基于图像的静态b超图像纹理特征识别卵巢良恶性肿块的有效性评价。

IF 2.5 4区 医学 Q1 ACOUSTICS
Ultrasonic Imaging Pub Date : 2021-05-01 Epub Date: 2021-02-25 DOI:10.1177/0161734621998091
Dhurgham Al-Karawi, Hisham Al-Assam, Hongbo Du, Ahmad Sayasneh, Chiara Landolfo, Dirk Timmerman, Tom Bourne, Sabah Jassim
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引用次数: 6

摘要

机器学习方法在各种应用中的图像分析取得了重大成功,激发了人们对医学图像自动诊断支持系统的强烈兴趣。对癌变改变肿块/肿瘤细胞网络结构的方式不断深入的了解,已经为这些诊断系统提供了更合适的图像纹理特征及其提取方法。近年来,通过对不同表现水平的卵巢b超图像进行分析,几种纹理特征被应用于卵巢肿块的良恶性鉴别。然而,缺乏使用临床批准的通用图像集对这些报道的特征进行比较性能评估。本文利用242张不同病理特征的卵巢肿块超声扫描图像,对7种常用的纹理特征(直方图、直方图矩、局部二值模式[256-bin和59-bin]、定向梯度直方图、分形维数和Gabor滤波器)进行了实证评价。该评价不仅检验了基于单个纹理特征的分类方案的有效性,还检验了使用简单多数规则决策级融合的这些方案的各种组合的有效性。经过训练的支持向量机分类器在没有任何特定预处理的情况下对单个纹理特征进行分类,其准确率在75%到85%之间,其中7个矩和256-bin LBP位于下端,而Gabor滤波器位于上端。结合前k个(k = 3,5,7)表现最好的特征的分类结果,进一步将整体准确率提高到86%到90%之间。这些评估结果表明,所研究的每一个基于图像的纹理特征都为区分良性或恶性卵巢肿块提供了信息支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Evaluation of the Effectiveness of Image-based Texture Features Extracted from Static B-mode Ultrasound Images in Distinguishing between Benign and Malignant Ovarian Masses.

Significant successes in machine learning approaches to image analysis for various applications have energized strong interest in automated diagnostic support systems for medical images. The evolving in-depth understanding of the way carcinogenesis changes the texture of cellular networks of a mass/tumor has been informing such diagnostics systems with use of more suitable image texture features and their extraction methods. Several texture features have been recently applied in discriminating malignant and benign ovarian masses by analysing B-mode images from ultrasound scan of the ovary with different levels of performance. However, comparative performance evaluation of these reported features using common sets of clinically approved images is lacking. This paper presents an empirical evaluation of seven commonly used texture features (histograms, moments of histogram, local binary patterns [256-bin and 59-bin], histograms of oriented gradients, fractal dimensions, and Gabor filter), using a collection of 242 ultrasound scan images of ovarian masses of various pathological characteristics. The evaluation examines not only the effectiveness of classification schemes based on the individual texture features but also the effectiveness of various combinations of these schemes using the simple majority-rule decision level fusion. Trained support vector machine classifiers on the individual texture features without any specific pre-processing, achieve levels of accuracy between 75% and 85% where the seven moments and the 256-bin LBP are at the lower end while the Gabor filter is at the upper end. Combining the classification results of the top k (k = 3, 5, 7) best performing features further improve the overall accuracy to a level between 86% and 90%. These evaluation results demonstrate that each of the investigated image-based texture features provides informative support in distinguishing benign or malignant ovarian masses.

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来源期刊
Ultrasonic Imaging
Ultrasonic Imaging 医学-工程:生物医学
CiteScore
5.10
自引率
8.70%
发文量
15
审稿时长
>12 weeks
期刊介绍: Ultrasonic Imaging provides rapid publication for original and exceptional papers concerned with the development and application of ultrasonic-imaging technology. Ultrasonic Imaging publishes articles in the following areas: theoretical and experimental aspects of advanced methods and instrumentation for imaging
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