A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term

Image quality and dosimetry of a dual source computed tomography scanner with special emphasis on radiation dose of lung in a chest examination

The purpose of the current study was to evaluate the Dual Source Computed Tomography scanner in terms of Image quality and dosimetry with special emphasis of radiation dose of lung in a Chest examination.Zielsetzung der Studie war die Evaluation eines Dual-Source-Computertomographen hinsichtlich Bildqualität und Dosimetrie mit speziellem Fokus auf der Lungendosis in Thoraxuntersuchungen

Cluster analysis of the signal curves in perfusion DCE-MRI datasets

Pathological studies show that tumors consist of different sub-regions with more homogeneous vascular properties during their growth. In addition, destroying tumor's blood supply is the target of most cancer therapies. Finding the sub-regions in the tissue of interest with similar perfusion patterns provides us with valuable information about tissue structure and angiogenesis. This information on cancer therapy, for example, can be used in monitoring the response of the cancer treatment to the drug. Cluster analysis of perfusion curves assays to find sub-regions with a similar perfusion pattern. The present work focuses on the cluster analysis of perfusion curves, measured by dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). The study, besides searching for the proper clustering method, follows two other major topics, the choice of an appropriate similarity measure, and determining the number of clusters. These three subjects are connected to each other in such a way that success in one direction will help solving the other problems. This work introduces a new similarity measure, parallelism measure (PM), for comparing the parallelism in the washout phase of the signal curves. Most of the previous works used the Euclidean distance as the measure of dissimilarity. However, the Euclidean distance does not take the patterns of the signal curves into account and therefore for comparing the signal curves is not sufficient. To combine the advantages of both measures a two-steps clustering is developed. The two-steps clustering uses two different similarity measures, the introduced PM measure and Euclidean distance in two consecutive steps. The results of two-steps clustering are compared with the results of other clustering methods. The two-steps clustering besides good performance has some other advantages. The granularity and the number of clusters are controlled by thresholds defined by considering the noise in signal curves. The method is easy to implement and is robust against noise. The focus of the work is mainly the cluster analysis of breast tumors in DCE-MRI datasets. The possibility to adopt the method for liver datasets is studied as well

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

Dynamic Contrast-Enhanced MR Microscopy: Functional Imaging in Preclinical Models of Cancer

<p>Dynamic contrast-enhanced (DCE) MRI has been widely used as a quantitative imaging method for monitoring tumor response to therapy. The pharmacokinetic parameters derived from this technique have been used in more than 100 phase I trials and investigator led studies. The simultaneous challenges of increasing the temporal and spatial resolution, in a setting where the signal from the much smaller voxel is weaker, have made this MR technique difficult to implement in small-animal imaging. Existing preclinical DCE-MRI protocols acquire a limited number of slices resulting in potentially lost information in the third dimension. Furthermore, drug efficacy studies measuring the effect of an anti-angiogenic treatment, often compare the derived biomarkers on manually selected tumor regions or over the entire volume. These measurements include domains where the interpretation of the biomarkers may be unclear (such as in necrotic areas).</p><p>This dissertation describes and compares a family of four-dimensional (3D spatial + time), projection acquisition, keyhole-sampling strategies that support high spatial and temporal resolution. An interleaved 3D radial trajectory with a quasi-uniform distribution of points in k-space was used for sampling temporally resolved datasets. These volumes were reconstructed with three different k-space filters encompassing a range of possible keyhole strategies. The effect of k-space filtering on spatial and temporal resolution was studied in phantoms and in vivo. The statistical variation of the DCE-MRI measurement is analyzed by considering the fundamental sources of error in the MR signal intensity acquired with the spoiled gradient-echo (SPGR) pulse sequence. Finally, the technique was applied for measuring the extent of the opening of the blood-brain barrier in a mouse model of pediatric glioma and for identifying regions of therapeutic effect in a model of colorectal adenocarcinoma. </p><p>It is shown that 4D radial keyhole imaging does not degrade the system spatial and temporal resolution at a cost of 20-40% decrease in SNR. The time-dependent concentration of the contrast agent measured in vivo is within the theoretically predicted limits. The uncertainty in measuring the pharmacokinetic parameters with the sequences is of the same order, but always higher than, the uncertainty in measuring the pre-injection longitudinal relaxation time. The histogram of the time-to-peak provides useful knowledge about the spatial distribution of K^trans and microvascular density. Two regions with distinct kinetic parameters were identified when the TTP map from DCE-MRM was thresholded at 1000 sec. The effect of bevacizumab, as measured by a decrease in K^trans, was confined to one of these regions. DCE-MRI studies may contribute unique insights into the response of the tumor microenvironment to therapy.</p>Dissertatio

Computer-Aided, Multi-Modal, and Compression Diffuse Optical Studies of Breast Tissue

Diffuse Optical Tomography and Spectroscopy permit measurement of important physiological parameters non-invasively through ~10 cm of tissue. I have applied these techniques in measurements of human breast and breast cancer. My thesis integrates three loosely connected themes in this context: multi-modal breast cancer imaging, automated data analysis of breast cancer images, and microvascular hemodynamics of breast under compression. As per the first theme, I describe construction, testing, and the initial clinical usage of two generations of imaging systems for simultaneous diffuse optical and magnetic resonance imaging. The second project develops a statistical analysis of optical breast data from many spatial locations in a population of cancers to derive a novel optical signature of malignancy; I then apply this data-derived signature for localization of cancer in additional subjects. Finally, I construct and deploy diffuse optical instrumentation to measure blood content and blood flow during breast compression; besides optics, this research has implications for any method employing breast compression, e.g., mammography

