Mammogram Image Enhancement Using Entropy and CLAHE Based Intuitionistic Fuzzy Method

Jyoti Dabass, Shaveta Arora, R. Vig, M. Hanmandlu
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引用次数: 21

Abstract

Mortality rate because of breast cancer diminishes to a large extent if the categorization of breast lesions as malignant or benign is done properly. But this process is quite complicated owing to erroneous detection of noise pixels as false positives. It can be reduced by proper enhancement of the features of the mammogram giving an indication of cancer. In this paper, contrast limited adaptive histogram equalization (CLAHE) and entropy-based intuitionistic fuzzy method are anticipated for improving the contrast of digital mammogram images. To validate the efficacy of the proposed algorithm over type II fuzzy set-based techniques, subjective, quantitative and visual evaluation is done on publicly available MIAS database. Experimental results prove that the proposed technique gives better visual quality. It provides high values of subjective and quantitative metrics compared to several states of art algorithms.
基于熵和CLAHE的乳房x线图像增强直觉模糊方法
如果正确地将乳腺病变分类为恶性或良性,乳腺癌的死亡率就会大大降低。但由于噪声像素被错误地检测为假阳性,这一过程相当复杂。它可以通过适当增强乳房x光片的特征来减少癌症的迹象。本文提出了对比度限制自适应直方图均衡化(CLAHE)和基于熵的直觉模糊方法来提高数字乳房x线图像的对比度。为了验证所提出的算法优于基于II型模糊集的技术,在公开的MIAS数据库上进行了主观、定量和视觉评价。实验结果表明,该方法具有较好的视觉效果。与一些先进的算法相比,它提供了很高的主观和定量度量值。
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