- Date: 110506
Graph-based Methods for Interactive Image Segmentation
Student: Filip Malmberg
Supervisor: Ingela Nyström
Assistant Supervisor: Ewert Bengtsson
Opponent: Jayaram K. Udupa, Medical Image Processing Group, Dept. of Radiology,
University of Pennsylvania, USA
Committee: Carolina Wählby,
Örjan Smedby, Dept. of Medical and Health Sciences, Linköping University,
Ghassan Hamarneh, School of Computing Science, Simon Fraser University, Burnaby, BC, Canada,
Magnus Borga, Dept. of Biomedical Engineering, Linköping University,
Fredrik Kahl, Mathematical Imaging Group, Centre for Mathematical Sciences, Faculty of Engineering, Lund University
Publisher: Acta Universitatis Upsaliensis, ISBN: 978-91-554-8037-0
Abstract: The subject of digital image, analysis deals with extracting relevant information from image data, stored in digital form in a computer. A fundamental problem in image analysis is image segmentation, i.e., the identification and separation of relevant objects and structures in an image. Accurate segmentation of objects of interest is often required before further processing and analysis can be performed.
Despite years of active research, fully automatic segmentation of arbitrary images remains an unsolved problem. Interactive, or semi-automatic, segmentation methods use human expert knowledge as additional input, thereby making the segmentation problem more tractable. The goal of interactive segmentation methods is to minimize the required user interaction time, while maintaining tight user control to guarantee the correctness of the results. Methods for interactive segmentation typically operate under one of two paradigms for user guidance: (1) Specification of pieces of the boundary of the desired object(s). (2) Specification of correct segmentation labels for a small subset of the image elements. These types of user input are referred to as boundary constraints and regional constraints, respectively.
This thesis concerns the development of methods for interactive segmentation, using a graph-theoretic approach. We view an image as an edge weighted graph, whose vertex set is the set of image elements, and whose edges are given by an adjacency relation among the image elements. Due to its discrete nature and mathematical simplicity, this graph based image representation lends itself well to the development of efficient, and provably correct, methods.
The contributions in this thesis may be summarized as follows:
- Existing graph-based methods for interactive segmentation are modified to improve their performance on images with noisy or missing data, while maintaining a low computational cost.
- Fuzzy techniques are utilized to obtain segmentations from which feature measurements can be made with increased precision.
- A new paradigm for user guidance, that unifies and generalizes regional and boundary constraints, is proposed.
The practical utility of the proposed methods is illustrated with examples from the medical field.
- Date: 110923
Evaluation of Osseointegration using Image Analysis and Visualization of 2D and 3D Image Data
Student: Hamid Sarve
Supervisor: Gunilla Borgefors
Assistant Supervisors: Joakim Lindblad; Carina Johansson, Institute of Odontology, The Sahlgrenska Academy, Göteborg
Opponent: Prof. em. Albert Vossepoel, Quantitative Imaging Group, Delft University of Technology, The Netherlands
Committee: Christina Lindh, Dept. of Oral Radiology, Faculty of Odontology, Malmö University,
Carolina Wählby,
Andrew Mehnert, Signals and Systems, Chalmers University of Technology, Göteborg
Publisher: Acta Universitatis agriculturae Sueciae, ISBN 978-91-576-7605-4
Abstract: Computerized image analysis, the discipline of using computers to automatically extract information from digital images, is a powerful tool for automating time consuming analysis tasks. In this thesis, image analysis and visualization methods are developed to facilitate the evaluation of osseointegration, i.e., the biological integration of a load-carrying implant in living bone. Adequate osseointegration is essential in patients who are in need of implant treatment. New implant types, with variations in bulk material and surface structural parameters, are continuously being developed. The main goal is to improve and speed up the osseointegration and thereby enhance patient well-being. The level of osseointegration can be evaluated by quantifying the bone tissue in proximity to the implant in e.g., light microscopy images of thin cross sections of bone implant samples extracted from humans or animals. This operator dependent quantitative analysis is cumbersome, time consuming and subjective. Furthermore, the thin sections represent only a small region of the whole sample. In this thesis work, computerized image analysis methods are developed to automate the quantification step. An image segmentation method is proposed for classifying the pixels of the images as bone tissue, non-bone tissue or implant. Subsequently, bone area and bone implant contact length in regions of interest are quantified. To achieve an accurate classification, the segmentation is based on both intensity and spatial information of the pixels. The automated method speeds up and facilitates the evaluation of osseointegration in the research laboratories. Another aim of this thesis is extending the 2D analysis to 3D and presenting methods for visualization of the 3D image volumes. To get a complete picture, information from the whole sample should be considered, rather than thin sections only. As a first step, 3D imaging of the implant samples is evaluated. 3D analysis methods, which follow the helix shaped implant thread and collects quantified features along the path, are presented. Additionally, methods for finding the position of the 2D section in the corresponding 3D image volume, i.e., image registration, are presented, enabling a direct comparison of the data from the two modalities. These novel and unique 3D quantification and visualization methods support the biomaterial researchers with improved tools for gaining a wider insight into the osseointegration process, with the ultimate goal of improved quality of life for the patients.
