ORCID Profile
0000-0001-5943-1989
Current Organisation
CSIRO
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In Research Link Australia (RLA), "Research Topics" refer to ANZSRC FOR and SEO codes. These topics are either sourced from ANZSRC FOR and SEO codes listed in researchers' related grants or generated by a large language model (LLM) based on their publications.
Artificial Intelligence and Image Processing | Pattern Recognition and Data Mining | Computer Vision | Microelectromechanical Systems (MEMS) | Library and Information Studies | Environmental Monitoring | Information Retrieval and Web Search | Image Processing
Expanding Knowledge in Technology | Control of Pests, Diseases and Exotic Species at Regional or Larger Scales | Plant Production and Plant Primary Products not elsewhere classified | Aquaculture Rock Lobster | Fisheries - Aquaculture not elsewhere classified | Electronic Information Storage and Retrieval Services |
Publisher: Elsevier BV
Date: 04-2016
Publisher: Elsevier BV
Date: 11-2022
Publisher: Elsevier BV
Date: 03-2003
Publisher: IEEE
Date: 06-2018
Publisher: Academy and Industry Research Collaboration Center (AIRCC)
Date: 28-02-2015
Publisher: Hindawi Limited
Date: 07-08-2021
DOI: 10.1155/2021/5590180
Abstract: For the analysis of medical images, one of the most basic methods is to diagnose diseases by examining blood smears through a microscope to check the morphology, number, and ratio of red blood cells and white blood cells. Therefore, accurate segmentation of blood cell images is essential for cell counting and identification. The aim of this paper is to perform blood smear image segmentation by combining neural ordinary differential equations (NODEs) with U-Net networks to improve the accuracy of image segmentation. In order to study the effect of ODE-solve on the speed and accuracy of the network, the ODE-block module was added to the nine convolutional layers in the U-Net network. Firstly, blood cell images are preprocessed to enhance the contrast between the regions to be segmented secondly, the same dataset was used for the training set and testing set to test segmentation results. According to the experimental results, we select the location where the ordinary differential equation block (ODE-block) module is added, select the appropriate error tolerance, and balance the calculation time and the segmentation accuracy, in order to exert the best performance finally, the error tolerance of the ODE-block is adjusted to increase the network depth, and the training NODEs-UNet network model is used for cell image segmentation. Using our proposed network model to segment blood cell images in the testing set, it can achieve 95.3% pixel accuracy and 90.61% mean intersection over union. By comparing the U-Net and ResNet networks, the pixel accuracy of our network model is increased by 0.88% and 0.46%, respectively, and the mean intersection over union is increased by 2.18% and 1.13%, respectively. Our proposed network model improves the accuracy of blood cell image segmentation and reduces the computational cost of the network.
Publisher: Wiley
Date: 09-04-2015
DOI: 10.1111/JMI.12246
Abstract: Clusters or clumps of cells or nuclei are frequently observed in two dimensional images of thick tissue sections. Correct and accurate segmentation of overlapping cells and nuclei is important for many biological and biomedical applications. Many existing algorithms split clumps through the binarization of the input images therefore, the intensity information of the original image is lost during this process. In this paper, we present a curvature information, gray scale distance transform, and shortest path splitting line-based algorithm which can make full use of the concavity and image intensity information to find out markers, each of which represents an in idual object, and detect accurate splitting lines between objects using shortest path and junction adjustment. The proposed algorithm is tested on both synthetic and real nuclei images. Experiment results show that the performance of the proposed method is better than that of marker-controlled watershed method and ellipse fitting method.
