Matheus Cardoso Moraes

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

Intravascular ultrasound (IVUS) image segmentation can provide more detailed vessel and plaque information, resulting in better diagnostics, evaluation and therapy planning. A novel automatic segmentation proposal is described herein; the method relies on a binary morphological object reconstruction to segment the coronary wall in IVUS images. First, a preprocessing followed by a feature extraction block are performed, allowing for the desired information to be extracted. Afterward, binary versions of the desired objects are reconstructed, and their contours are extracted to segment the image. The effectiveness is demonstrated by segmenting 1300 images, in which the outcomes had a strong correlation to their corresponding gold standard. Moreover, the results were also corroborated statistically by having as high as 92.72% and 91.9% of true positive area fraction for the lumen and media adventitia border, respectively. In addition, this approach can be adapted easily and applied to other related modalities, such as intravascular optical coherence tomography and intravascular magnetic resonance imaging.

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction

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.

Realistic IVUS Image Generation in Different Intraluminal Pressures

Intravascular ultrasound (IVUS) phantoms are important to calibrate and evaluate many IVUS imaging processing tasks. However, phantom generation is never the primary focus of related works; hence, it cannot be well covered, and is usually based on more than one platform, which may not be accessible to investigators. Therefore, we present a framework for creating representative IVUS phantoms, for different intraluminal pressures, based on the finite element method and Field II. First, a coronary cross-section model is selected. Second, the coronary regions are identified to apply the properties. Third, the corresponding mesh is generated. Fourth, the intraluminal force is applied and the deformation computed. Finally, the speckle noise is incorporated. The framework was tested taking into account IVUS contrast, noise and strains. The outcomes are in line with related studies and expected values. Moreover, the framework toolbox is freely accessible and fully implemented in a single platform.

Realistic deformable 3D numeric phantom for transcutaneous ultrasound

Introduction Numerical phantoms are important tools to design, calibrate and evaluate several methods in various image-processing applications, such as echocardiography and mammography. We present a framework for creating ultrasound numerical deformable phantoms based on Finite Element Method (FEM), Linear Isomorphism and Field II. The proposed method considers that the scatterers map is a property of the tissue; therefore, the scatterers should move according to the tissue strain. Methods First, a volume representing the target tissue is loaded. Second, parameter values, such as Young’s Modulus, scatterers density, attenuation and scattering amplitudes are inserted for each different regions of the phantom. Then, other parameters related to the ultrasound equipment, such as ultrasound frequency and number of transducer elements, are also defined in order to perform the ultrasound acquisition using Field II. Third, the size and position of the transducer and the pressures that are applied against the tissue are defined. Subsequently, FEM is executed and deformation is computed. Next, 3D linear isomorphism is performed to displace the scatterers according to the deformation. Finally, Field II is carried out to generate the non-deformed and deformed ultrasound data. Results The framework is evaluated by comparing strain values obtained the numerical simulation and from the physical phantom from CIRS. The mean difference between both phantoms is lesser than 10%. Conclusion The acoustic and deformation outcomes are similar to those obtained using a physical phantom. This framework led to a tool, which is available online and free of charges for educational and research purposes.