- Date: 111025
Spectral Image Filtering Methods for Biomedical Applications
Student: Khalid Khan M. Niazi
Supervisor: Ingela Nyström
Assistant Supervisor: Ewert Bengtsson
Opponent: Lucas van Vliet, Delft University of Technology, The Netherlands
Committee: Josef Bigun, Intelligent Systems Laboratory, Halmstad University,
Gunilla Borgefors,
Ulf Eriksson, Dept. of Medical Cell Biology, UU,
Michael Felsberg, Computer Vision Laboratory, Linköping University,
Lennart Thurfjell, GE Healthcare, Uppsala
Publisher: Acta Universitatis Upsaliensis, ISBN:978-91-554-8155-1
Abstract: Filtering is a key step in digital image processing and analysis. It is mainly used for amplification or attenuation of some frequencies depending on the nature of the application. Filtering can either be performed in the spatial domain or in a transformed domain. The selection of the filtering method, filtering domain, and the filter parameters are often driven by the properties of the underlying image. This thesis presents three different kinds of biomedical image filtering applications, where the filter parameters are automatically determined from the underlying images.
Filtering can be used for image enhancement. We present a robust image dependent filtering method for intensity inhomogeneity correction of biomedical images. In the presented filtering method, the filter parameters are automatically determined from the grey-weighted distance transform of the magnitude spectrum. An evaluation shows that the filter provides an accurate estimate of intensity inhomogeneity.
Filtering can also be used for analysis. The thesis presents a filtering method for heart localization and robust signal detection from video recordings of rat embryos. It presents a strategy to decouple motion artifacts produced by the non-rigid embryonic boundary from the heart. The method also filters out noise and the trend term with the help of empirical mode decomposition. Again, all the filter parameters are determined automatically based on the underlying signal.
Transforming the geometry of one image to fit that of another one, so called image registration, can be seen as a filtering operation of the image geometry. To assess the progression of eye disorder, registration between temporal images is often required to determine the movement and development of the blood vessels in the eye. We present a robust method for retinal image registration. The method is based on particle swarm optimization, where the swarm searches for optimal registration parameters based on the direction of its cognitive and social components. An evaluation of the proposed method shows that the method is less susceptible to becoming trapped in local minima than previous methods.
With these thesis contributions, we have augmented the filter toolbox for image analysis with methods that adjust to the data at hand.
- Date: 111111
Methods for 2D and 3D Quantitative Microscopy of Biological Samples
Student: Amin Allalou
Supervisor: Carolina Wählby
Assistant Supervisors: Ewert Bengtsson, Ida-Maria Sintorn
Opponent: Jens Rittscher, GE Global Research & Rensselaer Polytechnic Institute, USA
Committee: Hans Blom, Dept. of Biomolecular Physics, Royal Institute of Technology (KTH),
Ola Friman, FOI, Swedish Defence Research Agency, Linköping,
Reinald Fundele, Evolutionary Biology Centre, UU,
Stina Svensson, Raysearch Labs, Stockholm,
Rasmus Larsen, Dept. of Informatics and Mathematical Modelling, Technical University of Denmark, Denmark
Publisher: Acta Universitatis Upsaliensis, ISBN: 978-91-554-8167-4
Abstract: New microscopy techniques are continuously developed, resulting in more rapid acquisition of large amounts of data. Manual analysis of such data is extremely time-consuming and many features are difficult to quantify without the aid of a computer. But with automated image analysis biologists can extract quantitative measurements and increases throughput significantly, which becomes particularly important in high-throughput screening (HTS). This thesis addresses automation of traditional analysis of cell data as well as automation of both image capture and analysis in zebrafish high-throughput screening.