Publisher: IEEE
Date: 09-2013
Publisher: Elsevier BV
Date: 04-2020
Publisher: IEEE
Date: 09-2019
Publisher: Frontiers Media SA
Date: 14-03-2019
Publisher: The Optical Society
Date: 17-05-2016
DOI: 10.1364/OE.24.011345
Publisher: IEEE
Date: 05-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: Springer International Publishing
Date: 2020
Publisher: IEEE Comput. Soc
Publisher: IEEE
Date: 2008
Publisher: Elsevier BV
Date: 10-2008
Publisher: SPIE
Date: 27-08-1992
DOI: 10.1117/12.135994
Publisher: Elsevier BV
Date: 02-2021
Publisher: Elsevier BV
Date: 06-2006
Publisher: SPIE
Date: 04-03-2010
DOI: 10.1117/12.844141
Publisher: IEEE
Date: 11-2021
Publisher: Elsevier BV
Date: 08-2015
Publisher: Elsevier BV
Date: 04-2022
Publisher: IEEE
Date: 08-2010
Publisher: Elsevier BV
Date: 11-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2019
Publisher: ACTA Press
Date: 2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 06-2014
Publisher: Academy and Industry Research Collaboration Center (AIRCC)
Date: 30-06-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 09-2018
Publisher: IEEE
Date: 10-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Publisher: International Union of Crystallography (IUCr)
Date: 29-10-2010
DOI: 10.1107/S0021889810040963
Abstract: The application of robotics to protein crystallization trials has resulted in the production of millions of images. Manual inspection of these images to find crystals and other interesting outcomes is a major rate-limiting step. As a result there has been intense activity in developing automated algorithms to analyse these images. The very first step for most systems that have been described in the literature is to delineate each droplet. Here, a novel approach that reaches over 97% success rate and subsecond processing times is presented. This will form the seed of a new high-throughput system to scrutinize massive crystallization c aigns automatically.
Publisher: Elsevier BV
Date: 10-2020
Publisher: SPIE-Intl Soc Optical Eng
Date: 2009
DOI: 10.1117/1.3059582
Publisher: IEEE
Date: 08-2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 11-2016
Publisher: IEEE
Date: 07-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: MDPI AG
Date: 06-04-2017
DOI: 10.3390/S17040785
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1997
DOI: 10.1109/34.574800
Publisher: Elsevier BV
Date: 11-2009
Publisher: Elsevier BV
Date: 10-2012
Publisher: Wiley
Date: 12-05-2016
DOI: 10.1111/JMI.12421
Abstract: In studies of germ cell transplantation, counting cells and measuring tubule diameters from different populations using labelled antibodies are important measurement processes. However, it is slow and sanity grinding to do these tasks manually. This paper proposes a way to accelerate these processes using a new image analysis framework based on several novel algorithms: centre points detection of tubules, tubule shape classification, skeleton-based polar-transformation, boundary weighting of polar-transformed image, and circular shortest path smoothing. The framework has been tested on a dataset consisting of 27 images which contain a total of 989 tubules. Experiments show that the detection results of our algorithm are very close to the results obtained manually and the novel approach can achieve a better performance than two existing methods.
Publisher: Elsevier BV
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Springer New York
Date: 2011
Publisher: IEEE
Date: 12-2016
Publisher: International Union of Crystallography (IUCr)
Date: 11-08-2016
DOI: 10.1107/S1600577516011498
Abstract: The quantification of micro-vasculatures is important for the analysis of angiogenesis on which the detection of tumor growth or hepatic fibrosis depends. Synchrotron-based X-ray computed micro-tomography (SR-µCT) allows rapid acquisition of micro-vasculature images at micrometer-scale spatial resolution. Through skeletonization, the statistical features of the micro-vasculature can be extracted from the skeleton of the micro-vasculatures. Thinning is a widely used algorithm to produce the vascular skeleton in medical research. Existing three-dimensional thinning methods normally emphasize the preservation of topological structure rather than geometrical features in generating the skeleton of a volumetric object. This results in three problems and limits the accuracy of the quantitative results related to the geometrical structure of the vasculature. The problems include the excessively shortened length of elongated objects, eliminated branches of blood vessel tree structure, and numerous noisy spurious branches. The inaccuracy of the skeleton directly introduces errors in the quantitative analysis, especially on the parameters concerning the vascular length and the counts of vessel segments and branching points. In this paper, a robust method using a consolidated end-point constraint for thinning, which generates geometry-preserving skeletons in addition to maintaining the topology of the vasculature, is presented. The improved skeleton can be used to produce more accurate quantitative results. Experimental results from high-resolution SR-µCT images show that the end-point constraint produced by the proposed method can significantly improve the accuracy of the skeleton obtained using the existing ITK three-dimensional thinning filter. The produced skeleton has laid the groundwork for accurate quantification of the angiogenesis. This is critical for the early detection of tumors and assessing anti-angiogenesis treatments.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: SPIE-Intl Soc Optical Eng
Date: 04-1997
DOI: 10.1117/1.601290
Publisher: Elsevier BV
Date: 09-2022
Publisher: Oxford University Press (OUP)
Date: 09-11-2011
DOI: 10.1017/S1431927611012128
Abstract: The detection of line-like features in images finds many applications in microanalysis. Actin fibers, microtubules, neurites, pilis, DNA, and other biological structures all come up as tenuous curved lines in microscopy images. A reliable tracing method that preserves the integrity and details of these structures is particularly important for quantitative analyses. We have developed a new image transform called the “Coalescing Shortest Path Image Transform” with very encouraging properties. Our scheme efficiently combines information from an extensive collection of shortest paths in the image to delineate even very weak linear features.
