Geoff Dougherty · Cambridge University Press eBooks · 2018
Image processing is a hands-on discipline, and the best way to learn is by doing. This text takes its motivation from medical applications and uses real medical images and situations to illustrate and clarify concepts and to build intuition, insight and understanding. Designed for advanced undergraduates and graduate students who will become end-users of digital image processing, it covers the basics of the major clinical imaging modalities, explaining how the images are produced and acquired. It then presents the standard image processing operations, focusing on practical issues and problem solving. Crucially, the book explains when and why particular operations are done, and practical computer-based activities show how these operations affect real images. All images, links to the public-domain software ImageJ and custom plug-ins, and selected solutions are available from www.cambridge.org/books/dougherty.
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Brent Foster, Ulas Bagci N, Awais Mansoor et al. · 2013
This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues. Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier’s archiving and manuscript policies are encouraged to visit:
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Imran Uddin Afridi (6729776), Tariq Bashir (127259), Hasan Ali Khattak (6729779) et al. · Figshare · 2019
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Burnham, K., Haas, O.C.L., Bueno, Maria Gloria · The Open Collections (Coventry University) · 2004
In a method of segmenting an image a first, seed pixel unit is selected from a first group of pixel units in which the pixel units all have substantially the same grey-level intensity. The grey-level intensity of said first pixel unit is compared with the grey-level intensity of each of selected adjacent pixel units of said image and those pixel units with grey levels within a selected range are assigned as a pixel unit of the same region as said first pixel unit. This comparison process is repeated for each of the pixel units in the image, those already having been assigned being ignored. A further seed pixel unit is selected from a further group of pixel units in which the pixel units all have substantially the same grey-level intensity and the comparison process repeated for all of the unassigned pixel units.; Further seed pixel units are selected and the comparison process repeated until all the pixel units of the image have been assigned. A watershed transform is then applied to provide the segmented image.
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Stanislav Mikeš · Digital Repository (National Repository of Grey Literature) · 2010
Image segmentation is a fundamental part in low level computer vision processing. It has an essential influence on the subsequent higher level visual scene interpretation for a wide range of applications. Unsupervised image segmentation is an ill-defined problem and thus cannot be optimally solved in general. Several novel unsupervised multispectral image segmentation methods based on the underlaying random field texture models (GMRF, 2D/3D CAR) were developed. These segmenters use efficient data representations that allow an analytical solutions and thus the segmentation algorithm is much faster in comparison to methods based on MCMC. All segmenters were extensively compared with the alternative state- of-the-art segmenters with very good results. The MW3AR segmenter scored as one of the best available. The cluster validation problem was solved by a modified EM algorithm. Two multiple resolution segmenters were designed as a combination of a set of single segmenters. To tackle a realistic variable lighting in images, the illumination invariant features were derived and the illumination invariant segmenter was developed. For the proper evaluation of segmentation results and ranking of algorithms, a unique web-based texture segmentation benchmark was proposed and implemented. It was used for comprehensive...
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2008
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K. Mohaideen Abdul Kadhar, Gaurish Anand · Maker Innovations Series · 2024
An important aspect of vision systems is to differentiate between objects in a scene. The first step toward this goal is to segregate the various objects in a given image. This is accomplished by a process called segmentation wherein all pixels corresponding to various regions (objects) are grouped together thereby segregating the given image into its constituent objects. In the field of computer vision, image segmentation is an essential and fundamental process that forms the basis for many applications, including autonomous vehicles, medical imaging, object recognition, and more. Building on the knowledge gained in earlier chapters, this chapter explores the complex field of segmentation with a focus on advanced approaches and procedures.
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Burnham, K., Haas, O.C.L., Bueno, Maria Gloria · The Open Collections (Coventry University) · 2004
In a method of segmenting an image a first seed pixel unit is selected from a first group of pixel units in which the pixel units all have substantially the same grey-level intensity. The grey-level intensity of said first pixel unit is compared with the grey-level intensity of each of selected adjacent pixel units of said image and those pixel units with grey levels within a selected range arm assigned as a pixel unit of the same region as said first pixel unit This comparison process is repeated for each of the pixel units in the image, those already having been assigned being ignored. A further seed pixel unit is selected from a further group of pixel units in which the pixel units all have substantially the same grey-level intensity and the comparison process repeated for all of the unassigned pixel units. Further seed pixel units are selected and the comparison process repeated until all the pixel units of the image have been assigned. A watershed transform is then applied to provide the segmented image
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Mikeš, Stanislav · Digital Repository (National Repository of Grey Literature) · 2010
Image segmentation is a fundamental part in low level computer vision processing. It has an essential influence on the subsequent higher level visual scene interpretation for a wide range of applications. Unsupervised image segmentation is an ill-defined problem and thus cannot be optimally solved in general. Several novel unsupervised multispectral image segmentation methods based on the underlaying random field texture models (GMRF, 2D/3D CAR) were developed. These segmenters use efficient data representations that allow an analytical solutions and thus the segmentation algorithm is much faster in comparison to methods based on MCMC. All segmenters were extensively compared with the alternative state- of-the-art segmenters with very good results. The MW3AR segmenter scored as one of the best available. The cluster validation problem was solved by a modified EM algorithm. Two multiple resolution segmenters were designed as a combination of a set of single segmenters. To tackle a realistic variable lighting in images, the illumination invariant features were derived and the illumination invariant segmenter was developed. For the proper evaluation of segmentation results and ranking of algorithms, a unique web-based texture segmentation benchmark was proposed and implemented. It was used for comprehensive...
