337 research outputs found
Modified mass-spring system for physically based deformation modeling
Mass-spring systems are considered the simplest and most intuitive of all deformable models. They are computationally efficient, and can handle large deformations with ease. But they suffer several intrinsic limitations. In this book a modified mass-spring system for physically based deformation modeling that addresses the limitations and solves them elegantly is presented. Several implementations in modeling breast mechanics, heart mechanics and for elastic images registration are presented
Modified mass-spring system for physically based deformation modeling
Mass-spring systems are considered the simplest and most intuitive of all deformable models. They are computationally efficient, and can handle large deformations with ease. But they suffer several intrinsic limitations. In this book a modified mass-spring system for physically based deformation modeling that addresses the limitations and solves them elegantly is presented. Several implementations in modeling breast mechanics, heart mechanics and for elastic images registration are presented
Mammography
In this volume, the topics are constructed from a variety of contents: the bases of mammography systems, optimization of screening mammography with reference to evidence-based research, new technologies of image acquisition and its surrounding systems, and case reports with reference to up-to-date multimodality images of breast cancer. Mammography has been lagged in the transition to digital imaging systems because of the necessity of high resolution for diagnosis. However, in the past ten years, technical improvement has resolved the difficulties and boosted new diagnostic systems. We hope that the reader will learn the essentials of mammography and will be forward-looking for the new technologies. We want to express our sincere gratitude and appreciation?to all the co-authors who have contributed their work to this volume
An Optical Machine Vision System for Applications in Cytopathology
This paper discusses a new approach to the processes of object detection, recognition and classification in a digital image focusing on problem in Cytopathology. A unique self learning procedure is presented in order to incorporate expert knowledge. The classification method is based on the application of a set of features which includes fractal parameters such as the Lacunarity and Fourier dimension. Thus, the approach includes the characterisation of an object in terms of its fractal properties and texture characteristics. The principal issues associated with object recognition are presented which include the basic model and segmentation algorithms. The self-learning procedure for designing a decision making engine using fuzzy logic and membership function theory is also presented and a novel technique for the creation and extraction of information from a membership function considered. The methods discussed and the algorithms developed have a range of applications and in this work, we focus the engineering of a system for automating a Papanicolaou screening test
Scutoids unveil the three-dimensional packing in curved epithelia
As animals develop, the initial simple planar epithelia of the early embryos must
acquire complex three-dimensional architectures to form the final functional tissues of
the organism. Epithelial bending is, therefore, a general principle of all developing
systems. Scholarly publications depict epithelial cells as prisms where their basal and
apical faces resemble polygons with the same number of sides. The accepted view is
that, when a tissue bend, the cells of the epithelia modify their shape from columnar to
what has been traditionally called “bottle shape”. However, the morphology and packing
of curved epithelia remain largely unknown. Here, through mathematical and
computational modelling, we show that cells in bent epithelia necessarily undergo
intercalations along the apico-basal axis. This event forces cells to exchange their
neighbours between their basal and apical surfaces. Therefore, the traditional view of
epithelial cells as simple prisms is incompatible with this phenomenon. Consequently,
epithelial cells are compelled to adopt a novel geometrical shape that we have named
“scutoid”. The in-depth analysis of diverse epithelial tissues and organs confirm the
generation of apico-basal transitions among cell during morphogenesis. Using
biophysics arguments, we determine that scutoids support the energetic minimization on
the tissue and conclude that the transitions along the apico-basal axis stabilize the threedimensional packing of the tissue. Altogether, we argue that scutoids are nature’s
solution to bend efficiently epithelia, and the missing piece for developing a unifying and
realistic model of epithelial architecture
Micro/Nano Devices for Blood Analysis, Volume II
The development of micro- and nanodevices for blood analysis continues to be a growing interdisciplinary subject that demands the careful integration of different research fields. Following the success of the book “Micro/Nano Devices for Blood Analysis”, we invited more authors from the scientific community to participate in and submit their research for a second volume. Researchers from different areas and backgrounds cooperated actively and submitted high-quality research, focusing on the latest advances and challenges in micro- and nanodevices for diagnostics and blood analysis; micro- and nanofluidics; technologies for flow visualization and diagnosis; biochips, organ-on-a-chip and lab-on-a-chip devices; and their applications to research and industry
Morphological analysis of optical coherence tomography images for automated classification of gastrointestinal tissues
The impact of digestive diseases, which include disorders affecting the oropharynx and alimentary canal, ranges from the inconvenience of a transient diarrhoea to dreaded conditions such as pancreatic cancer, which are usually fatal. Currently, the major limitation for the diagnosis of such diseases is sampling error because, even in the cases of rigorous adherence to biopsy protocols, only a tiny fraction of the surface of the involved gastrointestinal tract is sampled. Optical coherence tomography (OCT), which is an interferometric imaging technique for the minimally invasive measurement of biological samples, could decrease sampling error, increase yield, and even eliminate the need for tissue sampling provided that an automated, quick and reproducible tissue classification system is developed. Segmentation and quantification of ophthalmologic pathologies using OCT traditionally rely on the extraction of thickness and size measures from the OCT images, but layers are often not observed in nonopthalmic OCT imaging. Distinct mathematical methods, namely Principal Component Analysis (PCA) and textural analyses including both spatial textural analysis derived from the two-dimensional discrete Fourier transform (DFT) and statistical texture analysis obtained independently from center-symmetric autocorrelation (CSAC) and spatial grey-level dependency matrices (SGLDM), have been previously reported to overcome this problem. We propose an alternative approach consisting of a region segmentation according to the intensity variation along the vertical axis and a pure statistical technique for feature quantification, i.e. morphological analysis. Qualitative and quantitative comparisons with traditional approaches are accomplished in the discrimination of freshly-excised specimens of gastrointestinal tissues to exhibit the feasibility of the proposed method for computer-aided diagnosis (CAD) in the clinical setting
Development Of Semi-Automatic Liver Segmentation Method For Three-Dimensional Computed Tomography Dataset
Segmentation of liver from 3D computed tomography (CT) dataset is very important in
hepatic disease diagnosis and treatment planning. Manual segmentation gives accurate result but
the process is tedious and time-consuming due to a large number of slices produced by the CT
scanner. Low contrast of liver boundary with neighbouring organs, high shape variability of liver
and presence of various liver pathologies will affect the accuracy of automatic liver segmentation
and thus make automatic liver segmentation a challenging task. Therefore, a semi-automated liver
segmentation program is developed in this project in order to obtain high accuracy in liver
segmentation and reduce the time required for manual liver segmentation. The proposed
algorithm can be divided into three stages. The first stage is parameter setup and pre-processing.
User interaction is required to setup the segmentation parameters. For pre-processing, anisotropic
diffusion filtering is applied to reduce noise in the image and smooth the image. In second stage,
thresholding is applied to CT images to extract the possible liver regions. Then, morphological
closing and opening are used close small holes inside liver region and break the thin connections
between liver and neighbouring organs. Hole-filling is employed to fill up the large holes inside
liver region. Next, the connected component analysis is performed to extract liver region from
the CT slices. The last stage is post-processing. In post-processing, the contour of liver is smooth
by binary Gaussian filter. The liver segmentation program with proposed algorithm is evaluated
with CT datasets obtained from SLIVER07 to prove its effectiveness in liver segmentation. The
results of liver segmentation achieved average VOE of 9.9
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