Publisher: Elsevier BV
Date: 09-2014
Publisher: Korean Society for Internet Information (KSII)
Date: 30-09-2018
Publisher: IEEE
Date: 09-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 12-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Springer Berlin Heidelberg
Date: 2013
Publisher: IEEE
Date: 06-2019
Publisher: IEEE
Date: 07-2017
Publisher: IEEE
Date: 11-2016
Publisher: IEEE
Date: 11-2016
Publisher: IEEE
Date: 12-2011
Publisher: Informa UK Limited
Date: 28-08-2015
Publisher: Elsevier BV
Date: 03-2024
Publisher: Springer Science and Business Media LLC
Date: 29-10-2020
Publisher: Fuji Technology Press Ltd.
Date: 20-12-2020
DOI: 10.20965/JACIII.2020.P0963
Abstract: In this paper, a segmentation method for cell images using Markov random field (MRF) based on a Chinese restaurant process model (CRPM) is proposed. Firstly, we carry out the preprocessing on the cell images, and then we focus on cell image segmentation using MRF based on a CRPM under a maximum a posteriori (MAP) criterion. The CRPM can be used to estimate the number of clusters in advance, adjusting the number of clusters automatically according to the size of the data. Finally, the conditional iteration mode (CIM) method is used to implement the MRF based cell image segmentation process. To validate our proposed method, segmentation experiments are performed on oral mucosal cell images. The segmentation results were compared with other methods, using precision, Dice, and mean square error (MSE) as the objective evaluation criteria. The experimental results show that our method produces accurate cell image segmentation results, and our method can effectively improve segmentation for the nucleus, binuclear cell, and micronucleus cell. This work will play an important role in cell image recognition and analysis.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Springer New York
Date: 2011
Publisher: Wiley
Date: 20-05-2013
DOI: 10.1111/JMI.12043
Abstract: Segmentation of nuclei from images of tissue sections is important for many biological and biomedical studies. Many existing image segmentation algorithms may lead to oversegmentation or undersegmentation for clustered nuclei images. In this paper, we proposed a new image segmentation algorithm based on concave curve expansion to correctly and accurately extract markers from the original images. Marker-controlled watershed is then used to segment the clustered nuclei. The algorithm was tested on both synthetic and real images and better results are achieved compared with some other state-of-the-art methods.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: IEEE
Date: 12-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2022
Publisher: Inderscience Publishers
Date: 2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Publisher: Springer International Publishing
Date: 2014
Publisher: Springer International Publishing
Date: 2021
Publisher: IEEE
Date: 12-2007
Publisher: Elsevier BV
Date: 08-2021
Publisher: IEEE Comput. Soc
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 12-2014
DOI: 10.1016/J.COMPMEDIMAG.2014.07.006
Abstract: Dendritic spines are tiny membranous protrusions from neuron's dendrites. They play a very important role in the nervous system. A number of mental diseases such as Alzheimer's disease and mental retardation are revealed to have close relations with spine morphologies or spine number changes. Spines have various shapes, and spine images are often not of good quality hence it is very challenging to detect spines in neuron images. This paper presents a novel pipeline to detect dendritic spines in 2D maximum intensity projection (MIP) images and a new dendrite backbone extraction method is developed in the pipeline. The strategy for the backbone extraction approach is that it iteratively refines the extraction result based on directional morphological filtering and improved Hessian filtering until a satisfactory extraction result is obtained. A shortest path method is applied along a backbone to extract the boundary of the dendrites. Spines are then segmented from the dendrites outside the extracted boundary. Touching spines will be split using a marker-controlled watershed algorithm. We present the results of our algorithm on real images and compare our algorithm with two other spine detection methods. The results show that the proposed approach can detect dendrites and spines more accurately. Measurements and classification of spines are also made in this paper.
