Pixel-Level Tissue Classification for Ultrasound Images

Pazinato, Daniel V.; Stein, Bernardo V.; Almeida, Waldir Rodrigues de; Werneck, Rafael de Oliveira; Mendes, Pedro; Penatti, Otávio Augusto Bizetto; Torres, Ricardo da Silva; Menezes, Fábio Hüsemann; Rocha, Anderson

doi:10.1109/jbhi.2014.2386796

Cited by 31 publications

(22 citation statements)

References 33 publications

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“…The characterization of plaque composition at each pixel position based solely on the intensity value at that pixel, as proposed in several papers [21], [23], [25], is challenging due to the significant overlap between the intensity distributions of the different plaque constituents, as demonstrated in this recent study [26]. Instead, using patches around each pixel position (see examples of Fig.…”

Section: Methodsmentioning

confidence: 99%

“…3. In [26], the authors considered patches around the pixel position but only to calculate predefined imaging features such as statistical moments and histograms of oriented gradients before the learning and classification stages. This means not all the information contained in the patches was used for the plaque discrimination.…”

Section: Methodsmentioning

confidence: 99%

“…through an operator who marks two corresponding regions in different images, which are then used as the references for intensity normalization of the dynamic ranges of the images. In most works, such as the recent study using SVM [26], the operator is required to draw manually two regions corresponding to the blood and to the adventitia wall. Such an approach, however, is dependent on the user input, which can change significantly due to the variability in the appearance of both the blood and the adventitia in the same image that result from the presence of shadowing, reverberations, and inconsistencies within same tissues.…”

Section: Methodsmentioning

confidence: 99%

“…A recent study by Pazinato et al . [26] has shown that there is significant overlap between the intensity distributions of the different constituents in ultrasound, thus leading to accuracy results just over 50% even with the choice of optimal threshold values [26]. The authors proposed instead to estimate imaging features from patches around each pixel before classification, improving the identification of the plaque constituents to over 70% accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound

Lekadir

Galimzianova

Betriu

et al. 2017

IEEE J. Biomed. Health Inform.

176

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Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estimation of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of choice for plaque characterization in clinical practice. However, its significant image noise, coupled with the small size of the plaques and their complex appearance, makes it difficult for automated techniques to discriminate between the different plaque constituents. In this paper, we propose to address this challenging problem by exploiting the unique capabilities of the emerging deep learning framework. More specifically, and unlike existing works which require a priori definition of specific imaging features or thresholding values, we propose to build a convolutional neural network that will automatically extract from the images the information that is optimal for the identification of the different plaque constituents. We used approximately 90,000 patches extracted from a database of images and corresponding expert plaque characterizations to train and to validate the proposed convolutional neural network (CNN). The results of cross-validation experiments show a correlation of about 0.90 with the clinical assessment for the estimation of lipid core, fibrous cap, and calcified tissue areas, indicating the potential of deep learning for the challenging task of automatic characterization of plaque composition in carotid ultrasound

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound

Lekadir

Galimzianova

Betriu

et al. 2017

IEEE J. Biomed. Health Inform.

176

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“…Quantitative metrics [47], e.g., the Hausdorff distance, are calculated to compare the quality of reconstructed surface by four point-based surface reconstruction methods: Ball Pivoting, Power Crust, Poisson, and VSR.…”

Section: Framework Of Surface Reconstruction For Freehand 3d Us Usingmentioning

confidence: 99%

Quantitative Assessment of Variational Surface Reconstruction from Sparse Point Clouds in Freehand 3D Ultrasound Imaging during Image-Guided Tumor Ablation

Deng

Jiang

et al. 2016

Applied Sciences

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Surface reconstruction for freehand 3D ultrasound is used to provide 3D visualization of a VOI (volume of interest) during image-guided tumor ablation surgery. This is a challenge because the recorded 2D B-scans are not only sparse but also non-parallel. To solve this issue, we established a framework to reconstruct the surface of freehand 3D ultrasound imaging in 2011. The key technique for surface reconstruction in that framework is based on variational interpolation presented by Greg Turk for shape transformation and is named Variational Surface Reconstruction (VSR). The main goal of this paper is to evaluate the quality of surface reconstructions, especially when the input data are extremely sparse point clouds from freehand 3D ultrasound imaging, using four methods: Ball Pivoting, Power Crust, Poisson, and VSR. Four experiments are conducted, and quantitative metrics, such as the Hausdorff distance, are introduced for quantitative assessment. The experiment results show that the performance of the proposed VSR method is the best of the four methods at reconstructing surface from sparse data. The VSR method can produce a close approximation to the original surface from as few as two contours, whereas the other three methods fail to do so. The experiment results also illustrate that the reproducibility of the VSR method is the best of the four methods.

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Clinical Feasibility of Quantitative Ultrasound Texture Analysis: A Robustness Study Using Fetal Lung Ultrasound Images

Perez‐Moreno

Domínguez

Gratacós

et al. 2018

J of Ultrasound Medicine

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Objectives To compare the robustness of several methods based on quantitative ultrasound (US) texture analysis to evaluate its feasibility for extracting features from US images to use as a clinical diagnostic tool. Methods We compared, ranked, and validated the robustness of 5 texture‐based methods for extracting textural features from US images acquired under different conditions. For comparison and ranking purposes, we used 13,171 non‐US images from widely known available databases (OUTEX [University of Oulu, Oulu, Finland] and PHOTEX [Texture Lab, Heriot‐Watt University, Edinburgh, Scotland]), which were specifically acquired under different controlled parameters (illumination, resolution, and rotation) from 103 textures. The robustness of those methods with better results from the non‐US images was validated by using 666 fetal lung US images acquired from singleton pregnancies. In this study, 2 similarity measurements (correlation and Chebyshev distances) were used to evaluate the repeatability of the features extracted from the same tissue images. Results Three of the 5 methods (gray‐level co‐occurrence matrix, local binary patterns, and rotation‐invariant local phase quantization) had favorably robust performance when using the non‐US database. In fact, these methods showed similarity values close to 0 for the acquisition variations and delineations. Results from the US database confirmed robustness for all of the evaluated methods (gray‐level co‐occurrence matrix, local binary patterns, and rotation‐invariant local phase quantization) when comparing the same texture obtained from different regions of the image (proximal/distal lungs and US machine brand stratification). Conclusions Our results confirmed that texture analysis can be robust (high similarity for different condition acquisitions) with potential to be included as a clinical tool.

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Pixel-Level Tissue Classification for Ultrasound Images

Cited by 31 publications

References 33 publications

A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound

A Convolutional Neural Network for Automatic Characterization of Plaque Composition in Carotid Ultrasound

Quantitative Assessment of Variational Surface Reconstruction from Sparse Point Clouds in Freehand 3D Ultrasound Imaging during Image-Guided Tumor Ablation

Clinical Feasibility of Quantitative Ultrasound Texture Analysis: A Robustness Study Using Fetal Lung Ultrasound Images

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