Registration and analysis of dynamic magnetic resonance image series

Cystic fibrosis (CF) is an autosomal-recessive inherited metabolic disorder that affects all organs in the human body. Patients affected with CF suffer particularly from chronic inflammation and obstruction of the airways. Through early detection, continuous monitoring methods, and new treatments, the life expectancy of patients with CF has been increased drastically in the last decades. However, continuous monitoring of the disease progression is essential for a successful treatment. The current state-of-the-art method for lung disease detection and monitoring is computed tomography (CT) or X-ray. These techniques are ill-suited for the monitoring of disease progressions because of the ionizing radiation the patient is exposed during the examination. Through the development of new magnetic resonance imaging (MRI) sequences and evaluation methods, MRI is able to measure physiological changes in the lungs. The process to create physiological maps, i.e. ventilation and perfusion maps, of the lungs using MRI can be split up into three parts: MR-acquisition, image registration, and image analysis. In this work, we present different methods for the image registration part and the image analysis part. We developed a graph-based registration method for 2D dynamic MR image series of the lungs in order to overcome the problem of sliding motion at organ boundaries. Furthermore, we developed a human-inspired learning-based registration method. Here, the registration is defined as a sequence of local transformations. The sequence-based approach combines the advantage of dense transformation models, i.e. large space of transformations, and the advantage of interpolating transformation models, i.e. smooth local transformations. We also developed a general registration framework called Autograd Image Registration Laboratory (AIRLab), which performs automatic calculation of the gradients for the registration process. This allows rapid prototyping and an easy implementation of existing registration algorithms. For the image analysis part, we developed a deep-learning approach based on gated recurrent units that are able to calculate ventilation maps with less than a third of the number of images of the current method. Automatic defect detection in the estimated MRI ventilation and perfusion maps is essential for the clinical routine to automatically evaluate the treatment progression. We developed a weakly supervised method that is able to infer a pixel-wise defect segmentation by using only a continuous global label during training. In this case, we directly use the lung clearance index (LCI) as a global weak label, without any further manual annotations. The LCI is a global measure to describe ventilation inhomogeneities of the lungs and is obtained by a multiple breath washout test

Automatic Computation of Liver and Lung Perfusion Parameters through the Analysis of CT Image Sequences

Computed Tomography perfusion (CTp) is a promising technique for early assessment of the effectiveness of the new anti-angiogenic therapies for cancer treatment. Nonetheless, some difficulties to achieve standardized results in the examinations have slowed down its application in the daily clinical practice. This Thesis addresses three important open issues: a lack of methods to measure results reliability, the clinical relevance of the global perfusion parameter values commonly utilised, a critical revision of protocols of the multi-centre studies. In this work, lung and liver CTp examinations were considered, being among the most studied sites in oncology. First, I set up and validated an error index capable to measure the quality of perfusion results. After that, structured regions affected by noise (e.g., artefacts), vessels or bronchi have been looked for on whole slices. Also exploiting a new error index, an adaptive algorithm has allowed detecting and excluding all those regions automatically. The common practice in CTp perfusion studies is providing one averaged value only for each perfusion parameter, computed on the whole tumour, to reduce data variability, but at the expense of tumour heterogeneity. Accordingly, whole lung lesions were considered to inquire into the clinical representativeness of global perfusion values. After proposing a statistical index to quantify tumour functional heterogeneity, it has been proved that using one global value even misleads clinical considerations. The last part of the Thesis regards the activities carried out in the widest European multi-centre study on liver CT. Some algorithms to improve the accuracy of perfusion results were developed to favour comparisons of multi-centre CTp examinations. Some meaningful results regarding baseline and blood flow values of liver are discussed, highlighting whether different CT scanners affect CTp outcomes. Finally, tentative guidelines are provided to help considering all the concealable sources of heterogeneity in advance, before planning the protocols

A discrete graph Laplacian for signal processing

In this thesis we exploit diffusion processes on graphs to effect two fundamental problems of image processing: denoising and segmentation. We treat these two low-level vision problems on the pixel-wise level under a unified framework: a graph embedding. Using this framework opens us up to the possibilities of exploiting recently introduced algorithms from the semi-supervised machine learning literature. We contribute two novel edge-preserving smoothing algorithms to the literature. Furthermore we apply these edge-preserving smoothing algorithms to some computational photography tasks. Many recent computational photography tasks require the decomposition of an image into a smooth base layer containing large scale intensity variations and a residual layer capturing fine details. Edge-preserving smoothing is the main computational mechanism in producing these multi-scale image representations. We, in effect, introduce a new approach to edge-preserving multi-scale image decompositions. Where as prior approaches such as the Bilateral filter and weighted-least squares methods require multiple parameters to tune the response of the filters our method only requires one. This parameter can be interpreted as a scale parameter. We demonstrate the utility of our approach by applying the method to computational photography tasks that utilise multi-scale image decompositions. With minimal modification to these edge-preserving smoothing algorithms we show that we can extend them to produce interactive image segmentation. As a result the operations of segmentation and denoising are conducted under a unified framework. Moreover we discuss how our method is related to region based active contours. We benchmark our proposed interactive segmentation algorithms against those based upon energy-minimisation, specifically graph-cut methods. We demonstrate that we achieve competitive performance