Publisher: Frontiers Media SA
Date: 05-11-2015
Publisher: Wiley
Date: 24-08-2007
DOI: 10.1002/CYTO.A.20462
Abstract: Manual neuron tracing is a very labor-intensive task. In the drug screening context, the sheer number of images to process means that this approach is unrealistic. Moreover, the lack of reproducibility, objectivity, and auditing capability of manual tracing is limiting even in the context of smaller studies. We have developed fast, sensitive, and reliable algorithms for the purpose of detecting and analyzing neurites in cell cultures, and we have integrated them in software called HCA-Vision, suitable for the research environment. We validate the software on images of cortical neurons by comparing results obtained using HCA-Vision with those obtained using an established semi-automated tracing solution (NeuronJ). The effect of the Sez-6 deletion was characterized in detail. Sez-6 null neurons exhibited a significant increase in neurite branching, although the neurite field area was unchanged due to a reduction in mean branch length. HCA-Vision delivered considerable speed benefits and reliable traces.
Publisher: AIP
Date: 2007
DOI: 10.1063/1.2816642
Publisher: Wiley
Date: 11-2006
DOI: 10.1111/J.1365-2818.2006.01687.X
Abstract: Image mosaicing has found a number of applications such as panoramic imaging, digital terrain mapping, ophthalmology and virtual microscopy. In this study, we present an automated mosaicing technique for generating virtual slides from microscope images. We carried out robust image feature matching and global geometric and radiometric parameter estimation. The input images were transformed using the estimated geometric and radiometric parameters and mosaiced together, producing accurate registration of overlapping regions without visible seams.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2023
Publisher: IEEE
Date: 12-2010
Publisher: ACM
Date: 26-11-2012
Publisher: Academy & Industry Research Collaboration Center (AIRCC)
Date: 21-02-2014
Publisher: IEEE
Date: 12-2011
DOI: 10.1109/UCC.2011.64
Publisher: Elsevier BV
Date: 07-2020
Publisher: IGI Global
Date: 2016
DOI: 10.4018/978-1-4666-9435-4.CH003
Abstract: An approach is suggested for automating fish identification and measurement using stereo Baited Remote Underwater Video footage. Simple methods for identifying fish are not sufficient for measurement, since the snout and tail points must be found, and the stereo data should be incorporated to find a true measurement. We present a modular framework that ties together various approaches in order to develop a generalized system for automated fish detection and measurement. A method is also suggested for using machine learning to improve identification. Experimental results indicate the suitability of our approach.
Publisher: IEEE
Date: 2004
Publisher: Elsevier BV
Date: 04-2009
Publisher: AIP
Date: 2011
DOI: 10.1063/1.3596624
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2021
Publisher: Springer Science and Business Media LLC
Date: 28-03-2012
Publisher: ACM
Date: 26-11-2012
Publisher: IEEE
Date: 2005
Publisher: ACM
Date: 17-10-2021
Publisher: Elsevier BV
Date: 12-2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: AIP
Date: 2013
DOI: 10.1063/1.4824987
Publisher: Elsevier BV
Date: 03-2016
Publisher: Elsevier BV
Date: 04-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2001
DOI: 10.1109/30.964101
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Elsevier BV
Date: 11-1998
DOI: 10.1016/S0895-6111(98)00051-2
Abstract: We present a knowledge-based approach to segmentation and analysis of the lung boundaries in chest X-rays. Image edges are matched to an anatomical model of the lung boundary using parametric features. A modular system architecture was developed which incorporates the model, image processing routines, an inference engine and a blackboard. Edges associated with the lung boundary are automatically identified and abnormal features are reported. In preliminary testing on 14 images for a set of 18 detectable abnormalities, the system showed a sensitivity of 88% and a specificity of 95% when compared with assessment by an experienced radiologist.
