A Novel Segmentation Approach Combining Region- and Edge-Based Information for Ultrasound Images PDF Download
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Author: YaozhongLuo
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 18
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Book Description
Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality.
Author: YaozhongLuo
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 18
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Book Description
Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications. However, ultrasound (US) image segmentation, which is the essential process for further analysis, is a challenging task due to the poor image quality.
Author: Mehmet Kemal Kocamaz
Publisher:
ISBN:
Category :
Languages : en
Pages : 26
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Book Description
Author: Yaozhong Luo
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 19
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Book Description
Ultrasound imaging has become one of the most popular medical imaging modalities with numerous diagnostic applications.
Author: Zhujiang Cao
Publisher:
ISBN:
Category : Imaging systems in medicine
Languages : en
Pages : 278
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Book Description
Author: S. Kamaledin Setarehdan
Publisher: Springer Science & Business Media
ISBN: 0857293338
Category : Computers
Languages : en
Pages : 661
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Book Description
Medical imaging is an important topic and plays a key role in robust diagnosis and patient care. It has experienced an explosive growth over the last few years due to imaging modalities such as X-rays, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasound. This book focuses primarily on model-based segmentation techniques, which are applied to cardiac, brain, breast and microscopic cancer cell imaging. It includes contributions from authors working in industry and academia, and presents new material.
Author: Marek Gzik
Publisher: Springer Nature
ISBN: 3030991121
Category : Technology & Engineering
Languages : en
Pages : 341
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Book Description
This book presents the latest developments in the field of biomedical engineering and includes practical solutions and strictly scientific considerations. The development of new methods of treatment, advanced diagnostics or personalized rehabilitation requires close cooperation of experts from many fields, including, among others, medicine, biotechnology and finally biomedical engineering. The latter, combining many fields of science, such as computer science, materials science, biomechanics, electronics not only enables the development and production of modern medical equipment, but also participates in the development of new directions and methods of treatment. The presented monograph is a collection of scientific papers on the use of engineering methods in medicine. The topics of the work include both practical solutions and strictly scientific considerations expanding knowledge about the functioning of the human body. We believe that the presented works will have an impact on the development of the field of science, which is biomedical engineering, constituting a contribution to the discussion on the directions of development of cooperation between doctors, physiotherapists and engineers. We would also like to thank all the people who contributed to the creation of this monograph—both the authors of all the works and those involved in technical works.
![A New Segmentation Algorithm for Prostate Boundary Detection in 2D Ultrasound Images [electronic Resource] PDF](https://books.google.com/books/content/images/frontcover/cuPiwAEACAAJ?fife=w80-h100)
Author: Chiu, Bernard
Publisher: University of Waterloo
ISBN:
Category :
Languages : en
Pages :
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Book Description
Prostate segmentation is a required step in determining the volume of a prostate, which is very important in the diagnosis and the treatment of prostate cancer. In the past, radiologists manually segment the two-dimensional cross-sectional ultrasound images. Typically, it is necessary for them to outline at least a hundred of cross-sectional images in order to get an accurate estimate of the prostate℗s volume. This approach is very time-consuming. To be more efficient in accomplishing this task, an automated procedure has to be developed. However, because of the quality of the ultrasound image, it is very difficult to develop a computerized method for defining boundary of an object in an ultrasound image. The goal of this thesis is to find an automated segmentation algorithm for detecting the boundary of the prostate in ultrasound images. As the first step in this endeavour, a semi-automatic segmentation method is designed. This method is only semi-automatic because it requires the user to enter four initialization points, which are the data required in defining the initial contour. The discrete dynamic contour (DDC) algorithm is then used to automatically update the contour. The DDC model is made up of a set of connected vertices. When provided with an energy field that describes the features of the ultrasound image, the model automatically adjusts the vertices of the contour to attain a maximum energy. In the proposed algorithm, Mallat℗s dyadic wavelet transform is used to determine the energy field. Using the dyadic wavelet transform, approximate coefficients and detailed coefficients at different scales can be generated. In particular, the two sets of detailed coefficients represent the gradient of the smoothed ultrasound image. Since the gradient modulus is high at the locations where edge features appear, it is assigned to be the energy field used to drive the DDC model. The ultimate goal of this work is to develop a fully-automatic segmentation algorithm. Since only the initialization stage requires human supervision in the proposed semi-automatic initialization algorithm, the task of developing a fully-automatic segmentation algorithm is reduced to designing a fully-automatic initialization process. Such a process is introduced in this thesis. In this work, the contours defined by the semi-automatic and the fully-automatic segmentation algorithm are compared with the boundary outlined by an expert observer. Tested using 8 sample images, the mean absolute difference between the semi-automatically defined and the manually outlined boundary is less than 2.5 pixels, and that between the fully-automatically defined and the manually outlined boundary is less than 4 pixels. Automated segmentation tools that achieve this level of accuracy would be very useful in assisting radiologists to accomplish the task of segmenting prostate boundary much more efficiently.
