structures can be detected. In the work described in this Thesis, methods that utilize image databases in medical applications were studied. Methods were developed and compared for deformable model-based segmentation and registration. Model selection procedure, mean models, and combination of classifiers were studied for the construction of a good a priori model. Statistical and probabilistic shape models were generated to constrain the deformations in segmentation and registration so that only the shapes typical to the object studied were accepted. In the shape analysis of the striatum, both volume and local shape changes were studied. The effects of aging and gender, and also the asymmetries were examined. The results proved that the segmentation and registration accuracy of deformable model-based methods can be improved by utilizing the information in image databases. The databases used were relatively small. Therefore, the statistical and probabilistic methods were not able to model all the population-specific variation. On the other hand, the simpler methods, the model selection procedure, mean models, and combination of classifiers, gave good results also with the small image databases. Two main applications were the reconstruction of 3-D geometry from incomplete data and the segmentation of heart ventricles and atria from short- and long-axis magnetic resonance images. In both applications, the methods studied provided promising results. The shape analysis of the striatum showed that the volume of the striatum decreases in aging. Also, the shape of the striatum changes locally. Asymmetries in the shape were found, too, but any gender-related local shape differences were not found.reviewe

Exploring variability in medical imaging

Although recent successes of deep learning and novel machine learning techniques improved the perfor- mance of classification and (anomaly) detection in computer vision problems, the application of these methods in medical imaging pipeline remains a very challenging task. One of the main reasons for this is the amount of variability that is encountered and encapsulated in human anatomy and subsequently reflected in medical images. This fundamental factor impacts most stages in modern medical imaging processing pipelines. Variability of human anatomy makes it virtually impossible to build large datasets for each disease with labels and annotation for fully supervised machine learning. An efficient way to cope with this is to try and learn only from normal samples. Such data is much easier to collect. A case study of such an automatic anomaly detection system based on normative learning is presented in this work. We present a framework for detecting fetal cardiac anomalies during ultrasound screening using generative models, which are trained only utilising normal/healthy subjects. However, despite the significant improvement in automatic abnormality detection systems, clinical routine continues to rely exclusively on the contribution of overburdened medical experts to diagnosis and localise abnormalities. Integrating human expert knowledge into the medical imaging processing pipeline entails uncertainty which is mainly correlated with inter-observer variability. From the per- spective of building an automated medical imaging system, it is still an open issue, to what extent this kind of variability and the resulting uncertainty are introduced during the training of a model and how it affects the final performance of the task. Consequently, it is very important to explore the effect of inter-observer variability both, on the reliable estimation of model’s uncertainty, as well as on the model’s performance in a specific machine learning task. A thorough investigation of this issue is presented in this work by leveraging automated estimates for machine learning model uncertainty, inter-observer variability and segmentation task performance in lung CT scan images. Finally, a presentation of an overview of the existing anomaly detection methods in medical imaging was attempted. This state-of-the-art survey includes both conventional pattern recognition methods and deep learning based methods. It is one of the first literature surveys attempted in the specific research area.Open Acces