Image Analysis Techniques for Automated IVUS Contour Detection

Papadogiorgaki, Maria; Mezaris, Vasileios; Chatzizisis, Yiannis S.; Giannoglou, George D.; Kompatsiaris, Ioannis

doi:10.1016/j.ultrasmedbio.2008.01.022

Cited by 68 publications

(73 citation statements)

References 24 publications

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“…Downe et al (Downe et al (2008)) introduced a method where principal component analysis was used for pre-processing, while active contour models were used to provide an initial segmentation for a 3D graph search method. Multilevel discrete wavelet frames decomposition was used by Papadogiorgaki et al (Papadogiorgaki et al (2008)) to generate texture information that was used along with the intensity information for contour initialization. Similarly, Katouzian et al (Katouzian et al (2008)) presented a method where texture information was extracted using a discrete wavelet packet transform.…”

Section: Previous Workmentioning

confidence: 99%

Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach

Mendizabal-Ruiz

Rivera

Kakadiaris

2013

Medical Image Analysis

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Intravascular ultrasound (IVUS) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels and is particularly useful for studying atherosclerosis. In this paper, we present a computational method for the delineation of the luminal border in IVUS B-mode images. The method is based in the minimization of a probabilistic cost function (that deforms a parametric curve) which defines a probability field that is regularized with respect to the given likelihoods of the pixels belonging to blood and non-blood. These likelihoods are obtained by a Support Vector Machine classifier trained using samples of the lumen and non-lumen regions provided by the user in the first frame of the sequence to be segmented. In addition, an optimization strategy is introduced in which the direction of the steepest descent and Broyden-Fletcher-Goldfarb-Shanno optimization methods are linearly combined to improve convergence. Our proposed method (MRK) is capable of segmenting IVUS B-mode images from different systems and transducer frequencies without the need of any parameter tuning, and it is robust with respect to changes of the B-mode reconstruction parameters which are subjectively adjusted by the interventionist. We validated the proposed method on six 20 MHz and six 40 MHz IVUS stationary sequences corresponding to regions with different degrees of stenosis, and evaluated its performance by comparing the * Corresponding author segmentation results with manual segmentation by two observers. Furthermore, we compared our method with the segmentation results on the same sequences as provided by the authors of three other segmentation methods available in the literature. The performance of all methods was quantified using Dice and Jaccard similarity indexes, Hausdorff distance, linear regression and Bland-Altman analysis. The results indicate the advantages of our method for the segmentation of the lumen contour.

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Section: Previous Workmentioning

confidence: 99%

Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach

Mendizabal-Ruiz

Rivera

Kakadiaris

2013

Medical Image Analysis

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“…IVUS exam is a catheter-based technique that renders 2D cross-sectional image of the coronary arteries, and provides information concerning the lumen and wall morphology [3].…”

Section: Introductionmentioning

confidence: 99%

“…Automatic IVUS border segmentation methods can be designed to segment the borders such as: Lumen border [21], Media-adventitial border [22], [23], or both of the lumen and the Media-adventitial borders [3].…”

Section: Introductionmentioning

confidence: 99%

“…As edge detection techniques, in [18], Canny edge detection with different sigma is used to segment IVUS borders. Also a texture information extraction technique is used to segment IVUS images, in [3], the author uses multilevel discrete wavelet frames decomposition along with the intensity information for contour initialization. In the same year, [19] uses a discrete wavelet packet transform along with k-means clustering for contour initialization.…”

Section: Introductionmentioning

confidence: 99%

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An Automatic Segmentation for Determination of IV Vessel Boundaries

Tayel¹,

Massoud²,

Shehata³

2014

IJBBB

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Abstract-Coronary heart disease ranks as the top disease which causes the death in the Egypt. Intravascular (IV) ultrasound imaging is used along with X-ray coronary angiography to detect vessel pathologies. This paper introduces three automated approaches for detection of lumen and media-adventitia borders in IV images. In the first method, a 2D median filter is used to initialize the lumen and media-adventitia contours. The second method uses the Lucy-Richardson method and the 2D median filter to determine the lumen border. Method three is a combination of two methods to combine their advantages. The catheter boundaries were automatically determined. This method shows a higher accuracy and ability to work under artifacts and noisy conditions.

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“…For these images, edge information is not sufficient and therefore, later approaches incorporated prior knowledge using region and global information such as texture [1], gray level variances [2,3], statistical properties of the intensities [4], temporal information (3D segmentation) [5], and discrete wavelet decomposition [6]. Most recent approaches include the use of nonparametric probability densities with global measurements [7], multilevel discrete wavelet frame decomposition [8], discrete wavelet packet transform [9], machine learning classification methods [10], a combination of gray level probability density functions and the intensity gradient [11], linear-filtered gradient vector flow which drives the deformation of a balloon snake [12], and binary morphological object reconstruction [13].…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Segmentation of the Lumen from Intravascular Ultrasound Radio Frequency Data

Mendizabal-Ruiz

Kakadiaris

2012

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012

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Abstract. Intravascular ultrasound (IVUS) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels. In this paper, we present a method for the segmentation of the luminal border using IVUS radio frequency (RF) data. Specifically, we parameterize the lumen contour using Fourier series. This contour is deformed by minimizing a cost function that is formulated using a probabilistic approach in which the a priori term is obtained using the prediction confidence of a Support Vector Machine classifier using features extracted from the RF signal. We evaluated the performance of our method by comparing our results with manual segmentations from two expert observers on 280 frames from eight 40 MHz IVUS sequences from rabbits and pigs. The performance was evaluated using the Dice similarity coefficient, coefficient of determination, and linear regressions of the lumen area for each frame. Our results indicate the feasibility of our method for the segmentation of the lumen from IVUS RF data.

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Image Analysis Techniques for Automated IVUS Contour Detection

Cited by 68 publications

References 24 publications

Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach

Segmentation of the luminal border in intravascular ultrasound B-mode images using a probabilistic approach

An Automatic Segmentation for Determination of IV Vessel Boundaries

Probabilistic Segmentation of the Lumen from Intravascular Ultrasound Radio Frequency Data

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