Content-aware approach for improving biomedical image analysis: an interdisciplinary study series

Biomedicine is a highly interdisciplinary research area at the interface of sciences, anatomy, physiology, and medicine. In the last decade, biomedical studies have been greatly enhanced by the introduction of new technologies and techniques for automated quantitative imaging, thus considerably advancing the possibility to investigate biological phenomena through image analysis. However, the effectiveness of this interdisciplinary approach is bounded by the limited knowledge that a biologist and a computer scientist, by professional training, have of each other’s fields. The possible solution to make up for both these lacks lies in training biologists to make them interdisciplinary researchers able to develop dedicated image processing and analysis tools by exploiting a content-aware approach. The aim of this Thesis is to show the effectiveness of a content-aware approach to automated quantitative imaging, by its application to different biomedical studies, with the secondary desirable purpose of motivating researchers to invest in interdisciplinarity. Such content-aware approach has been applied firstly to the phenomization of tumour cell response to stress by confocal fluorescent imaging, and secondly, to the texture analysis of trabecular bone microarchitecture in micro-CT scans. Third, this approach served the characterization of new 3-D multicellular spheroids of human stem cells, and the investigation of the role of the Nogo-A protein in tooth innervation. Finally, the content-aware approach also prompted to the development of two novel methods for local image analysis and colocalization quantification. In conclusion, the content-aware approach has proved its benefit through building new approaches that have improved the quality of image analysis, strengthening the statistical significance to allow unveiling biological phenomena. Hopefully, this Thesis will contribute to inspire researchers to striving hard for pursuing interdisciplinarity

Texture Analysis of Diffraction Enhanced Synchrotron Images of Trabecular Bone at the Wrist

The purpose of this study is to determine the correlation between texture features of Di raction Enhanced Imaging (DEI) images and trabecular properties of human wrist bone in the assessment of osteoporosis. Osteoporosis is a metabolic bone disorder that is characterized by reduced bone mass and a deterioration of bone structure which results in an increased fracture risk. Since the disease is preventable, diagnostic techniques are of major importance. Bone micro-architecture and Bone mineral density (BMD) are two main factors related to osteoporotic fractures. Trabecular properties like bone volume (BV), trabecular number (Tb.N), trabecular thickness (Tb.Th), bone surface (BS), and other properties of bone, characterizes the bone architecture. Currently, however, BMD is the only measurement carried out to assess osteoporosis. Researchers suggest that bone micro-architecture and texture analysis of bone images along with BMD can provide more accuracy in the assessment. We have applied texture analysis on DEI images and extracted texture features. In our study, we used fractal analysis, gray level co-occurrence matrix (GLCM), texture feature coding method (TFCM), and local binary patterns (LBP) as texture analysis methods to extract texture features. 3D Micro-CT trabecular properties were extracted using SkyScanTM CTAN software. Then, we determined the correlation between texture features and trabecular properties. GLCM energy fea- ture of DEI images explained more than 39% of variance in bone surface by volume ratio (BS/BV), 38% of variance in percent bone volume (BV/TV), and 37% of variance in trabecular number (Tb.N). TFCM homogeneity feature of DEI images explained more than 42% of variance in bone surface (BS) parameter. LBP operator - LBP 11 of DEI images explained more than 34% of vari- ance in bone surface (BS) and 30% of variance in bone surface density (BS/TV). Fractal dimension parameter of DEI images explained more than 47% of variance in bone surface (BS) and 32% of variance in bone volume (BV). This study will facilitate in the quanti cation of osteoporosis beyond conventional BMD

An investigation of techniques in deformable object recognition

The human\u27s innate ability to process information garnered from a visual scene has no parallel in the digital realm. This task is taken for granted in human cognition, but has not been met by a complete digital solution even following years of research. This difficulty can be explained by the shear complexity of the physology of the visual pathway. Although a complete solution has not been created, there are a number of examples of solutions that address parts of the problem. The recognition of deformable objects is the area addressed in this work. The specific task researched was the recognition of creatures in structured visual scenes. The focus was on developing a set of features which are able to differentiate between target creature classes. The implications of this research lie in ecoinformatics and field biology with the automated collection and annotation of biological data. The thesis will present a survey of the current literature addressing techniques which have been used to solve similar problems. An algorithm to perform the recognition will be presented and the results discussed. Finally, potential areas for improvement will be described

Quantitative Imaging in Electron and Confocal Microscopies for Applications in Biology

Among the large number of topics related to the quantification of images in electron and confocal microscopies for applications in biology, we selected four subjects that we consider to be representative of some recent tendencies. The first is the quantification of three-dimensional data sets recorded routinely in scanning confocal microscopy. The second is the quantification of the textural and fractal appearance of images. The two other topics are related to image series, which are more and more often provided by imaging instruments. The first kind of series concerns electron energy-filtered images. We show that the parametric (modelling) approach can be complemented by non-parametric approaches (e.g., different variants of multivariate statistical techniques). The other kind of series consists of multiple mappings of a specimen. We describe several new tools for the study and quantification of the co-location, with potential application to multiple mappings in microanalysis or in fluorescence microscopy

