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Analysis of Oriented Texture with application to the Detection of Architectural Distortion in Mammograms [electronic resource] / by Fábio J Ayres, Rangaraj M Rangayyan, J. E. Leo Desautels.

By: Ayres, Fábio J [author.].
Contributor(s): Rangayyan, Rangaraj M [author.] | Desautels, J. E. Leo [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Biomedical Engineering: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2011Edition: 1st ed. 2011.Description: XII, 150 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031016479.Subject(s): Engineering | Biophysics | Biomedical engineering | Technology and Engineering | Biophysics | Biomedical Engineering and BioengineeringAdditional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification: 620 Online resources: Click here to access online
Contents:
Detection of Oriented Features in Images -- Analysis of Oriented Patterns Using Phase Portraits -- Optimization Techniques -- Detection of Sites of Architectural Distortion in Mammograms.
In: Springer Nature eBookSummary: The presence of oriented features in images often conveys important information about the scene or the objects contained; the analysis of oriented patterns is an important task in the general framework of image understanding. As in many other applications of computer vision, the general framework for the understanding of oriented features in images can be divided into low- and high-level analysis. In the context of the study of oriented features, low-level analysis includes the detection of oriented features in images; a measure of the local magnitude and orientation of oriented features over the entire region of analysis in the image is called the orientation field. High-level analysis relates to the discovery of patterns in the orientation field, usually by associating the structure perceived in the orientation field with a geometrical model. This book presents an analysis of several important methods for the detection of oriented features in images, and a discussion of the phase portrait method for high-level analysis of orientation fields. In order to illustrate the concepts developed throughout the book, an application is presented of the phase portrait method to computer-aided detection of architectural distortion in mammograms. Table of Contents: Detection of Oriented Features in Images / Analysis of Oriented Patterns Using Phase Portraits / Optimization Techniques / Detection of Sites of Architectural Distortion in Mammograms.
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Detection of Oriented Features in Images -- Analysis of Oriented Patterns Using Phase Portraits -- Optimization Techniques -- Detection of Sites of Architectural Distortion in Mammograms.

The presence of oriented features in images often conveys important information about the scene or the objects contained; the analysis of oriented patterns is an important task in the general framework of image understanding. As in many other applications of computer vision, the general framework for the understanding of oriented features in images can be divided into low- and high-level analysis. In the context of the study of oriented features, low-level analysis includes the detection of oriented features in images; a measure of the local magnitude and orientation of oriented features over the entire region of analysis in the image is called the orientation field. High-level analysis relates to the discovery of patterns in the orientation field, usually by associating the structure perceived in the orientation field with a geometrical model. This book presents an analysis of several important methods for the detection of oriented features in images, and a discussion of the phase portrait method for high-level analysis of orientation fields. In order to illustrate the concepts developed throughout the book, an application is presented of the phase portrait method to computer-aided detection of architectural distortion in mammograms. Table of Contents: Detection of Oriented Features in Images / Analysis of Oriented Patterns Using Phase Portraits / Optimization Techniques / Detection of Sites of Architectural Distortion in Mammograms.

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