Detection of Luminal Contour Using Fuzzy Clustering and Mathematical Morphology in Intravascular Ultrasound Images

Filho, E. dos Santos; Yoshizawa, Makoto; Saijo, Yoshifumi; Iwamoto, Takahiro

doi:10.1109/iembs.2005.1617226

Cited by 9 publications

(3 citation statements)

References 6 publications

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“…In recent years, researchers have tried various ways to solve this problem. Santos et al (2005) proposed a system for automatic luminal contour segmentation, it simply use Morphological filtering to get solution points obtained by linear scanner as the edge of the image, so this method cannot be applied in complex background systems. Bosworth and Acton (1999) proposed a general multi-scale mathematical morphological segmentation method but it requires some prior knowledge of the image.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Segmentation Method for 2-D Barcode Image Base on Mathematic Morphological

Li¹,

Wang²,

Chen³

et al. 2013

RJASET

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Segmentation is a key process of 2-D barcode identification. In this study we propose a fast adaptive segmentation method that is based on morphological method which is suitable for kinds of 2-D barcode images with different scale, angle and sort. The algorithm is based on mathematical morphology, the basic idea of the algorithm is to use Multi-scale open reconstruction of mathematical morphology to transform the image continuously, then choose whether to terminate by the results of the adjacent image transformation and finally get the final segmentation results by further processing of the images obtain from termination.The proposed approach is applied in experiments on 2-D barcodes with complicated background. The results indicated that the proposed method is very effective in adaptively 2-D barcode image segmentation.

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Section: Introductionmentioning

confidence: 99%

Adaptive Segmentation Method for 2-D Barcode Image Base on Mathematic Morphological

Li¹,

Wang²,

Chen³

et al. 2013

RJASET

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show abstract

“…Despite the simplicity of some of the presented works [47]- [49], [53] encouraging results were produced, especially on images acquired with low-frequency transducers (20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). However, in reality, the border detection problem is more complex, especially when high-frequency transducers (above 40 MHz) are used due to intrinsic and extrinsic image artifacts (see Section I-G) as well as local and global variations among luminal and intimal grayscale distributions, demanding for more sophisticated methods.…”

Section: Statistical-and Probabilistic-based Techniquesmentioning

confidence: 99%

“…Taki et al [49] proposed a similar automated technique but used two different threshold values after despeckling through affine invariant anisotropic filters to detect both borders simultaneously. It has been shown that if distributions corresponding to lumen and tissue regions are well separated, simple decorrelation [50], thresholding techniques [51], [52], or unsupervised classification algorithms [53] along with morphological operations [52], [53] could lead to accurate identification of vessel borders.…”

Section: Statistical-and Probabilistic-based Techniquesmentioning

confidence: 99%

A State-of-the-Art Review on Segmentation Algorithms in Intravascular Ultrasound (IVUS) Images

Katouzian

Angelini

Carlier

et al. 2012

IEEE Trans. Inform. Technol. Biomed.

132

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Abstract-Over the past two decades, intravascular ultrasound (IVUS) image segmentation has remained a challenge for researchers while the use of this imaging modality is rapidly growing in catheterization procedures and in research studies. IVUS provides cross-sectional grayscale images of the arterial wall and the extent of atherosclerotic plaques with high spatial resolution in real time. In this paper, we review recently developed image processing methods for the detection of media-adventitia and luminal borders in IVUS images acquired with different transducers operating at frequencies ranging from 20 to 45 MHz. We discuss methodological challenges, lack of diversity in reported datasets, and weaknesses of quantification metrics that make IVUS segmentation still an open problem despite all efforts. In conclusion, we call for a common reference database, validation metrics, and ground-truth definition with which new and existing algorithms could be benchmarked.Index Terms-Intravascular ultrasound (IVUS), lumen, mediaadventitia (MA), segmentation.

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An automated approach for segmentation of intravascular ultrasound images based on parametric active contour models

Vard

Jamshidi

Movahhedinia

2012

Australas Phys Eng Sci Med

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This paper presents a fully automated approach to detect the intima and media-adventitia borders in intravascular ultrasound images based on parametric active contour models. To detect the intima border, we compute a new image feature applying a combination of short-term autocorrelations calculated for the contour pixels. These feature values are employed to define an energy function of the active contour called normalized cumulative short-term autocorrelation. Exploiting this energy function, the intima border is separated accurately from the blood region contaminated by high speckle noise. To extract media-adventitia boundary, we define a new form of energy function based on edge, texture and spring forces for the active contour. Utilizing this active contour, the media-adventitia border is identified correctly even in presence of branch openings and calcifications. Experimental results indicate accuracy of the proposed methods. In addition, statistical analysis demonstrates high conformity between manual tracing and the results obtained by the proposed approaches.

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Detection of Luminal Contour Using Fuzzy Clustering and Mathematical Morphology in Intravascular Ultrasound Images

Cited by 9 publications

References 6 publications

Adaptive Segmentation Method for 2-D Barcode Image Base on Mathematic Morphological

Adaptive Segmentation Method for 2-D Barcode Image Base on Mathematic Morphological

A State-of-the-Art Review on Segmentation Algorithms in Intravascular Ultrasound (IVUS) Images

An automated approach for segmentation of intravascular ultrasound images based on parametric active contour models

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