2,278 research outputs found
Fuzzy-based Propagation of Prior Knowledge to Improve Large-Scale Image Analysis Pipelines
Many automatically analyzable scientific questions are well-posed and offer a
variety of information about the expected outcome a priori. Although often
being neglected, this prior knowledge can be systematically exploited to make
automated analysis operations sensitive to a desired phenomenon or to evaluate
extracted content with respect to this prior knowledge. For instance, the
performance of processing operators can be greatly enhanced by a more focused
detection strategy and the direct information about the ambiguity inherent in
the extracted data. We present a new concept for the estimation and propagation
of uncertainty involved in image analysis operators. This allows using simple
processing operators that are suitable for analyzing large-scale 3D+t
microscopy images without compromising the result quality. On the foundation of
fuzzy set theory, we transform available prior knowledge into a mathematical
representation and extensively use it enhance the result quality of various
processing operators. All presented concepts are illustrated on a typical
bioimage analysis pipeline comprised of seed point detection, segmentation,
multiview fusion and tracking. Furthermore, the functionality of the proposed
approach is validated on a comprehensive simulated 3D+t benchmark data set that
mimics embryonic development and on large-scale light-sheet microscopy data of
a zebrafish embryo. The general concept introduced in this contribution
represents a new approach to efficiently exploit prior knowledge to improve the
result quality of image analysis pipelines. Especially, the automated analysis
of terabyte-scale microscopy data will benefit from sophisticated and efficient
algorithms that enable a quantitative and fast readout. The generality of the
concept, however, makes it also applicable to practically any other field with
processing strategies that are arranged as linear pipelines.Comment: 39 pages, 12 figure
New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty
Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images
New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty
Multidimensional imaging techniques provide powerful ways to examine various
kinds of scientific questions. The routinely produced datasets in the
terabyte-range, however, can hardly be analyzed manually and require an
extensive use of automated image analysis. The present thesis introduces a new
concept for the estimation and propagation of uncertainty involved in image
analysis operators and new segmentation algorithms that are suitable for
terabyte-scale analyses of 3D+t microscopy images.Comment: 218 pages, 58 figures, PhD thesis, Department of Mechanical
Engineering, Karlsruhe Institute of Technology, published online with KITopen
(License: CC BY-SA 3.0, http://dx.doi.org/10.5445/IR/1000057821
Texture Based Multifocus Image Fusion Using Interval Type 2 Fuzzy Logic
Multifocus image fusion is a process of fusing two or more images where region of focus in each image is different.  The objective is to obtain one image which contains the clear regions or in-focus regions of each image. Extracting the focused region in each image is a challenging task. Various techniques are available in literature to perform this task. Texture is one such feature which acts as a discriminating factor between focused and out-of-focus regions. Texture based image fusion has been used in our approach in combination with interval type 2 fuzzy logic and discrete wavelet transforms. Performance metrics obtained using this approach are better compared to other existing techniques. Gray Level Cooccurence Matrix (GLCM) method is used to extract the texture. Type 2 Sugeno fuzzy logic is used to combine the images. The fused image is compared with the reference image when it is available. It is also compared with the original images and performance metrics are computed and presented in this paper. Keywords: Discrete Wavelet Transform, Gray Level Cooccurence Matrix, Image Fusion, Multifocus Image, Type 2 Fuzzy Logic, Mamdani FLS, Sugeno FL
Automated Segmentation of Cerebral Aneurysm Using a Novel Statistical Multiresolution Approach
Cerebral Aneurysm (CA) is a vascular disease that threatens the lives of
many adults. It a ects almost 1:5 - 5% of the general population. Sub-
Arachnoid Hemorrhage (SAH), resulted by a ruptured CA, has high rates of
morbidity and mortality. Therefore, radiologists aim to detect it and diagnose
it at an early stage, by analyzing the medical images, to prevent or reduce its
damages.
The analysis process is traditionally done manually. However, with the
emerging of the technology, Computer-Aided Diagnosis (CAD) algorithms are
adopted in the clinics to overcome the traditional process disadvantages, as
the dependency of the radiologist's experience, the inter and intra observation
variability, the increase in the probability of error which increases consequently
with the growing number of medical images to be analyzed, and the artifacts
added by the medical images' acquisition methods (i.e., MRA, CTA, PET, RA,
etc.) which impedes the radiologist' s work.
Due to the aforementioned reasons, many research works propose di erent
segmentation approaches to automate the analysis process of detecting a CA
using complementary segmentation techniques; but due to the challenging task
of developing a robust reproducible reliable algorithm to detect CA regardless
of its shape, size, and location from a variety of the acquisition methods, a
diversity of proposed and developed approaches exist which still su er from
some limitations.
This thesis aims to contribute in this research area by adopting two promising
techniques based on the multiresolution and statistical approaches in the
Two-Dimensional (2D) domain. The rst technique is the Contourlet Transform
(CT), which empowers the segmentation by extracting features not apparent
in the normal image scale. While the second technique is the Hidden
Markov Random Field model with Expectation Maximization (HMRF-EM),
which segments the image based on the relationship of the neighboring pixels
in the contourlet domain.
The developed algorithm reveals promising results on the four tested Three-
Dimensional Rotational Angiography (3D RA) datasets, where an objective
and a subjective evaluation are carried out. For the objective evaluation, six
performance metrics are adopted which are: accuracy, Dice Similarity Index
(DSI), False Positive Ratio (FPR), False Negative Ratio (FNR), speci city,
and sensitivity. As for the subjective evaluation, one expert and four observers
with some medical background are involved to assess the segmentation visually.
Both evaluations compare the segmented volumes against the ground
truth data
Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification
Data fusion is the process of integrating information from multiple sources to produce specific, comprehensive, unified data about an entity. Data fusion is categorized as low level, feature level and decision level. This research is focused on both investigating and developing feature- and decision-level data fusion for automated image analysis and classification. The common procedure for solving these problems can be described as: 1) process image for region of interest\u27 detection, 2) extract features from the region of interest and 3) create learning model based on the feature data. Image processing techniques were performed using edge detection, a histogram threshold and a color drop algorithm to determine the region of interest. The extracted features were low-level features, including textual, color and symmetrical features. For image analysis and classification, feature- and decision-level data fusion techniques are investigated for model learning using and integrating computational intelligence and machine learning techniques. These techniques include artificial neural networks, evolutionary algorithms, particle swarm optimization, decision tree, clustering algorithms, fuzzy logic inference, and voting algorithms. This work presents both the investigation and development of data fusion techniques for the application areas of dermoscopy skin lesion discrimination, content-based image retrieval, and graphic image type classification --Abstract, page v
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New Methods to Improve Large-Scale Microscopy Image Analysis with Prior Knowledge and Uncertainty
Multidimensional imaging techniques provide powerful ways to examine various kinds of scientific questions. The routinely produced data sets in the terabyte-range, however, can hardly be analyzed manually and require an extensive use of automated image analysis. The present work introduces a new concept for the estimation and propagation of uncertainty involved in image analysis operators and new segmentation algorithms that are suitable for terabyte-scale analyses of 3D+t microscopy images
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