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A segmentation procedure of the pervious area of the aorta artery from CT images without contrast medium

MATHEMATICAL METHODS IN THE APPLIED SCIENCES 2020年第1期43卷 114-133页

作者： Costarelli, Danilo Seracini, Marco Vinti, Gianluca Univ Perugia Dept Math & Comp Sci 1 Via Vanvitelli I-06123 Perugia Italy

In this paper, we develop an algorithm for the segmentation of the pervious lumen of the aorta artery in computed tomography (CT) images without contrast medium, a challenging task due to the closeness gray levels of the different zones to segment. The novel approach of the proposed procedure mainly resides in enhancing the resolution of the image by the application of the algorithm deduced from the mathematical theory of sampling Kantorovich operators. After the application of suitable digital image processing techniques, the pervious zone of the artery can be distinguished from the occluded one. Numerical tests have been performed using 233 CT images, and suitable numerical errors have been computed and introduced ex novo to evaluate the performance of the proposed method. The above procedure is completely automatic in all its parts after the initial region of interest (ROI) selection. The main advantages of this approach relies in the potential possibility of performing diagnosis concerning vascular pathologies even for patients with severe kidney diseases or allergic problems, for which CT images with contrast medium cannot be achieved.

关键词： aorta artery biomedical image CT image equalization image processing mathematical procedure normalization sampling Kantorovich operator segmentation algorithm vascular apparatus

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IMAGE segmentation FOR COMPRESSION OF IMAGES AND IMAGE SEQUENCES

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IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING 1995年第1期142卷 15-21页

作者： SANDERSON, H CREBBIN, G UNIV WESTERN AUSTRALIA DEPT ELECT & ELECTR ENGNNEDLANDSWA 6009AUSTRALIA

Region-based image coding schemes, the so-called second generation techniques, have gained much favour in recent years. For still picture coding, they can increase the compression ratio obtained by transform coding by an order of magnitude, while maintaining adequate image representation. The success of these techniques relies on the ability to describe regions in an image succinctly by their shape and size, The algorithms presented describe methods for segmenting images. Unlike most other region based algorithms, these algorithms incorporate knowledge of the border coding process in deciding how to partition the image. The extension from single image compression to sequential image compression is also considered. A new, efficient segmentation scheme is proposed that exploits temporal redundancies between successive images, and reduces some problems associated with error accumulation in error images.

关键词： IMAGE PROCESSING segmentation algorithm

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A concept for movement-based computerized segmentation of connective tissue in ultrasound imaging

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第26期81卷 38053-38066页

作者： Ramakrishnan, Ananth Hari Rajappa, Muthaiah Krithivasan, Kannan Chatzistergos, Panagiotis E. Chockalingam, Nachiappan Nalluri, Madhusudhana Rao SASTRA Deemed Univ Sch Comp Comp Vis & Soft Comp Lab Thanjavur 613401 India Staffordshire Univ Ctr Biomech & Rehabil Technol Sch Hlth Sci & Wellbeing Stoke On Trent ST4 2DE Staffs England Amrita Vishwa Vidyapeetham Dept Comp Sci & Engn Amrita Sch Engn Coimbatore 641112 Tamil Nadu India

This study proposes a novel concept for the computerized segmentation of ultrasound images of connective tissue based on movement. Tendons and ligaments are capable of almost frictionless movement relative to their neighbouring tissues making them good candidates for movement-based segmentation. To demonstrate this concept, a central cross section of the patellar tendon was imaged in the axial plane while movement was generated by manually pulling and pushing the skin close to the imaging area. Maps of internal movement were created for four representative pairs of consecutive images using normalised cross corelation. Thresholding followed by a series of morphological operations (k-clustering, blob extraction, curve fitting) enabled the extraction of the superficial-most tendon boundary. Comparison against manually segmented outputs indicated good agreement against ground truth (average +/- STDEV Bhattacharyya distance: 0.170 +/- 0.039). In contrast to the more superficial parts of the tissue, the applied method for motion generation did not result in clearly visible movement in the tissue areas deeper in the imaging window. The segmentation of the entire tendon will require movement patterns that involve equally the entire tendon (e.g., generated by a contraction of the in-series muscle). The results of this study demonstrate for the first time that movement mapping can be used for the segmentation of connective tissue. Further research will be needed to identify the optimal way to use motion to complement existing segmentation approaches which are based on signal intensity, texture, and shape features.

