A curvelet-based lacunarity approach for ulcer detection from Wireless Capsule Endoscopy images

Alexis Eid, V. Charisis, L. Hadjileontiadis, G. Sergiadis
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引用次数: 36

Abstract

Wireless Capsule Endoscopy (WCE) is a fairly new technology that offers a low-risk, non invasive visual inspection of the patient's digestive tract, especially the small bowel, that was previously unreachable using the traditional endoscopic methods. However, the large amount of images produced by WCE requires a highly trained physician to manually inspect them; a procedure that is time consuming and prone to human error. This was the rationale to propose a novel strategy for automatic detection of WCE images related to ulcer, one of the most common findings of the digestive tract. This paper introduces a new texture extraction method based on the Discrete Curvelet Transform (DCT), a recent multi-resolution analysis tool. Textural information is acquired by calculating the lacunarity index of DCT subbands of the WCE images. The classification step is performed by a Support Vector Machine (SVM), demonstrating promising classification accuracy (86.5%) and pointing towards further research in this field.
无线胶囊内窥镜图像中基于曲线的间隙检测方法
无线胶囊内窥镜(WCE)是一项相当新的技术,它提供了一种低风险、无创的对患者消化道,特别是小肠的视觉检查,这是以前使用传统内窥镜方法无法达到的。然而,WCE产生的大量图像需要训练有素的医生手工检查;耗时且容易出现人为错误的过程。这是提出一种自动检测与溃疡相关的WCE图像的新策略的基本原理,溃疡是消化道最常见的发现之一。本文介绍了一种基于离散曲线变换(DCT)的纹理提取方法。通过计算WCE图像的DCT子带的间隙指数来获取纹理信息。分类步骤由支持向量机(SVM)执行,显示出良好的分类准确率(86.5%),为该领域的进一步研究指明了方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
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