Spatial anisotropic diffusion and local time correlation applied to segmentation of vessels in ultrasound image sequences

Balocco, Simone; Basset, Olivier; Cachard, Christian; Delachartre, Philippe

doi:10.1109/ultsym.2003.1293202

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…Simulations and results driven from in vitro data demonstrated the effectiveness of the proposed technique. The rational behind most BNR and BPD algorithms [73], [80]- [83] is that blood and plaques embody incoherent and coherent textural patterns along the pullback direction. Therefore, spatiotemporal information provides discriminative features for blood speckle and blood pool.…”

Section: Blood Noise Reduction and Blood Pool Detection Algorithmsmentioning

confidence: 99%

“…In order to validate the efficacy of the proposed method, they manually traced borders, measured lumen and plaque cross-sectional areas, and reported statistics for results before and after BNR. The authors in [80] also presented a BNR algorithm based on the fusion of anisotropically diffused filtered images [85] with temporal information and detection of the lumen borders by thresholding of edge images. A multiscale BNR algorithm was also proposed in [73] as discussed in Section II-D.…”

Section: Blood Noise Reduction and Blood Pool Detection Algorithmsmentioning

confidence: 99%

See 1 more Smart Citation

A State-of-the-Art Review on Segmentation Algorithms in Intravascular Ultrasound (IVUS) Images

Katouzian

Angelini

Carlier

et al. 2012

IEEE Trans. Inform. Technol. Biomed.

132

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Abstract-Over the past two decades, intravascular ultrasound (IVUS) image segmentation has remained a challenge for researchers while the use of this imaging modality is rapidly growing in catheterization procedures and in research studies. IVUS provides cross-sectional grayscale images of the arterial wall and the extent of atherosclerotic plaques with high spatial resolution in real time. In this paper, we review recently developed image processing methods for the detection of media-adventitia and luminal borders in IVUS images acquired with different transducers operating at frequencies ranging from 20 to 45 MHz. We discuss methodological challenges, lack of diversity in reported datasets, and weaknesses of quantification metrics that make IVUS segmentation still an open problem despite all efforts. In conclusion, we call for a common reference database, validation metrics, and ground-truth definition with which new and existing algorithms could be benchmarked.Index Terms-Intravascular ultrasound (IVUS), lumen, mediaadventitia (MA), segmentation.

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Section: Blood Noise Reduction and Blood Pool Detection Algorithmsmentioning

confidence: 99%

Section: Blood Noise Reduction and Blood Pool Detection Algorithmsmentioning

confidence: 99%

A State-of-the-Art Review on Segmentation Algorithms in Intravascular Ultrasound (IVUS) Images

Katouzian

Angelini

Carlier

et al. 2012

IEEE Trans. Inform. Technol. Biomed.

132

View full text Add to dashboard Cite

show abstract

“…From the perspective of MI-based deformable image registration, using these low-dose images as is will cause the dispersion of the mutual histogram (due to noise, in an otherwise uniform structure) leading to poor image registration. Anisotropic diffusion filtering has been shown to be an effective processing step prior to advanced image processing [76,83,182,183]. Figure 6.4 compares the performance of anisotropic diffusion filtering against other standard preprocessing techniques.…”

Section: Image Preprocessingmentioning

confidence: 99%

High-Performance 3D Image Processing Architectures for Image-Guided Interventions

Dandekar¹

2008

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Minimally invasive image-guided interventions (IGIs) are time and cost efficient, minimize unintended damage to healthy tissues, and lead to faster patient recovery. Advanced three-dimensional (3D) image processing is a critical need for navigation during IGIs. However, achieving on-demand performance, as required by IGIs, for these image processing operations using software-only implementations is challenging because of the sheer size of the 3D images, and memory and compute intensive nature of the operations. This dissertation, therefore, is geared toward developing high-performance 3D image processing architectures, which will enable improved intraprocedural visualization and navigation capabilities during IGIs.In this dissertation we present an architecture for real-time implementation of 3D filtering operations that are commonly employed for preprocessing of medical images. This architecture is approximately two orders of magnitude faster than corresponding software implementations and is capable of processing 3D medical images at their acquisition speeds. Report Documentation PageCombining complementary information through registration between pre-and intraprocedural images is a fundamental need in the IGI workflow. Intensity-based deformable registration, which is completely automatic and locally accurate, is a promising approach to achieve this alignment. These algorithms, however, are extremely compute intensive, which has prevented their clinical use. We present an FPGA-based architecture for accelerated implementation of intensity-based deformable image registration. This high-performance architecture achieves over an order of magnitude speedup when compared with a corresponding software implementation and reduces the execution time of deformable registration from hours to minutes while offering comparable image registration accuracy.Furthermore, we present a framework for multiobjective optimization of finite-precision implementations of signal processing algorithms that takes into account multiple conflicting objectives such as implementation accuracy and hardware resource consumption. The evaluation that we have performed in the context of FPGA-based image registration demonstrates that such an analysis can be used to enhance automated hardware design processes, and efficiently identify a system configuration that meets given design constraints. In addition, we also outline two novel clinical applications that can directly benefit from these developments and demonstrate the feasibility of our approach in the context of these applications. These advances will ultimately enable integration of 3D image processing into clinical workflow.

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Functional Assessment of Stenotic Coronary Artery in 3D Geometric Reconstruction From Fusion of Intravascular Ultrasound and X-Ray Angiography

et al. 2018

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Spatial anisotropic diffusion and local time correlation applied to segmentation of vessels in ultrasound image sequences

Cited by 5 publications

References 10 publications

A State-of-the-Art Review on Segmentation Algorithms in Intravascular Ultrasound (IVUS) Images

A State-of-the-Art Review on Segmentation Algorithms in Intravascular Ultrasound (IVUS) Images

High-Performance 3D Image Processing Architectures for Image-Guided Interventions

Functional Assessment of Stenotic Coronary Artery in 3D Geometric Reconstruction From Fusion of Intravascular Ultrasound and X-Ray Angiography

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