6,441 research outputs found
GPIS: genetic programming based image segmentation with applications to biomedical object detection
Image segmentation plays a critical role in many image analysis applications. However, it is ill-defined in nature and remains one of the most intractable problems in image processing. In this thesis, we propose a genetic programming based algorithm for image segmentation (GPIS). Typically, genetic programming is a Darwinian-evolution inspired program discovery method and in the past it has been successfully used as an automatic programming tool. We make use of this property of GP to evolve efficient and accurate image segmentation programs from a pool of basic image analysis operators. In addition, we provide no a priori information about that nature of the images to the GP. The algorithm was tested on two separate medical image databases and results show the proposed GP's ability to adapt and produce short and accurate segmentation algorithms, irrespective of the database in use. We compared our results with a popular GA based image segmentation/classification system, GENIE Pro. We found that our proposed algorithm produced accurate image segmentations performed consistently on both databases and could possibly be extended to other image databases as a general-purpose image segmentation tool
Towards multiple 3D bone surface identification and reconstruction using few 2D X-ray images for intraoperative applications
This article discusses a possible method to use a small number, e.g. 5, of conventional 2D X-ray images to reconstruct multiple 3D bone surfaces intraoperatively. Each bone’s edge contours in X-ray images are automatically identified. Sparse 3D landmark points of each bone are automatically reconstructed by pairing the 2D X-ray images. The reconstructed landmark point distribution on a surface is approximately optimal covering main characteristics of the surface. A statistical shape model, dense point distribution model (DPDM), is then used to fit the reconstructed optimal landmarks vertices to reconstruct a full surface of each bone separately. The reconstructed surfaces can then be visualised and manipulated by surgeons or used by surgical robotic systems
A Survey on Evolutionary Computation for Computer Vision and Image Analysis: Past, Present, and Future Trends
Computer vision (CV) is a big and important field
in artificial intelligence covering a wide range of applications.
Image analysis is a major task in CV aiming to extract, analyse
and understand the visual content of images. However, imagerelated
tasks are very challenging due to many factors, e.g., high
variations across images, high dimensionality, domain expertise
requirement, and image distortions. Evolutionary computation
(EC) approaches have been widely used for image analysis with
significant achievement. However, there is no comprehensive
survey of existing EC approaches to image analysis. To fill
this gap, this paper provides a comprehensive survey covering
all essential EC approaches to important image analysis tasks
including edge detection, image segmentation, image feature
analysis, image classification, object detection, and others. This
survey aims to provide a better understanding of evolutionary
computer vision (ECV) by discussing the contributions of different
approaches and exploring how and why EC is used for
CV and image analysis. The applications, challenges, issues, and
trends associated to this research field are also discussed and
summarised to provide further guidelines and opportunities for
future research
Automated segmentation on the entire cardiac cycle using a deep learning work-flow
The segmentation of the left ventricle (LV) from CINE MRI images is essential
to infer important clinical parameters. Typically, machine learning algorithms
for automated LV segmentation use annotated contours from only two cardiac
phases, diastole, and systole. In this work, we present an analysis work-flow
for fully-automated LV segmentation that learns from images acquired through
the cardiac cycle. The workflow consists of three components: first, for each
image in the sequence, we perform an automated localization and subsequent
cropping of the bounding box containing the cardiac silhouette. Second, we
identify the LV contours using a Temporal Fully Convolutional Neural Network
(T-FCNN), which extends Fully Convolutional Neural Networks (FCNN) through a
recurrent mechanism enforcing temporal coherence across consecutive frames.
Finally, we further defined the boundaries using either one of two components:
fully-connected Conditional Random Fields (CRFs) with Gaussian edge potentials
and Semantic Flow. Our initial experiments suggest that significant improvement
in performance can potentially be achieved by using a recurrent neural network
component that explicitly learns cardiac motion patterns whilst performing LV
segmentation.Comment: 6 pages, 2 figures, published on IEEE Xplor
Active skeleton for bacteria modeling
The investigation of spatio-temporal dynamics of bacterial cells and their
molecular components requires automated image analysis tools to track cell
shape properties and molecular component locations inside the cells. In the
study of bacteria aging, the molecular components of interest are protein
aggregates accumulated near bacteria boundaries. This particular location makes
very ambiguous the correspondence between aggregates and cells, since computing
accurately bacteria boundaries in phase-contrast time-lapse imaging is a
challenging task. This paper proposes an active skeleton formulation for
bacteria modeling which provides several advantages: an easy computation of
shape properties (perimeter, length, thickness, orientation), an improved
boundary accuracy in noisy images, and a natural bacteria-centered coordinate
system that permits the intrinsic location of molecular components inside the
cell. Starting from an initial skeleton estimate, the medial axis of the
bacterium is obtained by minimizing an energy function which incorporates
bacteria shape constraints. Experimental results on biological images and
comparative evaluation of the performances validate the proposed approach for
modeling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the
proposed method can be found online at http://fluobactracker.inrialpes.fr.Comment: Published in Computer Methods in Biomechanics and Biomedical
Engineering: Imaging and Visualizationto appear i
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
Digital Image Processing Applications
Digital image processing can refer to a wide variety of techniques, concepts, and applications of different types of processing for different purposes. This book provides examples of digital image processing applications and presents recent research on processing concepts and techniques. Chapters cover such topics as image processing in medical physics, binarization, video processing, and more
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