Advances in Motion Estimators for Applications in Computer Vision

abstract: Motion estimation is a core task in computer vision and many applications utilize optical flow methods as fundamental tools to analyze motion in images and videos. Optical flow is the apparent motion of objects in image sequences that results from relative motion between the objects and the imaging perspective. Today, optical flow fields are utilized to solve problems in various areas such as object detection and tracking, interpolation, visual odometry, etc. In this dissertation, three problems from different areas of computer vision and the solutions that make use of modified optical flow methods are explained. The contributions of this dissertation are approaches and frameworks that introduce i) a new optical flow-based interpolation method to achieve minimally divergent velocimetry data, ii) a framework that improves the accuracy of change detection algorithms in synthetic aperture radar (SAR) images, and iii) a set of new methods to integrate Proton Magnetic Resonance Spectroscopy (1HMRSI) data into threedimensional (3D) neuronavigation systems for tumor biopsies. In the first application an optical flow-based approach for the interpolation of minimally divergent velocimetry data is proposed. The velocimetry data of incompressible fluids contain signals that describe the flow velocity. The approach uses the additional flow velocity information to guide the interpolation process towards reduced divergence in the interpolated data. In the second application a framework that mainly consists of optical flow methods and other image processing and computer vision techniques to improve object extraction from synthetic aperture radar images is proposed. The proposed framework is used for distinguishing between actual motion and detected motion due to misregistration in SAR image sets and it can lead to more accurate and meaningful change detection and improve object extraction from a SAR datasets. In the third application a set of new methods that aim to improve upon the current state-of-the-art in neuronavigation through the use of detailed three-dimensional (3D) 1H-MRSI data are proposed. The result is a progressive form of online MRSI-guided neuronavigation that is demonstrated through phantom validation and clinical application.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Central Nervous System Tumors

Though the treatment of central nervous system (CNS) tumors has been challenging, new advances have helped us better understand the molecular and genetic makeup of many tumor types, and new chemotherapies and immunotherapies have extended survival in patients with aggressive primary CNS tumors. This book discusses pediatric and adult tumors of the CNS, the classification schemes used to categorize them, advances in surgical techniques, and several important genetic alterations found in these tumors. We hope this book contributes to the reader’s understanding of these tumors and provides the most up-to-date and cutting-edge discoveries in this exciting field

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Multimodal wavelet embedding representation for data combination (MaWERiC): integratingmagnetic resonance imaging and spectroscopy for prostate cancer detection,”

Recently, both Magnetic Resonance (MR) Imaging (MRI) and Spectroscopy (MRS) have emerged as promising tools for detection of prostate cancer (CaP). However, due to the inherent dimensionality differences in MR imaging and spectral information, quantitative integration of T 2 weighted MRI (T 2 w MRI) and MRS for improved CaP detection has been a major challenge. In this paper, we present a novel computerized decision support system called multimodal wavelet embedding representation for data combination (MaWERiC) that employs, (i) wavelet theory to extract 171 Haar wavelet features from MRS and 54 Gabor features from T 2 w MRI, (ii) dimensionality reduction to individually project wavelet features from MRS and T 2 w MRI into a common reduced Eigen vector space, and (iii), a random forest classifier for automated prostate cancer detection on a per voxel basis from combined 1.5 T in vivo MRI and MRS. A total of 36 1.5 T endorectal in vivo T 2 w MRI and MRS patient studies were evaluated per voxel by MaWERiC using a three-fold cross validation approach over 25 iterations. Ground truth for evaluation of results was obtained by an expert radiologist annotations of prostate cancer on a per voxel basis who compared each MRI section with corresponding ex vivo wholemount histology sections with the disease extent mapped out on histology. Results suggest that MaWERiC based MRS T 2 w meta-classifier (mean AUC, m = 0.89 AE 0.02) significantly outperformed (i) a T 2 w MRI (using wavelet texture features) classifier (m = 0.55 AE 0.02), (ii) a MRS (using metabolite ratios) classifier (m = 0.77 AE 0.03), (iii) a decision fusion classifier obtained by combining individual T 2 w MRI and MRS classifier outputs (m = 0.85 AE 0.03), and (iv) a data combination method involving a combination of metabolic MRS and MR signal intensity features (m = 0.66 AE 0.02)

Advanced Neuroimaging in Brain Tumors : Diffusion, Spectroscopy, Perfusion and Permeability MR imaging for the evaluation of tumor characterization and surgical treatment planning.