关键词： Tendon Ligament Connective tissue segmentation algorithm Normalized cross correlation Displacement map

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Impact of image segmentation on high-content screening data quality for SK-BR-3 cells

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BMC BIOINFORMATICS 2007年第1期8卷 1-13页

作者： Hill, Andrew A. LaPan, Peter Li, Yizheng Haney, Steve Wyeth Ayerst Res Dept Biol Technol Cambridge MA 02140 USA

Background: High content screening (HCS) is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses automated microscopy to collect images of cultured cells. The images are subjected to segmentation algorithms to identify cellular structures and quantitate their morphology, for hundreds to millions of individual cells. However, image analysis may be imperfect, especially for "HCS-unfriendly" cell lines whose morphology is not well handled by current image segmentation algorithms. We asked if segmentation errors were common for a clinically relevant cell line, if such errors had measurable effects on the data, and if HCS data could be improved by automated identification of well-segmented cells. Results: Cases of poor cell body segmentation occurred frequently for the SK-BR-3 cell line. We trained classifiers to identify SK-BR-3 cells that were well segmented. On an independent test set created by human review of cell images, our optimal support-vector machine classifier identified well-segmented cells with 81% accuracy. The dose responses of morphological features were measurably different in well- and poorly-segmented populations. Elimination of the poorly-segmented cell population increased the purity of DNA content distributions, while appropriately retaining biological heterogeneity, and simultaneously increasing our ability to resolve specific morphological changes in perturbed cells. Conclusion: Image segmentation has a measurable impact on HCS data. The application of a multivariate shape-based filter to identify well-segmented cells improved HCS data quality for an HCS-unfriendly cell line, and could be a valuable post-processing step for some HCS datasets.

关键词： Texture Feature segmentation algorithm Segmented Object Image segmentation algorithm Segmented Cell

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Optimal approach to SAR image segmentation and classification

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IEE PROCEEDINGS-RADAR SONAR AND NAVIGATION 2000年第3期147卷 134-142页

作者： Stewart, D Blacknell, D Blake, A Cook, R Oliver, C NA Software Ltd Liverpool Merseyside England DERA Malvern WR14 3PS Worcs England

Many algorithms have been proposed for the segmentation and classification of SAR imagery. Typically these are heuristic in basis, and more successful on some types of imagery than others. Methods based on global optimisation techniques can achieve the optimal solution of the posed problem;the algorithm is then characterised by an objective function and the chosen optimisation technique. The authors construct an appropriate objective function for SAR segmentation or classification, and show how to optimally quantify the relationship between the competing terms within it. This removes the previous need for the introduction of heuristics. Also described is an implementation based on simulated annealing, in which considerable attention has been paid to details, and examples are given of the resulting algorithm's performance upon real data.

关键词： RCS reconstruction SAR imagery image classification Radar theory SAR image classification Optimisation techniques real data heuristics segmentation algorithm objective function image segmentation optimal approach optimal solution algorithm performance radar cross-section simulated annealing global optimisation techniques Optical, image and video signal processing radar imaging synthetic aperture radar image reconstruction SAR image segmentation

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A Laser Scanner Point Cloud Registration Method Using Difference of Normals (DoN) Based segmentation

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LASERS IN ENGINEERING 2022年第1-2期53卷 87-103页

作者： Lyu, J-H Wang, Z-W Zhou, Y. Meng, J. Wang, S-F Changchun Univ Sci & Technol Natl Demonstrat Ctr Expt Optoelect Engn Educ Sch Optoelect Engn 7089 Weixing Rd Changchun 130022 Jilin Peoples R China Changchun Univ Sci & Technol Key Lab Optoelect Measurement & Opt Informat Tran Minist Educ Sch Optoelect Engn 7089 Weixing Rd Changchun 130022 Jilin Peoples R China

The coarse registration is required before fine registration to obtain a better initial pose;however, it is difficult for coarse registration to obtain a better initial pose when the overlapping coefficient of source and target point cloud is low. To overcome this we propose a registration method using difference of normals (DoN) based segmentation algorithm and sample consensus initial alignment algorithm. First, the DoN based segmentation algorithm is employed to segment areas where obvious normal differences from source and target point cloud. Afterwards, Gaussian models for these subsets are established to find a pair of point cloud subsets with the most similar distribution. Then the sample consensus initial alignment algorithm (SAC-IA) is employed to register the matched subsets to obtain transformations between them, applied to source point cloud to find a better initial pose. Eventually, the iterative closest point (ICP) algorithm is involved to complete the registration. The experiments show the root mean square error (RMSE) of the registered point cloud using proposed method has been improved by 0.435 m compared with that of previous algorithm when the overlapping coefficient is 5%. The analysis shows the segmented region has clearer normal features;consequently, the SAC-IA algorithm to extract a more effective feature histogram to obtain a more accurate transformation.

关键词： Laser scanner point cloud registration iterative closest point (ICP) algorithm difference of normals (DoN) segmentation algorithm sample consensus initial alignment algorithm (SAC-IA)

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Detection and segmentation of drusen deposits on human retina: Potential in the diagnosis of age-related macular degeneration

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MEDICAL IMAGE ANALYSIS 2003年第1期7卷 95-108页

作者： Rapantzikos, K Zervakis, M Balas, K Tech Univ Crete Dept Elect Comp Engn Digital Image & Signal Proc Lab GR-73100 Iraklion Greece

