胶囊内窥镜气泡框中圆形图案的识别。

IF 1.1 Q4 ENGINEERING, BIOMEDICAL
Journal of Medical Signals & Sensors Pub Date : 2024-07-02 eCollection Date: 2024-01-01 DOI:10.4103/jmss.jmss_50_23
Hossein Mir, Vahid Sadeghi, Alireza Vard, Alireza Mehri Dehnavi
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引用次数: 0

摘要

背景:无线胶囊内窥镜捕获的大量图像都含有不同数量的气泡。由于气泡会降低小肠粘膜的可视化质量,不同的研究都认为气泡是无用的物质,而本研究旨在开发一种实用的方法来评估圆形气泡的流变能力,为未来的临床诊断提供建议:方法:从 Kvasir 胶囊内窥镜数据集中,根据气泡大小将气泡参与程度不同的图像分为两类。边界反射出现在圆形气泡的边界边缘,在频域中,高频带与空间域中的这些边缘相对应。第一步是使用小波变换(WT)和高斯差分对边界反射进行高通滤波,第二步是在提取的边界上应用快速小波变换(FCT)和霍夫变换作为圆检测工具,并评估各种半径气泡的分布和丰度:结果:使用 WT 作为预处理方法提取边界,使圆形检测工具更容易集中于高频圆形图案。因此,使用带有预定义参数的 FCT 可以指定图像中所有气泡的半径种类和范围以及丰度。总体判别因子(ODF)为 15.01,7.1 显示了胃肠道(GI)中不同的气泡分布。第 1-2 个数据集的 ODF 识别率表明,气泡的流变特性与其覆盖面积和丰度之间存在关系,突出了 WT 和 FCT 在确定气泡分布以实现诊断目标方面的性能:在胃肠道分析中采用面向对象的方法使胃肠病学家能够近似判断肠道内液体的组成特征。从数据集获得的结果证明,计算出的 ODF 之间的差异可用作肠道内液体流变特征(如粘度)质量评估的指标,这有助于胃肠病学家评估病人的消化质量。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Identification of Circular Patterns in Capsule Endoscopy Bubble Frames.

Identification of Circular Patterns in Capsule Endoscopy Bubble Frames.

Identification of Circular Patterns in Capsule Endoscopy Bubble Frames.

Identification of Circular Patterns in Capsule Endoscopy Bubble Frames.

Background: A significant number of frames captured by the wireless capsule endoscopy are involved with varying amounts of bubbles. Whereas different studies have considered bubbles as nonuseful agents due to the fact that they reduce the visualization quality of the small intestine mucosa, this research aims to develop a practical way of assessing the rheological capability of the circular bubbles as a suggestion for future clinical diagnostic purposes.

Methods: From the Kvasir-capsule endoscopy dataset, frames with varying levels of bubble engagements were chosen in two categories based on bubble size. Border reflections are present on the edges of round-shaped bubbles in their boundaries, and in the frequency domain, high-frequency bands correspond to these edges in the spatial domain. The first step is about high-pass filtering of border reflections using wavelet transform (WT) and Differential of Gaussian, and the second step is related to applying the Fast Circlet Transform (FCT) and the Hough transform as circle detection tools on extracted borders and evaluating the distribution and abundance of all bubbles with the variety of radii.

Results: Border's extraction using WT as a preprocessing approach makes it easier for circle detection tool for better concentration on high-frequency circular patterns. Consequently, applying FCT with predefined parameters can specify the variety and range of radius and the abundance for all bubbles in an image. The overall discrimination factor (ODF) of 15.01, and 7.1 showing distinct bubble distributions in the gastrointestinal (GI) tract. The discrimination in ODF from datasets 1-2 suggests a relationship between the rheological properties of bubbles and their coverage area plus their abundance, highlighting the WT and FCT performance in determining bubbles' distributions for diagnostic objectives.

Conclusion: The implementation of an object-oriented attitude in gastrointestinal analysis makes it intelligible for gastroenterologists to approximate the constituent features of intra-intestinal fluids. this can't be evaluated until the bubbles are considered as non-useful agents. The obtained results from the datasets proved that the difference between the calculated ODF can be used as an indicator for the quality estimation of intraintestinal fluids' rheological features like viscosity, which helps gastroenterologists evaluate the quality of patient digestion.

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来源期刊
Journal of Medical Signals & Sensors
Journal of Medical Signals & Sensors ENGINEERING, BIOMEDICAL-
CiteScore
2.30
自引率
0.00%
发文量
53
审稿时长
33 weeks
期刊介绍: JMSS is an interdisciplinary journal that incorporates all aspects of the biomedical engineering including bioelectrics, bioinformatics, medical physics, health technology assessment, etc. Subject areas covered by the journal include: - Bioelectric: Bioinstruments Biosensors Modeling Biomedical signal processing Medical image analysis and processing Medical imaging devices Control of biological systems Neuromuscular systems Cognitive sciences Telemedicine Robotic Medical ultrasonography Bioelectromagnetics Electrophysiology Cell tracking - Bioinformatics and medical informatics: Analysis of biological data Data mining Stochastic modeling Computational genomics Artificial intelligence & fuzzy Applications Medical softwares Bioalgorithms Electronic health - Biophysics and medical physics: Computed tomography Radiation therapy Laser therapy - Education in biomedical engineering - Health technology assessment - Standard in biomedical engineering.
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