Estimating coronary artery lumen area with optimization-based contour detection

Luo, Zhongchi; Wang, Yuan‐Yuan; Wang, Weiqi

doi:10.1109/tmi.2003.811048

Cited by 22 publications

(2 citation statements)

References 8 publications

(8 reference statements)

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“…In both modalities, image acquisition is carried out by inserting the specific catheter inside the artery and performing a pullback movement. Accordingly, cross-section images with anatomical, morphological and pathological information of arteries are provided [2,3,5,6]. As a result, more reliable diagnostic are obtained, and correct therapeutic procedure may be executed [4,7-9].…”

Section: Introductionmentioning

confidence: 99%

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Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Moraes¹,

Cárdenas²,

Furuie³

2013

BioMed Eng OnLine

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BackgroundAtherosclerosis causes millions of deaths, annually yielding billions in expenses round the world. Intravascular Optical Coherence Tomography (IVOCT) is a medical imaging modality, which displays high resolution images of coronary cross-section. Nonetheless, quantitative information can only be obtained with segmentation; consequently, more adequate diagnostics, therapies and interventions can be provided. Since it is a relatively new modality, many different segmentation methods, available in the literature for other modalities, could be successfully applied to IVOCT images, improving accuracies and uses.MethodAn automatic lumen segmentation approach, based on Wavelet Transform and Mathematical Morphology, is presented. The methodology is divided into three main parts. First, the preprocessing stage attenuates and enhances undesirable and important information, respectively. Second, in the feature extraction block, wavelet is associated with an adapted version of Otsu threshold; hence, tissue information is discriminated and binarized. Finally, binary morphological reconstruction improves the binary information and constructs the binary lumen object.ResultsThe evaluation was carried out by segmenting 290 challenging images from human and pig coronaries, and rabbit iliac arteries; the outcomes were compared with the gold standards made by experts. The resultant accuracy was obtained: True Positive (%) = 99.29 ± 2.96, False Positive (%) = 3.69 ± 2.88, False Negative (%) = 0.71 ± 2.96, Max False Positive Distance (mm) = 0.1 ± 0.07, Max False Negative Distance (mm) = 0.06 ± 0.1.ConclusionsIn conclusion, by segmenting a number of IVOCT images with various features, the proposed technique showed to be robust and more accurate than published studies; in addition, the method is completely automatic, providing a new tool for IVOCT segmentation.

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Section: Introductionmentioning

confidence: 99%

“…[10-12]. Therefore, accurately separating related objects in an image bring special information for a range of coronary investigation; consequently, segmentation has been the scope of many studies recently [2-4,6,7,9,13]. …”

Section: Introductionmentioning

confidence: 99%

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Moraes¹,

Cárdenas²,

Furuie³

2013

BioMed Eng OnLine

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Detection of Outer Layer of the Vessel Wall and Characterization of Calcified Plaques in IVUS Images

Roodaki

Najafi

Soltanzadi

et al. 2008

Communications in Computer and Information Science

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Abstract. Intravascular Ultrasound (IVUS) is a diagnostic imaging techniquethat provides tomographic visualization of coronary arteries. Important challenges in analysis of IVUS images are speckle noise, artifacts of catheter and calcified shadows. In this paper, we present a method for the automated detection of outer (media-adventitia) border of vessel by the use of geometric deformable models. Speckle noise is reduced with median filter. The initial contour is extracted using Canny edge detection and finally the calcified regions are characterized by using Bayes classifier and thresholding methods. The proposed methods were evaluated on 60 IVUS images from 7 different patients. The results show that the border detection method was statistically accurate and in the range of inter observer variability (based on the used validation methods). Bayesian classifier enables us to characterize the regions of interest, with a sensitivity and specificity of 92.67% and 98.5% respectively.

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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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Estimating coronary artery lumen area with optimization-based contour detection

Cited by 22 publications

References 8 publications

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Detection of Outer Layer of the Vessel Wall and Characterization of Calcified Plaques in IVUS Images

Probabilistic Segmentation of the Lumen from Intravascular Ultrasound Radio Frequency Data

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