1,721,125 research outputs found
Functional and structural MRI image analysis for brain glial tumors treatment
Full text linkThis Ph.D Thesis is the outcome of a close collaboration between the Center for Research in Image Analysis and Medical Informatics (CRAIIM) of the Insubria University and the Operative Unit of Neurosurgery, Neuroradiology and Health Physics of the University Hospital ”Circolo Fondazione Macchi”, Varese.
The project aim is to investigate new methodologies by means of whose, develop an integrated framework able to enhance the use of Magnetic Resonance Images, in order to support clinical experts in the treatment of patients with brain Glial tumor.
Both the most common uses of MRI technology for non-invasive brain inspection were analyzed. From the Functional point of view, the goal has been to provide tools for an objective reliable and non-presumptive assessment of the brain’s areas locations, to preserve them as much as possible at surgery.
From the Structural point of view, methodologies for fully automatic brain segmentation and recognition of the tumoral areas, for evaluating the tumor volume, the spatial distribution and to be able to infer correlation with other clinical data or trace growth trend, have been studied. Each of the proposed methods has been thoroughly assessed both qualitatively and quantitatively.
All the Medical Imaging and Pattern Recognition algorithmic solutions studied for this Ph.D. Thesis have been integrated in GliCInE: Glioma Computerized Inspection Environment, which is a MATLAB prototype of an integrated analysis environment that offers, in addition to all the functionality specifically described in this Thesis, a set of tools needed to manage Functional and Structural Magnetic Resonance Volumes and ancillary data related to the acquisition and the patient
Meningioma and peritumoral edema segmentation of preoperative MRI brain scans
No full textThis work focuses the attention on the segmentation of meningioma and peritumoral edema from multispectral brain MR imagery. Precise tumour and edema delineation and volume quantification from preoperative MRI data contribute to formulate surgical indications in elderly patients harbouring intracranial meningioma. The authors propose a fully automatic procedure based on the allied use of Graph Cut and support vector machine. The overall strategy combines the advantages of the image-based and machine learning techniques adopted, optimising the balancing between accuracy and stability/reproducibility of the results. Experimental results, obtained by processing in-house collected data, prove that the method is robust and oriented to the use in clinical practice
Hemisphere Dominance Evaluation by using fMRI Activation Weighted Vector
No full textIn this work, a new quantitative method for the evaluation of the hemispheric dominance using fMRI data has
been studied and experimentally evaluated. The quantitative evaluation of the Statistical ParametricMap (SPM)
could be a valuable tool supporting the radiologist in the interpretation of the map and in the localization of the
active area related to a specific task, avoiding a presumptive attitude in the analysis. This quantitative analysis
is useful in the evaluation of the functional modifications, due to neuronal plasticity phenomena. The aim of
this analysis is to extract a synthetic but comprehensive representation, the Activation Weighted Vector (AWV)
from which to derive from the SPM indexes which describe in an objective way the distribution of the cerebral
activations. Relevant information may be extracted from the clinical point of view, such as the hemispheric
dominance
Computation and management of weighted activation vectors in support to fMRI analysis of clinical subjects
No full textIn the present work, we investigate the usefulness of a new representation of the results obtained by fMRI data analysis, named weighted activation vector (WAV), built based on statistical parametric mapping. A software package for the generation and management of WAVs is illustrated. It is designed to support single-subject, multi-temporal and collective brain tumour studies. As seen in our experimental context, the combined use of WAVs and statistical parametric maps (SPMs) improves the quality of medical decisions before and after neurosurgical practice. Clustering techniques applied to WAVs can be efficiently analysed and optimised in an attempt to discover relevant properties of collective data
Fully automatic brain tumor segmentation by using competitive EM and graph cut
Full text linkManual MRI brain tumor segmentation is a difficult and time consuming
task which makes computer support highly desirable. This paper
presents a hybrid brain tumor segmentation strategy characterized by the allied
use of Graph Cut segmentation method and Competitive Expectation Maximization
(CEM) algorithm. Experimental results were obtained by processing inhouse
collected data and public data from benchmark data sets. To see if the
proposed method can be considered an alternative to contemporary methods,
the results obtained were compared with those obtained by authors who undertook
the Multi-modal Brain Tumor Segmentation challenge. The results obtained
prove that the method is competitive with recently proposed approaches
Fuzzy reference data estimation in brain tumor segmentation studies
No full textIn the present work we propose a novel label fusion strategy specifically oriented to MRI Brain tumor segmentation studies. The salient aspect of the overall strategy is the use of the fuzzy set framework to manage uncertainty in the visual interpretation process. In particular Fuzzy Connectedness principles are used to merge individual labels and generate segmentation representative of a common agreement. Labels are provided by the experts who are asked to manually trace few representative points within the objects of interest. Starting from these multiple seeds the Fuzzy Connectedness algorithm computes the segmentation. The proposed strategy is naturally oriented to integrate uncertain information and then it is expected to manage dissimilarity among input labels. Interaction is drastically limited with respect to a complete manual tracing and the formal fuzzy framework supports in the overall process of estimation without arbitrary solutions. A set of experiments have been conceived and in the context of MRI Brain segmentation studies.
Results obtained prove the reliability of the proposed label fusion strategy that can be considered alternative to conventional Voting Rules
Automatic Segmentation of MR Brain Tumor Images using Support Vector Machine in Combination with Graph Cut
No full textThis work focuses the attention on the automatic segmentation of meningioma from multispectral brain
Magnetic Resonance imagery. The Authors address the segmentation task by proposing a fully automatic
method hierarchically structured in two phases. The preliminary unsupervised phase is based on Graph Cut
framework. In the second phase, preliminary segmentation results are refined using a supervised
classification based on Support Vector Machine. The overall segmentation procedure is conceived fully
automatic and tailored to non-volumetric data characterized by poor inter-slice spacing, in an attempt to
facilitate the insertion in clinical practice. The results obtained in this preliminary study are encouraging and
prove that the segmentation benefits from the allied use of Graph Cut and Support Vector Machine
frameworks
2D MRI brain segmentation by using feasibility constraints
No full textThe work describes a 2D brainn segmentation algorithm that uses graph searching principle
Glial brain tumor detection by using symmetry analysis
No full textIn this work a fully automatic algorithm to detect brain tumors by using symmetry analysis is proposed.
A clustering method based on energy minimization through Graph-Cut is applied on the volume computed as a difference between the left hemisphere and the right hemisphere mirrored across the symmetry plane. Differential analysis involves the loss the knowledge of the tumor side. Through an histogram analysis the ill hemisphere is recognized. Many experiments are performed to assess the performance of the detection strategy on MRI volumes in presence of tumors varied in terms of shapes positions and intensity levels. The experiments showed good results also in complex situations
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