在 WCE 图像中进行溃疡分类/检测的稳健方法

A. Dahmouni, Abdelkaher Ait Abdelouahad, Yasser Aderghal, Ibrahim Guelzim, I. Bellamine, H. Silkan
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引用次数: 0

摘要

无线胶囊内窥镜检查(WCE)是一种医学诊断技术,因其微创和无痛的特性而得到广泛认可。它利用远程成像技术探索胃肠道(GI)的各个部分,尤其是难以触及的小肠,是传统内窥镜技术的有效替代品。然而,由于需要花费大量的精力和时间,医生在分析大量内窥镜图像时面临着巨大的挑战。因此,当务之急是实施能够自动检测可疑区域并进行后续医学评估的辅助诊断系统。在本文中,我们提出了一种从 WCE 图像中识别胃肠道异常的新方法,尤其侧重于溃疡区域。我们的方法涉及使用中值稳健扩展局部二进制模式(MRELBP)描述符,它能有效克服 WCE 图像采集时面临的挑战,如光照和对比度变化、旋转和噪声。利用机器学习算法,我们在广泛的 Kvasir-Capsule 数据集上进行了实验,随后将我们的结果与最近的相关研究进行了比较。值得注意的是,我们的方法在使用 SVM(RBF)分类器时达到了 97.04% 的准确率,在使用 RF 分类器时达到了 96.77% 的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Robust Approach for Ulcer Classification/Detection in WCE Images
Wireless Capsule Endoscopy (WCE) is a medical diagnostic technique recognized for its minimally invasive and painless nature for the patients. It uses remote imaging techniques to explore various segments of the gastrointestinal (GI) tract, particularly the hard-to-reach small intestine, making it an effective alternative to traditional endoscopic techniques. However, physicians face a significant challenge when it comes to analyzing a large number of endoscopic images due to the effort and time required. It is therefore imperative to implement aided-diagnostic systems capable of automatically detecting suspicious areas for subsequent medical assessment. In this paper, we present a novel approach to identify gastrointestinal tract abnormalities from WCE images, with a particular focus on ulcerated areas. Our approach involves the use of the Median Robust Extended Local Binary Pattern (MRELBP) descriptor, which effectively overcomes the challenges faced when WCE image acquisition, such as variations in illumination and contrast, rotation, and noise. Using machine learning algorithms, we conducted experiments on the extensive Kvasir-Capsule dataset, and subsequently compared our results with recent relevant studies. Noteworthy is the fact that our approach achieved an accuracy of 97.04% with the SVM (RBF) classifier and 96.77% with the RF classifier.
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