Assessment of the risk for the development of age-related macular degeneration requires reliable detection and quantitative mapping of retinal abnormalities that are considered as precursors of the disease. Typical signs for the latter are the so-called drusen that appear as abnormal white-yellow deposits on the retina. segmentation of these features using conventional image analysis methods is quite complicated mainly due to the non-uniform illumination and the variability of the pigmentation of the background tissue. This paper presents a novel segmentation algorithm for the automatic detection and mapping of drusen in retina images acquired with the aid of a digital Fundus camera. We employ a modified adaptive histogram equalization, namely the multilevel histogram equalization (MLE) scheme, for enhancing local intensity structures. For the detection of drusen in retina images, we develop a novel segmentation technique, the histogram-based adaptive local thresholding (HALT), which extracts the useful information from an image without being affected by the presence of other structures. We provide experimental results from the application of our technique to real images, where certain abnormalities (drusen) have slightly different characteristics from the background. The performance of the, algorithm is established through statistical analysis of the results. This analysis indicates that the proposed drusen detector gives reliable detection accuracy in both position and mass size. (C) 2002 Elsevier Science B.V. All rights reserved.

关键词： age-related macular degeneration drusen segmentation algorithm

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Dataset of Magnetic Resonance Images of Nonepileptic Subjects and Temporal Lobe Epilepsy Patients for Validation of Hippocampal segmentation Techniques

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NEUROINFORMATICS 2011年第4期9卷 335-346页

作者： Jafari-Khouzani, Kourosh Elisevich, Kost V. Patel, Suresh Soltanian-Zadeh, Hamid Henry Ford Hosp Dept Diagnost Radiol Detroit MI 48202 USA Henry Ford Hosp Dept Neurosurg Detroit MI 48202 USA Univ Tehran Control & Intelligent Proc Ctr Excellence Elect & Comp Engn Dept Tehran 14395515 Iran

The hippocampus has become the focus of research in several neurodegenerative disorders. Automatic segmentation of this structure from magnetic resonance (MR) imaging scans of the brain facilitates this work. segmentation techniques must be evaluated using a dataset of MR images with accurate hippocampal outlines generated manually. Manual segmentation is not a trivial task. Lack of a unique segmentation protocol and poor image quality are only two factors that have confounded the consistency required for comparative study. We have developed a publicly available dataset of T1-weighted (T1W) MR images of epileptic and nonepileptic subjects along with their hippocampal outlines to provide a means of evaluation of segmentation techniques. This dataset contains 50 T1W MR images, 40 epileptic and ten nonepileptic. All images were manually segmented by a widely used protocol. Twenty five images were selected for training and were provided with hippocampal labels. Twenty five other images were provided without labels for testing algorithms. The users are allowed to evaluate their generated labels for the test images using 11 segmentation similarity metrics. Using this dataset, we evaluated two segmentation algorithms, Brain Parser and Classifier Fusion and Labeling (CFL), trained by the training set. For Brain Parser, an average Dice coefficient of 0.64 was obtained with the testing set. For CFL, this value was 0.75. Such findings indicate a need for further improvement of segmentation algorithms in order to enhance reliability.

关键词： segmentation algorithm Hippocampus Magnetic resonance imaging Validation

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NetMets: software for quantifying and visualizing errors in biological network segmentation

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BMC BIOINFORMATICS 2012年第sup8期13卷 1-19页

作者： Mayerich, David Bjornsson, Chris Taylor, Jonathan Roysam, Badrinath Univ Illinois Beckman Inst Adv Sci & Technol Urbana IL 61801 USA Rensselaer Polytech Inst Ctr Biotechnol & Interdisciplinary Studies New York NY USA Univ Houston Dept Elect & Comp Engn Houston TX 77004 USA

One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization.

关键词： Mean Square Error Ground Truth segmentation algorithm Interconnected Network Hausdorff Distance

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segmentation of temperature microstructure

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JOURNAL OF GEOPHYSICAL RESEARCH-OCEANS 2002年第C12期107卷 4-1-4-13页

作者： Chen, HL Hondzo, M Rao, AR Lan Yang Inst Technol Dept Environm Engn Toucheng 26141 Ileng Taiwan Univ Minnesota St Anthony Falls Lab Dept Civil Engn Minneapolis MN 55414 USA Purdue Univ Sch Civil Engn W Lafayette IN 47907 USA

[1] The so-called indirect method of estimating the kinetic energy dissipation rate, a key parameter in environmental fluid mechanics, involves fitting the observed spectrum to the theoretical Batchelor spectrum. This requires the statistically nonstationary temperature gradient profiles to be split onto statistically stationary segments. This comparative study of segmentation algorithms uses synthetic temperature gradient series and the temperature gradient profiles that are measured in an inland lake. The results of segmentation based on autoregressive (AR) models and wavelet analysis are compared. The bias in estimating the Batchelor spectrum from nonstationary segments, which have changes in the spectral shape or the spectral magnitude, is demonstrated. The estimated spectra of the resulting stationary segments should be stationary in both the shape and the magnitude of the spectra. A modified AR-based test and a proposed wavelet-based test are sensitive to changes in both the spectral shape and the spectral magnitude. An empirical segmentation technique sensitive to changes in the spectral shape only is not recommended for use.

关键词： batchelor spectrum segmentation algorithm energy dissipation

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