人工智能
电阻抗断层成像
支持向量机
主成分分析
计算机科学
模式识别(心理学)
特征提取
分类器(UML)
上下文图像分类
分割
机器学习
断层摄影术
图像(数学)
医学
放射科
作者
Eoghan Dunne,Adam Santorelli,Brian McGinley,Geraldine Leader,Martin O’Halloran,Emily Porter
出处
期刊:Physiological Measurement
[IOP Publishing]
日期:2018-12-03
卷期号:39 (12): 124001-124001
被引量:16
标识
DOI:10.1088/1361-6579/aae6ed
摘要
Objective: In this study, we examine the potential of using machine learning classification to determine the bladder state ('not full', 'full') with electrical impedance tomography (EIT) images of the pelvic region. Accurate classification of these states would enable urinary incontinence (UI) monitoring to alert the patient, before involuntary voiding occurs, in a low-cost and discrete manner. Approach: Using both numerical and experimental data, we form datasets that contain diverse observations with varying clinical parameters such as bladder volume, urine conductivity, and the reference used for time-difference imaging. We then classify the bladder state using both pixel-wise and feature extraction-based classification techniques. We employ principal component analysis, wavelets, and image segmentation to help create features. Main results: The performance was compared across several classifier algorithms. The minimum accuracy was 77.50%. The highest accuracy observed was 100%, and was found by combining principal component analysis and the Gaussian radial based function kernel support vector machine. This combination also offered the best trade-off between classification performance and the costs of training time and memory space. The biggest challenge in bladder state classification is classifying volumes near the separation volume of not full and full, in which choosing the most suitable classifier combination can minimize this error. Significance: We performed the first machine learning classification of bladder EIT images, achieving high classification accuracies with both numerical and experimental data. This work highlights the potential of using image-based machine learning with an EIT device to support bladder monitoring for those suffering from UI.
科研通智能强力驱动
Strongly Powered by AbleSci AI