基于类间方差的乳房x光图像阈值分割,使用混合的伽玛分布

A. A. Ghosn, A. El-Zaart, E. Assidan
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引用次数: 3

摘要

乳腺癌是妇女中最常见的恶性肿瘤,每年在世界上有100万新病例。事实证明,对这种疾病的早期诊断有助于大大提高生存的预期。乳房x线摄影是发现无可触及的早期乳腺癌最有效的成像方法。图像处理技术已被用于处理乳房x光片图像。图像阈值分割是物体分割和识别领域的一个重要概念。它由于实现简单和执行速度快而被广泛使用。文献中提出了许多阈值处理技术。本文的目的是利用混合伽玛分布的类间方差为阈值图像提供公式及其实现。这些算法将通过给出它们的步骤和应用来描述。实验结果表明,该方法对乳房x线图像的分割效果良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mammogram images thresholding based on between-class variance using a mixture of gamma distributions
With one million new cases in the world every year, breast cancer is the most common malignancy in women and it has been proved that an early diagnosis of the disease can help to strongly enhance the expectancy of survival. Mammography is the most effective imaging method for detecting no-palpable early-stage breast cancer. Image processing techniques has been used for processing the mammogram image. Image thresholding is an important concept, both in the area of objects segmentation and recognition. It has been widely used due to the simplicity of implementation and speed of time execution. Many thresholding techniques have been proposed in the literature. The aim of this paper is to provide formula and their implementation to threshold images using Between-Class Variance with a Mixture of Gamma Distributions. The algorithms will be described by given their steps, and applications. Experimental results are presented to show good results on segmentation of mammogram image.
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