Advanced Neuroimaging in Brain Tumors: Diffusion, Spectroscopy, Perfusion and Permeability for the evaluation of tumor characterization and surgical treatment planning. The current standard of neuroimaging for brain tumor evaluation is anatomy-based MRI. Unfortunately, MRI does not fully reflect the complicated biology of infiltrative glioma, and has a limited capacity to differentiate a high-grade glioma (HGG) from a single brain metastasis. Grading of gliomas is important for the determination of appropriate treatment strategies and in the assessment of prognosis. It is clinically important to distinguish HGG from a single brain metastasis, because medical staging, surgical planning, and therapeutic decisions are different for each tumor type. The basis for this thesis was 208 patients admitted at Oslo University Hospital-Ullevål with the diagnosis of brain tumor between 2006 and 2010. The aim of this thesis was to evaluate in terms of diagnostic examination performance in the clinical decision-making process the use of advanced MRI techniques, namely, diffusion-weighted imaging (DWI), magnetic resonance spectroscopic imaging (MRSI), and T2*-weighted first pass dynamic susceptibility contrast-enhanced perfusion MRI (DSC MRI) in the diagnosis and preoperative planning of brain tumors, with focus in the grading and characterization of gliomas, as well as in the assessment of the peri-enhancing region aiming to demonstrate tumor-infiltration and tumor-free edema. In this thesis, we have demonstrated that MRSI and DSC MRI can be helpful to discriminate HGG from solitary metastases, supporting the hypothesis that these advanced MRI techniques can detect infiltration of tumor cells in the peri-enhancing region. We have demonstrated that combining DWI and MRSI increases the accuracy in the determination of glioma grade. We identified differences among all glial tumor grades for the parameters cerebral blood volume (rCBV) and microvascular leakage (MVL) derived from DSC MRI. Our correlation analysis indicate that MVL, rCBV, and cerebral blood flow (rCBF) may be related to different aspects of tumor angiogeneseis

Diffusion imaging and tractography in the paediatric neurosurgical population

Diffusion MRI uses magnetic field gradients to sensitise a MR sequence to in vivo water diffusion. Application of these gradients in specific directions (20 in this work) enables a 3D representation of diffusion on a voxel basis. Quantitative diffusion measures are derived; using the voxel maximal diffusion direction and linking neighbouring voxels iteratively based on this creates a visual construct of the white matter: tractography. It is not possible, currently, to non-invasively determine the histological nature of an intracranial tumour. We recruited paediatric patients with radiological evidence of such lesions from April 2006 to January 2008 and retrospectively to August 2003. We used diffusion MR metrics to discriminate paediatric central nervous system tumours based on existing histological diagnoses. Using apparent diffusion coefficient histograms, common posterior fossa childhood tumours were differentiated with 93% success; Primitive neuroectodermal tumours (PNET) and supratentorial atypical teratoid rhabdoid tumours (ATRT) were separated in 100% of cases. Development of these methods with a larger population may facilitate the obviation of surgical biopsy and its attendant risks. Diffusion data was used to reconstruct the cerebellar white matter anatomy using tractography. Initially a population of normal subjects were investigated using single region of interest (ROI) analysis. DTI metrics were implemented, demonstrating the existence of white matter asymmetry where lateralisation corresponded to handedness in 17 right-handed subjects. To asses functional significance of changes in DTI metrics; clinical cerebellar dysfunction was correlated with changes in cerebellar white matter DTI metrics in a patient population with posterior fossa tumours and with the normal population. Fractional anisotropy of the tracts was reduced in patients with tumours d clinical cerebellar signs as compared to healthy individuals. This work demonstrates that diffusion MRI and tractography metrics may enable discrimination of paediatric CNS tumour type and are related to the functional integrity of cerebellar white matter tracts

Clinical Management and Evolving Novel Therapeutic Strategies for Patients with Brain Tumors

A dramatic increase in knowledge regarding the molecular biology of brain tumors has been established over the past few years, and this has lead to the development of novel therapeutic strategies for these patients. In this book a review of the options available for the clinical management of patients with these tumors are outlined. In addition advances in radiology both for pre-operative diagnostic purposes along with surgical planning are described. Furthermore a review of newer developments in chemotherapy along with the evolving field of photodynamic therapy both for intra-operative management and subsequent therapy is provided. A discussion of certain surgical management issues along with tumor induced epilepsy is included. Finally a discussion of the management of certain unique problems including brain metastases, brainstem glioma, central nervous system lymphoma along with issues involving patients with a brain tumor and pregnancy is provided