Publisher: ACM
Date: 26-11-2012
Publisher: IEEE
Date: 12-2015
DOI: 10.1109/CICN.2015.66
Publisher: Elsevier BV
Date: 11-2003
Publisher: Elsevier BV
Date: 04-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2004
DOI: 10.1109/TSMCB.2003.816997
Abstract: This paper presents a fast panoramic stereo matching algorithm using a cylindrical maximum surface technique. The disparity for a pair of panoramic images is found in a cylindrical shaped correlation coefficient volume by obtaining the maximum surface rather than simply choosing a position that gives the maximum correlation coefficient value. The use of our cylindrical maximum surface technique ensures that the disparities obtained at the left and the right columns of the panoramic stereo images are properly constrained. Typical running time for a pair of 1324 x 120 images is about 0.33 s on a 1.7-GHz PC. A variety of real images have been tested, and good results have been obtained.
Publisher: Elsevier BV
Date: 10-2014
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2019
Publisher: Elsevier BV
Date: 09-2007
Publisher: IEEE
Date: 12-2010
Publisher: Elsevier BV
Date: 05-2016
Publisher: Frontiers Media SA
Date: 23-12-2020
DOI: 10.3389/FBIOE.2020.605132
Abstract: Automatic extraction of liver and tumor from CT volumes is a challenging task due to their heterogeneous and diffusive shapes. Recently, 2D deep convolutional neural networks have become popular in medical image segmentation tasks because of the utilization of large labeled datasets to learn hierarchical features. However, few studies investigate 3D networks for liver tumor segmentation. In this paper, we propose a 3D hybrid residual attention-aware segmentation method, i.e., RA-UNet, to precisely extract the liver region and segment tumors from the liver. The proposed network has a basic architecture as U-Net which extracts contextual information combining low-level feature maps with high-level ones. Attention residual modules are integrated so that the attention-aware features change adaptively. This is the first work that an attention residual mechanism is used to segment tumors from 3D medical volumetric images. We evaluated our framework on the public MICCAI 2017 Liver Tumor Segmentation dataset and tested the generalization on the 3DIRCADb dataset. The experiments show that our architecture obtains competitive results.
Publisher: Elsevier BV
Date: 08-2015
DOI: 10.1016/J.COMPBIOMED.2014.10.008
Abstract: A shortest path-based algorithm is proposed in this paper to find splitting lines for touching cell nuclei. First, an initial splitting line is obtained through the distance transform of a marker image and the watershed algorithm. The initial splitting line is then separated into different line segments as necessary, and the endpoint positions of these line segments are adjusted to the concave points on the contour. Finally, a shortest path algorithm is used to find the accurate splitting line between the starting-point and the end-point, and the final split can be achieved by the contour of the touching cell nuclei and the splitting lines. Comparisons of experimental results show that the proposed algorithm is effective for segmentation of different types of touching cell nuclei.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2017
Publisher: IEEE
Date: 10-2019
Publisher: AIP
Date: 2013
DOI: 10.1063/1.4824997
Publisher: Wiley
Date: 20-08-2013
DOI: 10.1111/JMI.12076
Abstract: Micronucleus assays are extensively used by biologists to assess genotoxicity and to monitor human exposure to genotoxic materials. As recent studies suggested that nuclear buds can be a new source of micronuclei formed in interphase, the quantification of nuclear buds, which are micronucleus like objects that are attached to the nuclei in interphase, in normal and control group is needed. Three automatic nuclear bud detection algorithms fit for different situations are proposed in this paper. One is based on ellipse fitting, one is based on a stick model and the other is based on the top-hat transform. Comparison of the three methods is also given in this paper. Experimental results showed that the proposed algorithms are all effective and efficient for nuclear bud detection.
Publisher: IEEE
Date: 11-2009
Publisher: Springer Science and Business Media LLC
Date: 02-03-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2005
Publisher: Springer International Publishing
Date: 2022
Publisher: Springer International Publishing
Date: 2019
Publisher: Cambridge University Press (CUP)
Date: 09-2000
DOI: 10.1017/S0263574799002520
Abstract: This paper introduces a simple method for obtaining the pure 3-D translational motion parameter (without rotation) and the translation parameter with a known rotation of a camera. Once the translation vector is available, the 3-D structure of an object can also be obtained. The minimum number of matching points in the two images can be as few as two, and the equations to be solved are linear. Synthetic and real image tests has been performed, and good results have been obtained.
