Automatic Coronary Wall Segmentation in Intravascular Ultrasound Images Using Binary Morphological Reconstruction

Moraes, Matheus Cardoso; Furuie, S.S.

doi:10.1016/j.ultrasmedbio.2011.05.018

Cited by 28 publications

(29 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The concept of energy minimization process, dynamic programming, deformable and active contours, as well as snakes, are used in the works by [17,23,26] in which this theory is also applied in IVUS segmentation by [4,27], as well as in IVOCT by [3,28-31]. Wavelet Transformations have also a good acceptance, and have demonstrated to be a strong feature extractor in recent studies, for instance, [4,32] in IVUS, and [2] for IVOCT images. In addition, statistical and probabilistic approaches, contextual knowledge, or global image information and heuristic graph searching, gray level distribution and intensity profile analysis, can be found in [7,33,34] with IVOCT application in [35-37].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, statistical and probabilistic approaches, contextual knowledge, or global image information and heuristic graph searching, gray level distribution and intensity profile analysis, can be found in [7,33,34] with IVOCT application in [35-37]. Finally, Otsu followed by mathematical morphology has been successfully applied to make binary images and post-processing them; this combination can be found in [32,38], in which they are employed in IVUS, and [29,39,40] applied similar concept in IVOCT segmentation. Specifically, [32] have successfully applied DWPF, with Otsu binarization, and Binary Morphological Reconstruction to segment the media-adventitia border and coronary wall in IVUS images.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, Otsu followed by mathematical morphology has been successfully applied to make binary images and post-processing them; this combination can be found in [32,38], in which they are employed in IVUS, and [29,39,40] applied similar concept in IVOCT segmentation. Specifically, [32] have successfully applied DWPF, with Otsu binarization, and Binary Morphological Reconstruction to segment the media-adventitia border and coronary wall in IVUS images.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Moraes¹,

Cárdenas²,

Furuie³

2013

BioMed Eng OnLine

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Moraes¹,

Cárdenas²,

Furuie³

2013

BioMed Eng OnLine

View full text Add to dashboard Cite

show abstract

“…Moraes et al (Moraes and Furuie (2011)) presented a method that relies on a binary morphological object reconstruction to segment the coronary wall in IVUS images. Balocco et al (Balocco et al (2011)) proposed a method based on the stabilization of the IVUS sequence and the subsequent registration of contiguous frames to generate a parametric image that distinguished the presence of tissues from blood.…”

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

View full text Add to dashboard Cite

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.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

Automatic Coronary Wall Segmentation in Intravascular Ultrasound Images Using Binary Morphological Reconstruction

Cited by 28 publications

References 26 publications

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

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

Probabilistic Segmentation of the Lumen from Intravascular Ultrasound Radio Frequency Data

Contact Info

Product

Resources

About