Textural Features in Medical Magnetic Resonance Image Analysis of the Brain and Thigh Muscles

Magnetic resonance imaging (MRI) provides high-quality images with excellent contrast detail of soft tissues and anatomic structures. MR images contain a large amount of detailed information – some of which is invisible to the human eye. Detailed information can be analysed with computer-assisted texture analysis (TA), which is based on features describing the grey level relationships between image pixels. The aim of this thesis was to assess the information content of textural features based on the image histogram, grey level co-occurrence matrix, and grey level run-length matrix. The strengths and limitations of the various textural features in medical MR image analysis were evaluated. The study was conducted by analysing different clinical data with TA in the clinical environment, and the results of the learning process were then gathered in this thesis. Our results indicated that all features have limitations in terms of their discrimination capacity in medical MR images and their dependence on the size of the region of interest and MR imaging parameters. By considering these limitations, TA may help in various MR imaging applications by revealing textural information of the images of various human organs. <br/

Comparison of 2D and 3D MRI Texture Analyses of Functionally Different Hip Muscles

The need for detailed image information to enhance radiological decision making has necessitated computerized analysis of medical images. The superior sensitivity of MRI in detecting subtle changes in soft tissues has facilitated automatic analysis of MR images through texture analysis. Though there have been a number of recent studies in this area, most of them have focused on using 2D MRI texture analysis in detecting and classifying pathological tissues from healthy ones. The objective of this thesis it to examine whether textural differences exist in hip muscles due to exercise-loading differences, if so, the effectiveness of 2D and 3D MRI texture analyses in detecting and characterizing these differences will be examined. Ninety-one high-level female athletes representing five distinct loading sports (High-impact, odd-impact, high-magnitude, low-impact and non-impact exercise-loading) and 20 healthy non-athlete (referent) female subjects were used in this study. A 1.5T MRI scanner (Siemens, Erlangen, Germany) was used to acquire axial T1-weighted FLASH sequence images of the hip muscles. Two-dimensional(2D) and three-dimensional(3D) texture analyses were performed on four specific load-bearing muscles (gluteus maximus, gluteus medius, iliopsoas and obturator internus) using texture analysis application software – MaZda (TUL, Poland: COST Action B11). The computed texture parameters were statistically analyzed (using SPSS, Chicago, Ill.) to ascertain differences in texture between the four muscles, the non-athlete group and the athlete groups, and to characterize them accordingly. A comparative evaluation of 2D and 3D texture analyses was also made. Significant differences (p-value < 0.00833) in texture were recorded between the four muscles. All the four muscles were found to be linearly separable from each other. Moreover, muscle texture of athletes who were involved in high-impact (triple-jumpers and high-jumpers), odd-impact (soccer and squash players) and low-impact (endurance runners) exercise-loadings differed significantly (p-value < 0.01) from that of the non-athletes. Subsequently, the high-impact, odd-impact and low-impact exercise-loading groups were completely separable from the non-athlete group. Contrarily, muscle texture of the high-magnitude (power lifters) and non-impact (swimmers) exercise-loading groups were not found to differ significantly from the non-athletes, some level of overlap was noticed in their classification from the non-athletes. Finally, 3D texture analysis was more effective in detecting and characterizing textural differences in skeletal muscles than the 2D texture analysis. In conclusion, the 3D texture analysis of MR images provides a more accurate quantitative method for detecting and classifying textural differences in skeletal muscles that are associated with specific exercise-loading types

Magneettiresonanssikuvien tekstuurianalyysisovelluksen kehittäminen MATLAB-ympäristössä

This thesis was based on the need to develop a generic software application frame for texture analysis of magnetic resonance (MR) images. In collaboration with the research group at the department of Medical Imaging Centre and Hospital Pharmacy (MICHP) at Tampere University Hospital (TAUH) the goal was to improve the user experience and work flow as well as implement a completely new user interface and key functionalities. The platform was required to be complex enough to manage with image processing algorithms and to provide high level and easily modifiable software architecture. The research group having years of experience with an open-source texture analysis oriented MaZda software the focus of this thesis was to analyse and solve the restrictions based on the observations from using MaZda. MATLAB was chosen as the programming platform due the high-level syntax with powerful built-in properties e.g. Image Processing Toolbox (IPT) that would allow proficient support for computationally demanding processes. Another advantage with MATLAB was the interface support for languages like Fortran, C and C++. MATLAB being commercial software platform, it was acknowledged that achieving a standalone end product would not be possible. Computational performance was also omitted for the purpose this thesis not only due to MATLAB’s limitations but also to keep the scale contained. The improvement suggestions provided by the research group were considered as a rough specification for the software to be implemented. These requirements included extensibility in terms of texture analysis algorithms and simplified user interface to improve the work flow. Selecting MATLAB as the programming environment extended the group of people capable of contributing to the tool in the future. Implementing the frame from the beginning allowed the texture analysis parameters and features to be fully configurable instead of static. The modular visual structure of the software allowed the user to switch between image sets more easily. Removing the region of interest (ROI) limitation ensured that same image set could be utilized more efficiently. The implemented MATLAB application provides a basic frame for more convenient medical image processing flow for texture analysis of MR images but further testing and development is required to complement the tool

