基于隐马尔可夫和活动轮廓准自动初始化的乳房x线图像肿瘤自动检测

Soukaina El Idrissi El Kaitouni, A. Abbad, H. Tairi
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引用次数: 4

摘要

肿瘤的检测与切除是医学影像领域中一个非常活跃的研究领域。在目前的工作中,我们提出了一种在乳房x线摄影图像中自动检测肿瘤的方法。该方法从Otsu方法的阈值步骤开始构建检测模式,然后基于局部二值模式(LBP)技术估计类数。为了实现初始化任务的自动化,我们提出了采用马尔可夫方法改进的k-均值动态分类。肿瘤的图像是最大相关的结果。第二个贡献是基于活动轮廓梯度矢量流(GVF),对图像的结构/纹理分解产生的结构应用准自动初始化进行分类。实验结果表明,与文献方法相比,该方法在医学图像中肿瘤检测的质量和自动化程度较高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic detection of the tumour on mammogram images based on hidden Markov and active contour with quasi-automatic initialisation
The area of tumour's detection and removal is a very active research within the field of medical imaging. In the present work, we present an automatic method for tumour's detection in mammography images. The proposed approach is to construct a detection pattern which starts with the Otsu's method: the thresholding step, followed by estimating the number of classes based on the local binary pattern (LBP) technique. To automate the initialisation task, we proposed to apply the classification by the k-means dynamic improved by Markov's method. The tumour's image is the result of the maximum correlation. A second contribution which is based on active contours gradient vector flow (GVF) with quasi-automatic initialisation applied on the structure that resulted from the structure/texture decomposition of the image to classify. The experimental results show the quality and automation of tumour's detection in medical images in comparison to literature methods.
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