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S Esakkirajan, T Veerakumar, Badri Narayan Subudhi · 2025
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Benoît M. Dawant, Alex Zijdenbos · SPIE eBooks · 2010
Image segmentation, defined as the separation of the image into regions, is one of the first steps leading to image analysis and interpretation. The goal is to separate the image into regions that are meaningful for a specific task. This may, for instance, involve the detection of organs such as the heart, the liver, or the lungs from MR or CT images. Other applications may require the calculation of white and gray matter volumes in MR brain images, the labeling of deep brain structures such as the thalamus or the hippocampus, or quantitative measurements made from ultrasound images. Image segmentation approaches can be classified according to both the features and the type of technique used. Features include pixel intensities, gradient magnitudes, or measures of texture. Segmentation techniques applied to these features can be broadly classified into one of three groups [1]: region-based, edge-based, or classification. Typically, region-based and edge-based segmentation techniques exploit, respectively, within-region similarities and between-region differences between features, whereas a classification technique assigns class labels to individual pixels or voxels based on feature values. Because of issues such as spatial resolution, poor contrast, ill-defined boundaries, noise, or acquisition artifacts, segmentation is a difficult task and it is illusory to believe that it can be achieved by using gray-level information alone. A priori knowledge has to be used in the process, and so-called low-level processing algorithms have to cooperate with higher level techniques such as deformable and active models or atlas-based methods. These high-level techniques are described in great detail in Chapters 3 and 17 of this handbook and they will only be mentioned briefly in this chapter to provide the appropriate links. This chapter focuses on the segmentation of the image into regions based only on gray-level information. Methods relying on a single image, also called mono-modality methods, will be presented as well as multi-modal methods that take advantage of several co-registered images (see Chapter 8). Because image segmentation is a broad field that can only be touched upon in a single chapter, the reader will be introduced to basic segmentation methods. A number of pointers to the pertinent literature will be provided for more detailed descriptions of these methods or for more advanced techniques that build upon the concepts being introduced.
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Chris Solomon, Toby Breckon · 2010
This chapter contains sections titled: Image segmentation Use of image properties and features in segmentation Intensity thresholding Region growing and region splitting Split-and-merge algorithm The challenge of edge detection The Laplacian of Gaussian and difference of Gaussians filters The Canny edge detector Interest operators Watershed segmentation Segmentation functions Image segmentation with Markov random fields
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Rodrigo Rojas Moraleda, Nektarios A. Valous, Wei Xiong et al. · 2019
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Stanislav Mikeš · Digital Repository (National Repository of Grey Literature) · 2010
Image segmentation is a fundamental part in low level computer vision processing. It has an essential influence on the subsequent higher level visual scene interpretation for a wide range of applications. Unsupervised image segmentation is an ill-defined problem and thus cannot be optimally solved in general. Several novel unsupervised multispectral image segmentation methods based on the underlaying random field texture models (GMRF, 2D/3D CAR) were developed. These segmenters use efficient data representations that allow an analytical solutions and thus the segmentation algorithm is much faster in comparison to methods based on MCMC. All segmenters were extensively compared with the alternative state- of-the-art segmenters with very good results. The MW3AR segmenter scored as one of the best available. The cluster validation problem was solved by a modified EM algorithm. Two multiple resolution segmenters were designed as a combination of a set of single segmenters. To tackle a realistic variable lighting in images, the illumination invariant features were derived and the illumination invariant segmenter was developed. For the proper evaluation of segmentation results and ranking of algorithms, a unique web-based texture segmentation benchmark was proposed and implemented. It was used for comprehensive...
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Stanislav Mikeš · Digital Repository (National Repository of Grey Literature) · 2010
Image segmentation is a fundamental part in low level computer vision processing. It has an essential influence on the subsequent higher level visual scene interpretation for a wide range of applications. Unsupervised image segmentation is an ill-defined problem and thus cannot be optimally solved in general. Several novel unsupervised multispectral image segmentation methods based on the underlaying random field texture models (GMRF, 2D/3D CAR) were developed. These segmenters use efficient data representations that allow an analytical solutions and thus the segmentation algorithm is much faster in comparison to methods based on MCMC. All segmenters were extensively compared with the alternative state- of-the-art segmenters with very good results. The MW3AR segmenter scored as one of the best available. The cluster validation problem was solved by a modified EM algorithm. Two multiple resolution segmenters were designed as a combination of a set of single segmenters. To tackle a realistic variable lighting in images, the illumination invariant features were derived and the illumination invariant segmenter was developed. For the proper evaluation of segmentation results and ranking of algorithms, a unique web-based texture segmentation benchmark was proposed and implemented. It was used for comprehensive...
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Hall, Ernest L. · 2015
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Saïd Allili, Djemel Ziou · 2007
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Burnham, K., Haas, O.C.L., Bueno, Maria Gloria · The Open Collections (Coventry University) · 2003
In a method of segmenting an image a first, seed pixel unit is selected from a first group of pixel units in which the pixel units all have substantially the same grey-level intensity. The grey-level intensity of said first pixel unit is compared with the grey-level intensity of each of selected adjacent pixel units of said image and those pixel units with grey levels within a selected range are assigned as a pixel unit of the same region as said first pixel unit. This comparison process is repeated for each of the pixel units in the image, those already having been assigned being ignored. A further seed pixel unit is selected from a further group of pixel units in which the pixel units all have substantially the same grey-level intensity and the comparison process repeated for all of the unassigned pixel units.; Further seed pixel units are selected and the comparison process repeated until all the pixel units of the image have been assigned. A watershed transform is then applied to provide the segmented image.
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Hina J. Chokshi, Abhishek Agarwal · 2022
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John L. Semmlow, Benjamin Griffel · 2021
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