Clinical Applicability of MRI Texture Analysis

Radiologisten kuvien tulkinta on perinteisesti perustunut asiantuntijan näköhavaintoihin. Tietokoneavusteisten menetelmien käyttö lisääntyy radiologisessa diagnostiikassa. Tekstuuria eli kuviorakennetta on käytetty erottelevana ominaisuutena kudoksia luokiteltaessa ja karakterisoitaessa. Kuvan kuviorakenteen ominaisuuksia kuvaavia tekstuuriparametreja voidaan laskea erilaisilla matemaattisilla ja signaalinkäsittelymenetelmillä. Tekstuurianalyysi on antanut lupaavia tuloksia magneettikuvien tarkastelussa. Sen avulla on voitu määrittää sekä pieniä hajanaisia että suurempia paikallisia muutoksia. Menetelmällä on mahdollista havaita ihmissilmälle näkymättömiä sekä näkyviä muutoksia. Menetelmää tulisi tutkia edelleen, koska kliinisen menetelmän kehittämistä varten tarvitaan lisätietoa sen soveltuvuudesta erilaisille aineistoille sekä analyysimenetelmän eri vaiheiden optimoimisesta. Tämän väitöstutkimuksen tavoite oli selvittää magneettikuvauksen tekstuurianalyysin kliinistä käytettävyyttä eri kannoilta. Tutkimusaineisto koostui kolmesta potilasmateriaalista ja yhdestä terveiden urheilijoiden joukosta sekä heidän verrokeistaan. Aineisto kerättiin osina Tampereen yliopistollisessa sairaalassa toteutettuja laajempia tutkimusprojekteja, ja mukaan otettiin yhteensä 220 osallistujaa. Ensimmäisessä osatyössä tarkasteltiin pehmytkudoskuvantamista, non-Hodgkin-lymfooman hoitovasteen arviointia tekstuurianalyysilla. Kaksi seuraavaa osatyötä käsitteli keskushermoston kuvantamista: lieviä aivovammoja sekä MS-tautia. Viimeisessä osatyössä arvioitiin liikunnan vaikutusta urheilijoiden ja verrokkien reisiluun kaulan luurakenteeseen. Kudosten ja muutosten vertailuissa oli edustettuna sekä ympäröivästä kudoksesta visuaalisella tarkastelulla erottumattomia että selkeästi erottuvia rakenteita. Lisäksi tutkimuksessa selvitettiin mielenkiintoalueen käsityönä tehtävän rajaamisen ja magneettikuvaussekvenssin valinnan vaikutusta analyysiin. Yhteenvetona todetaan, että tekstuurimenetelmällä on mahdollista havaita ja karakterisoida tutkimukseen valikoidun aineiston edustamia etiologialtaan erilaisia muutoksia kliinisistä 1.5 Teslan magneettikuvista. Tutkimuksessa käsitellyt yksityiskohdat MRI-kuvasarjojen valinnasta sekä mielenkiintoalueiden piirtämisestä antavat pohjaa kliinisen protokollan kehittämiseen. Osa tutkimusaineistoista oli kokeellisia, ja niiden tulokset tulisi vahvistaa laajemmilla kliinisillä tutkimuksilla.The usage of computerised methods in radiological image interpretation is becoming more common. Texture analysis has shown promising results as an image analysis method for detecting non-visible and visible lesions, with a number of applications in magnetic resonance imaging (MRI). Although several recent studies have investigated this topic, there remains a need for further analyses incorporating different clinical materials and taking protocol planning for clinical analyses into account. The purpose of this thesis was to determine the clinical applicability of MRI texture analysis from different viewpoints. This study is based on three patient materials and one collection of healthy athletes and their referents. A total of 220 participants in wider on-going study projects at Tampere University Hospital were included in this thesis. The materials include a study on non-Hodgkin lymphoma, representing soft tissue imaging with malignant disease treatment monitoring; and two studies on central nervous system diseases, mild traumatic brain injury and multiple sclerosis. A musculoskeletal imaging study investigated load-associated physiological changes in healthy participants? bones. Furthermore, manual Region of Interest (ROI) definition methods and the selection of MRI sequences for analyses of visible and non-visible lesions were evaluated. In summary, this study showed that non-visible lesions and physiological changes as well as visible focal lesions of different aetiologies could be detected and characterised by texture analysis of routine clinical 1.5 T scans. The details of MRI sequence selection and ROI definition in this study may serve as guidelines for the development of clinical protocols. However, these studies are partly experimental and need to be validated with larger sample sizes