MUSCLE TYPE AND GENDER RECOGNITION UTILISING HIGH-LEVEL TEXTURAL REPRESENTATION IN MUSCULOSKELETAL ULTRASONOGRAPHY
MUSCLE TYPE AND GENDER RECOGNITION UTILISING HIGH-LEVEL TEXTURAL REPRESENTATION IN MUSCULOSKELETAL ULTRASONOGRAPHY
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DOI：
10.1016/j.ultrasmedbio.2019.02.011
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发表时间：
2019-07-01
影响因子：
2.9
通讯作者：
Panayiotakis, George
中科院分区：
文献类型：
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作者：
Katakis, Sofoklis;Barotsis, Nikolaos;Panayiotakis, George
人类辅助技术和计算机辅助诊断是医学成像领域的一个新兴领域。随着该领域的最新进展，开展了一项将机器学习技术集成到肌肉骨骼超声图像中的研究。本次尝试的目的是研究捕获更高层次信息的特征提取技术在识别人类特征方面的表现。这些技术的潜在成功可能会导致当前评估方法（如灰度图像分析）的显着改进，用于区分健康和病理状况，这些方法严重依赖于图像采集系统。这项工作的贡献是三重的。首先，提出了包含 74 名健康患者的新的私人数据集。该数据集包括人体四块肌肉的肌肉骨骼超声图像，即肱二头肌、胫骨前肌、腓肠肌内侧肌和股直肌，记录在横向和纵向平面上。其次，执行了两个分类任务，即性别和肌肉类型识别，以评估所提出的方法成功识别所检查肌肉部分纹理差异的性能。第三，提出了一种在计算机视觉领域取得巨大成功的新方法，通过对局部不变纹理描述符的分布进行编码，可以提取高级特征表示。在肌肉类型识别上，我们的方法实现了 87.07% 的分类率，在性别识别任务上，它超越了文献中几乎所有检查的肌肉部分中最先进的纹理表示。 (C) 2019 年世界超声医学与生物学联合会。版权所有。
Human assistive technology and computer-aided diagnosis is an emerging field in the area of medical imaging. Following the recent advances in this domain, a study for integrating machine learning techniques in musculoskeletal ultrasonography images was conducted. The goal of this attempt was to investigate how feature extraction techniques, that capture higher-level information, perform in identifying human characteristics. The potential success of these techniques could lead to significant improvement of the current assessment methods-as the gray-scale image analysis-for distinguishing healthy and pathologic conditions, that are heavily dependent on the image-acquisition system. The contribution of this work is threefold. First, a new privately held data set of 74 healthy patients was presented. This data set included musculoskeletal ultrasound images from four muscles of the human body, namely the biceps brachii, tibialis anterior, gastrocnemius medialis and rectus femoris, recorded in the transverse and longitudinal plane. Second, two classification tasks were performed, namely, gender and muscle-type recognition, to assess the performance of the proposed method for successfully identifying differences in the texture of the examined muscle sections. Third, a novel method used with great success in the computer vision domain was presented, allowing the extraction of a high-level feature representation, by encoding the distribution of locally invariant texture descriptors. On the muscle-type recognition our method achieved an 87.07% classification rate, and on the task of gender recognition it surpassed state-of-the-art textural representations, reported in the literature in almost all the examined muscle sections. (C) 2019 World Federation for Ultrasound in Medicine & Biology. All rights reserved.