It is common in microscopy images to stain the nuclei in the cells, and to label the DNA and proteins in different ways. Padlock-probing and proximity ligation are highly specific detection methods that produce point-like signals within the cells. Accurate signal detection and segmentation is often a key step in analysis of these types of images. Cells in a sample will always show some degree of variation in DNA and protein expression and to quantify these variations each cell has to be analyzed individually. This thesis presents development and evaluation of single cell analysis on a range of different types of image data. In addition, we present a novel method for signal detection in three dimensions.
HTS systems often use a combination of microscopy and image analysis to analyze cell-based samples. However, many diseases and biological pathways can be better studied in whole animals, particularly those that involve organ systems and multi-cellular interactions. The zebrafish is a widely-used vertebrate model of human organ function and development. Our collaborators have developed a high-throughput platform for cellular-resolution in vivo chemical and genetic screens on zebrafish larvae. This thesis presents improvements to the system, including accurate positioning of the fish which incorporates methods for detecting regions of interest, making the system fully automatic. Furthermore, the thesis describes a novel high-throughput tomography system for screening live zebrafish in both fluorescence and bright field microscopy. This 3D imaging approach combined with automatic quantification of morphological changes enables previously intractable high-throughput screening of vertebrate model organisms.
- Date: 111202
Spectral Image Processing with Applications in Biotechnology and Pathology
Student: Milan Gavrilovic,
Supervisor: Carolina Wählby
Assistant Supervisors: Ewert Bengtsson, Ingrid Carlbom
Opponent: Robert F. Murphy, Ray and Stephanie Lane Center for Computational Biology, Carnegie Mellon University, USA
Committee: Caroline Kampf, Dept. of Immunology, Genetics and Pathology, UU,
Rachel Errington, School of Medicine, Cardiff University, UK,
Reiner Lenz, Dept. of Science and Technology, Linköping University,
Michal Kozubek, Faculty of Informatics, Masaryk University, Check Republic,
Anders Liljeborg, Dept. of Biomedical and X-ray Physics, Royal Institute of Technology (KTH)
Publisher: Acta Universitatis Upsaliensis, ISBN: 978-91-554-8209-1
Abstract: Color theory was first formalized in the seventeenth century by Isaac Newton just a couple of decades after the first microscope was built. But it was not until the twentieth century that technological advances led to the integration of color theory, optical spectroscopy and light microscopy through spectral image processing. However, while the focus of image processing often concerns modeling of how images are perceived by humans, the goal of image processing in natural sciences and medicine is the objective analysis. This thesis is focused on color theory that promotes quantitative analysis rather than modeling how images are perceived by humans.
Color and fluorescent dyes are routinely added to biological specimens visualizing features of interest. By applying spectral image processing to histopathology, subjectivity in diagnosis can be minimized, leading to a more objective basis for a course of treatment planning. Also, mathematical models for spectral image processing can be used in biotechnology research increasing accuracy and throughput, and decreasing bias.
This thesis presents a model for spectral image formation that applies to both fluorescence and transmission light microscopy. The inverse model provides estimates of the relative concentration of each individual component in the observed mixture of dyes. Parameter estimation for the model is based on decoupling light intensity and spectral information. This novel spectral decomposition method consists of three steps: (1) photon and semiconductor noise modeling providing smoothing parameters, (2) image data transformation to a chromaticity plane removing intensity variation while maintaining chromaticity differences, and (3) a piecewise linear decomposition combining advantages of spectral angle mapping and linear decomposition yielding relative dye concentrations.
The methods described herein were used for evaluation of molecular biology techniques as well as for quantification and interpretation of image-based measurements. Examples of successful applications comprise quantification of colocalization, autofluorescence removal, classification of multicolor rolling circle products, and color decomposition of histological images.