Publisher: Elsevier BV
Date: 02-2016
Publisher: IEEE
Date: 08-2012
Publisher: IEEE
Date: 12-2012
Publisher: Elsevier BV
Date: 08-2022
Publisher: Wiley
Date: 22-04-2009
DOI: 10.1111/J.1365-2818.2009.03156.X
Abstract: The capacity to detect linear features is central to image analysis, computer vision and pattern recognition and has practical applications in areas such as neurite outgrowth detection, retinal vessel extraction, skin hair removal, plant root analysis and road detection. Linear feature detection often represents the starting point for image segmentation and image interpretation. In this paper, we present a new algorithm for linear feature detection using multiple directional non-maximum suppression with symmetry checking and gap linking. Given its low computational complexity, the algorithm is very fast. We show in several ex les that it performs very well in terms of both sensitivity and continuity of detected linear features.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: IEEE
Date: 2006
Publisher: Elsevier BV
Date: 11-2014
Publisher: Elsevier BV
Date: 09-2008
Publisher: Elsevier BV
Date: 03-2003
Publisher: Springer International Publishing
Date: 13-10-2015
DOI: 10.1007/978-3-319-10984-8_10
Abstract: This chapter presents an approach to processing ultra high-resolution, large-size biomedical imaging data for the purposes of detecting and quantifying vasculature and microvasculature . Capturing early signs of any changes in vasculature may have significant values for early-diagnosis and treatment assessment due to the well understood observation that vascular changes precede cancerous growth and metastasis metastasis . With the advent of key enabling technologies for extremely high-resolution imaging, such as synchrotron radiation synchrotron radiation based computed tomography (CT) computed tomography , the required levels of detail have become accessible. However, these technologies also present challenges in data analysis. This chapter aims to offer some insights as to how these changes might be best dealt with. We argue that the necessary steps in quantitative understanding of vasculatures include targeted data enhancement enhancement , information reduction aimed at characterizing the linear structure of vessels vessels , and quantitatively describing the vessel hierarchy. We present results on cerebral and liver vasculatures of a mouse captured at the Shanghai Synchrotron Radiation Facility (SSRF). These results were achieved with a processing pipeline comprising of our empirically selected component for each of the above steps. Towards the end, we discuss how alternative and additional components may be incorporated for improved speed and robustness.
Publisher: ACM
Date: 26-11-2012
Publisher: SPIE-Intl Soc Optical Eng
Date: 04-2001
DOI: 10.1117/1.1344186
Publisher: Springer International Publishing
Date: 2014
Publisher: Elsevier BV
Date: 10-2010
Abstract: Automating the analysis of neurons in culture represents a key aspect of the search for neuroactive compounds. A number of commercial neurite analysis software packages tend to measure some basic features such as total neurite length and number of branching points. However, with only these measurements, some differences between neurite morphologies that are clear to a human observer cannot be identified. The authors have developed a suite of image analysis tools that will allow researchers to produce quality analyses at primary screening rates. The suite provides sensitive and information-rich measurements of neurons and neurites. It can discriminate subtle changes in complex neurite arborization even when neurons and neurites are dense. This allows users to selectively screen for compounds triggering different types of neurite outgrowth behavior. In mixed cell populations, neurons can be filtered and separated from other brain cell types so that neurite analysis can be performed only on neurons. It supports batch processing with a built-in database to store the batch-processing results, a batch result viewer, and an ad hoc query builder for users to retrieve features of interest. The suite of tools has been deployed into a software package called HCA-Vision. The free version of the software package is available at www.hca-vision.com.
Start Date: 06-2018
End Date: 06-2023
Amount: $480,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 2014
End Date: 12-2018
Amount: $405,000.00
Funder: Australian Research Council
View Funded ActivityStart Date: 03-2019
End Date: 03-2024
Amount: $5,000,000.00
Funder: Australian Research Council
View Funded Activity