Wavelets in Medical Image Processing: Denoising, Segmentation, and Registration

Jin, Yinpeng; Angelini, Elsa D.; Laine, Andrew F.

doi:10.1007/0-306-48551-6_6

Cited by 26 publications

(13 citation statements)

References 88 publications

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“…In addition, the wavelet frequency components decomposing an image at different scales and orientations are also used to guide the segmentation [17]. These components are able to localize spatial frequencies and detect the high frequency content of the image, such as lines and edges [18].…”

Section: Appearance Featuresmentioning

confidence: 99%

Medical Image Segmentation: A Brief Survey

Elnakib¹,

Gimel’farb²,

Suri³

et al. 2011

Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies

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Accurate segmentation of 2-D, 3-D, and 4-D medical images to isolate anatomical objects of interest for analysis is essential in almost any computer-aided diagnosis system or other medical imaging applications. Various aspects of segmentation features and algorithms have been extensively explored for many years in a host of publications. However, the problem remains challenging, with no general and unique solution, due to a large and constantly growing number of different objects of interest, large variations of their properties in images, different medical imaging modalities, and associated changes of signal homogeneity, variability, and noise for each object. This chapter overviews most popular medical image segmentation techniques and discusses their capabilities, and basic advantages and limitations. The state-of-the-art techniques of the last decade are also outlined.

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Section: Appearance Featuresmentioning

confidence: 99%

Medical Image Segmentation: A Brief Survey

Elnakib¹,

Gimel’farb²,

Suri³

et al. 2011

Multi Modality State-of-the-Art Medical Image Segmentation and Registration Methodologies

View full text Add to dashboard Cite

show abstract

“…In our implementation, a linear penalty function similar to wavelet soft-thresholding [26] function was used. However, more sophisticated membership function such as fuzzy membership function [27] can be used.…”

Section: Myocardial Segmentation In High Speed Mrimentioning

confidence: 99%

Real-time segmentation by Active Geometric Functions

Duan

Angelini

Laine

2010

Computer Methods and Programs in Biomedicine

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Recent advances in 4D imaging and real-time imaging provide image data with clinically important cardiac dynamic information at high spatial or temporal resolution. However, the enormous amount of information contained in these data has also raised a challenge for traditional image analysis algorithms in terms of efficiency. In this paper, a novel deformable model framework, Active Geometric Functions (AGF), is introduced to tackle the real-time segmentation problem. As an implicit framework paralleling to level-set, AGF has mathematical advantages in efficiency and computational complexity as well as several flexible feature similar to level-set framework. AGF is demonstrated in two cardiac applications: endocardial segmentation in 4D ultrasound and myocardial segmentation in MRI with super high temporal resolution. In both applications, AGF can perform real-time segmentation in several milliseconds per frame, which was less than the acquisition time per frame. Segmentation results are compared to manual tracing with comparable performance with inter-observer variability. The ability of such real-time segmentation will not only facilitate the diagnoses and workflow, but also enables novel applications such as interventional guidance and interactive image acquisition with online segmentation.

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“…Полученное в результате фильтрации изображение называется картой признака. В реальных вычислениях использовались три типа фильтров: вейвлеты, морфологические фильтры, характеризующие локальную геометрию изображения, и разностно-гауссовские фильтры (difference-of-Gaussians -DoG фильтры), поскольку эти фильтры уже зарекомендовали себя при анализе различных классов изображений [29,57,60]. Использование карт признаков позволяет найти на изображении наиболее вероятную область расположения целевых объектов.…”

Section: сегментация и вниманиеunclassified

Temporal Correlation Theory and Modeling the Segmentation of the Visual Information in the Brain (a review)

Казанович¹,

Kazanovich²

2010

Math.Biol.Bioinf.

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Wavelets in Medical Image Processing: Denoising, Segmentation, and Registration

Cited by 26 publications

References 88 publications

Medical Image Segmentation: A Brief Survey

Medical Image Segmentation: A Brief Survey

Real-time segmentation by Active Geometric Functions

Temporal Correlation Theory and Modeling the Segmentation of the Visual Information in the Brain (a review)

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