Generation and optimization of superpixels as image processing kernels for Jones matrix optical coherence tomography

Miyazawa, Akiyoshi; Hong, Young-Joo; Makita, Shuichi; Kasaragod, Deepa; Yasuno, Yoshiaki

doi:10.1364/boe.8.004396

Cited by 6 publications

(4 citation statements)

References 32 publications

(61 reference statements)

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“…The DOPU algorithm, therefore, averages the Stokes components over adjacent speckles, and the underlying polarization scrambling results in a decreased DOP in the evaluation window. Based on DOPU, several improvements concerning its noise sensitivity, orientation dependency, and evaluation kernel size have been proposed 9,[17][18][19][20] and enhance depolarization imaging as a powerful tool for several biomedical applications.…”

Section: Introductionmentioning

confidence: 99%

Detection of carious lesions utilizing depolarization imaging by polarization sensitive optical coherence tomography

et al. 2018

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Abstract. As dental caries is one of the most common diseases, the early and noninvasive detection of carious lesions plays an important role in public health care. Optical coherence tomography (OCT) with its ability of depth-resolved, high-resolution, noninvasive, fast imaging has been previously recognized as a promising tool in dentistry. Additionally, polarization sensitive imaging provides quantitative measures on the birefringent tissue properties and can be utilized for imaging dental tissue, especially enamel and dentin. By imaging three exemplary tooth samples ex vivo with proximal white spot, brown spot, and cavity, we show that the combination of polarization sensitive OCT and the degree of polarization uniformity (DOPU) algorithm is a promising approach for the detection of proximal carious lesions due to the depolarization contrast of demineralized tissue. Furthermore, we investigate different sizes of the DOPU evaluation kernel on the resulting contrast and conclude a suitable value for this application. We propose that DOPU provides an easy to interpret image representation and appropriate contrast for possible future screening applications in early caries diagnostics. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Section: Introductionmentioning

confidence: 99%

Detection of carious lesions utilizing depolarization imaging by polarization sensitive optical coherence tomography

et al. 2018

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“…Miyazawa et al demonstrated pixel grouping for JM-OCT, superpixelization [143]. In this method, the pixels in a crosssectional JM-OCT image are mapped into a six-dimensional feature space of four JM-OCT contrasts and two (lateral and axial) spatial dimensions.…”

Section: B Jm-oct Specific Image Processingmentioning

confidence: 99%

Multi-Contrast Jones-Matrix Optical Coherence Tomography—The Concept, Principle, Implementation, and Applications

Yasuno

2023

IEEE J. Select. Topics Quantum Electron.

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Jones-matrix optical coherence tomography (JM-OCT) is an extension of polarization-sensitive OCT, which provides multiple types of optical contrasts of biological and clinical samples. JM-OCT measures the spatial distribution of the Jones matrix of the sample and also its time sequence. All contrasts (i.e., multi-contrast OCT images) are then computed from the Jones matrix. The contrasts obtained from the Jones matrix include not only the conventional and polarization-insensitive OCT intensity, cumulative and local phase retardation (birefringence), degreeof-polarization uniformity quantifying the polarization randomness of the sample, diattenuation, but also signal attenuation coefficient, sample scatterer density, Doppler OCT, OCT angiography, and dynamic OCT that contrasts intracellular motility or metabolism by analyzing the temporal fluctuation of the OCT signal. JM-OCT is a generalized version of OCT because it measures the generalized form of the sample information; i.e., the Jones matrix sequence. This review summarizes the basic conception, mathematical principle, hardware implementation, signal and image processing, and biological and clinical applications of JM-OCT. Advanced technical topics, including JM-OCTspecific noise correction and quantity estimation and JM-OCT's self-calibration nature, are also described.

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“…[54] combined superpixel with LogitBoost adaptive boosting to detect glaucomatous damage in 3D OCT images. In [44], the superpixel technique was applied to generate the flexible kernels of local statistics on the Jones matrix-based polarization sensitive OCT.…”

Section: Related Workmentioning

confidence: 99%

Cardiac Adipose Tissue Segmentation via Image-Level Annotations

Huang

Gan

Lye

et al. 2023

IEEE J. Biomed. Health Inform.

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Automatically identifying the structural substrates underlying cardiac abnormalities can potentially provide real-time guidance for interventional procedures. With the knowledge of cardiac tissue substrates, the treatment of complex arrhythmias such as atrial fibrillation and ventricular tachycardia can be further optimized by detecting arrhythmia substrates to target for treatment (i.e., adipose) and identifying critical structures to avoid. Optical coherence tomography (OCT) is a real-time imaging modality that aids in addressing this need. Existing approaches for cardiac image analysis mainly rely on fully supervised learning techniques, which suffer from the drawback of workload on labor-intensive annotation process of pixelwise labeling. To lessen the need for pixel-wise labeling, we develop a two-stage deep learning framework for cardiac adipose tissue segmentation using image-level annotations on OCT images of human cardiac substrates. In particular, we integrate class activation mapping with superpixel segmentation to solve the sparse tissue seed challenge raised in cardiac tissue segmentation. Our study bridges the gap between the demand on automatic tissue analysis and the lack of high-quality pixel-wise annotations. To the best of our knowledge, this is the first study that attempts to address cardiac tissue segmentation on OCT images via weakly supervised learning techniques. Within an invitro human cardiac OCT dataset, we demonstrate that our weakly supervised approach on image-level annotations achieves comparable performance as fully supervised methods trained on pixel-wise annotations.

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Generation and optimization of superpixels as image processing kernels for Jones matrix optical coherence tomography

Cited by 6 publications

References 32 publications

Detection of carious lesions utilizing depolarization imaging by polarization sensitive optical coherence tomography

Detection of carious lesions utilizing depolarization imaging by polarization sensitive optical coherence tomography

Multi-Contrast Jones-Matrix Optical Coherence Tomography—The Concept, Principle, Implementation, and Applications

Cardiac Adipose Tissue Segmentation via Image-Level Annotations

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