基于数学形态学的医学图像形态学特征增强方法。

Yoshitaka Kimori
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引用次数: 68

摘要

背景:医学图像处理在医学研究和临床实践的许多领域都是必不可少的,因为它极大地促进了疾病的早期和准确的发现和诊断。特别是,对比度增强对于获得最佳图像质量和可见性非常重要。本文提出了一种新的图像处理方法,用于增强医学图像中肿块和其他异常的形态学特征。方法:该方法分为两个步骤:(1)通过数学形态学对目标特征进行选择性提取;(2)通过两种对比度修饰技术对提取的特征进行增强。结果:提出的方法的目标是增强病变区域的精细形态学特征,同时高度抑制周围组织。该方法的有效性以对比改善率为定量指标进行评价。结果清楚地表明,该方法优于五种传统的对比度增强方法。通过应用于三种类型的医学图像:乳房x线摄影图像、胸部x线摄影图像和视网膜图像,进一步证明了所提出方法的有效性和实用性。结论:该方法可实现肿块病灶的特异性提取和增强,为临床基于医学图像分析的诊断提供必要依据。因此,该方法有望实现病灶位置的自动识别和军团形态的定量分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Mathematical morphology-based approach to the enhancement of morphological features in medical images.

Mathematical morphology-based approach to the enhancement of morphological features in medical images.

Mathematical morphology-based approach to the enhancement of morphological features in medical images.

Mathematical morphology-based approach to the enhancement of morphological features in medical images.

Background: Medical image processing is essential in many fields of medical research and clinical practice because it greatly facilitates early and accurate detection and diagnosis of diseases. In particular, contrast enhancement is important for optimal image quality and visibility. This paper proposes a new image processing method for enhancing morphological features of masses and other abnormalities in medical images.

Method: The proposed method involves two steps: (1) selective extraction of target features by mathematical morphology and (2) enhancement of the extracted features by two contrast modification techniques.

Results: The goal of the proposed method is to enable enhancement of fine morphological features of a lesion region with high suppression of surrounding tissues. The effectiveness of the method was evaluated in quantitative terms of the contrast improvement ratio. The results clearly show that the method outperforms five conventional contrast enhancement methods. The effectiveness and usefulness of the proposed method were further demonstrated by application to three types of medical images: a mammographic image, a chest radiographic image, and a retinal image.

Conclusion: The proposed method enables specific extraction and enhancement of mass lesions, which is essential for clinical diagnosis based on medical image analysis. Thus, the method can be expected to achieve automatic recognition of lesion location and quantitative analysis of legion morphology.

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