Author: Ayman S. El-Baz
Publisher: Springer Science & Business Media
ISBN: 1441982043
Category : Medical
Languages : en
Pages : 369
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Book Description
With the advances in image guided surgery for cancer treatment, the role of image segmentation and registration has become very critical. The central engine of any image guided surgery product is its ability to quantify the organ or segment the organ whether it is a magnetic resonance imaging (MRI) and computed tomography (CT), X-ray, PET, SPECT, Ultrasound, and Molecular imaging modality. Sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures present in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning system designs. The focus of this book in towards the state of the art techniques in the area of image segmentation and registration.
![A New Segmentation Algorithm for Prostate Boundary Detection in 2D Ultrasound Images [electronic Resource] PDF](https://books.google.com/books/content/images/frontcover/8q1TAQAACAAJ?fife=w80-h100)
Author: Chiu, Bernard
Publisher: University of Waterloo
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Prostate segmentation is a required step in determining the volume of a prostate, which is very important in the diagnosis and the treatment of prostate cancer. In the past, radiologists manually segment the two-dimensional cross-sectional ultrasound images. Typically, it is necessary for them to outline at least a hundred of cross-sectional images in order to get an accurate estimate of the prostate℗s volume. This approach is very time-consuming. To be more efficient in accomplishing this task, an automated procedure has to be developed. However, because of the quality of the ultrasound image, it is very difficult to develop a computerized method for defining boundary of an object in an ultrasound image. The goal of this thesis is to find an automated segmentation algorithm for detecting the boundary of the prostate in ultrasound images. As the first step in this endeavour, a semi-automatic segmentation method is designed. This method is only semi-automatic because it requires the user to enter four initialization points, which are the data required in defining the initial contour. The discrete dynamic contour (DDC) algorithm is then used to automatically update the contour. The DDC model is made up of a set of connected vertices. When provided with an energy field that describes the features of the ultrasound image, the model automatically adjusts the vertices of the contour to attain a maximum energy. In the proposed algorithm, Mallat℗s dyadic wavelet transform is used to determine the energy field. Using the dyadic wavelet transform, approximate coefficients and detailed coefficients at different scales can be generated. In particular, the two sets of detailed coefficients represent the gradient of the smoothed ultrasound image. Since the gradient modulus is high at the locations where edge features appear, it is assigned to be the energy field used to drive the DDC model. The ultimate goal of this work is to develop a fully-automatic segmentation algorithm. Since only the initialization stage requires human supervision in the proposed semi-automatic initialization algorithm, the task of developing a fully-automatic segmentation algorithm is reduced to designing a fully-automatic initialization process. Such a process is introduced in this thesis. In this work, the contours defined by the semi-automatic and the fully-automatic segmentation algorithm are compared with the boundary outlined by an expert observer. Tested using 8 sample images, the mean absolute difference between the semi-automatically defined and the manually outlined boundary is less than 2.5 pixels, and that between the fully-automatically defined and the manually outlined boundary is less than 4 pixels. Automated segmentation tools that achieve this level of accuracy would be very useful in assisting radiologists to accomplish the task of segmenting prostate boundary much more efficiently.
Author: Min Xian
Publisher: Infinite Study
ISBN:
Category :
Languages : en
Pages : 9
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Book Description
Breast ultrasound (BUS) image segmentation is challenging and critical for BUS Computer-Aided Diagnosis (CAD) systems. Many BUS segmentation approaches have been proposed in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which result in discrepancy in performance comparison.
