首页> 外文会议>Computational Intelligence for Image Processing, 2009. CIIP '09 >Adaptive λ-enhancement: Type I versus type II fuzzy implementation
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Adaptive λ-enhancement: Type I versus type II fuzzy implementation

机译:自适应λ增强:I型与II型模糊实现

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lambda-enhancement, introduced by Tizhoosh et al., is a contrast adjustment technique that uses involutive fuzzy complements to find the best gray-level transformation in order to increase the image contrast. Applied on medical images, lambda-enhancement can provide good results with respect to visually perceived improvement of object-background discrimination. In this work, we provide two extensions of lambda-enhancement. First we extend it to employ interval-valued fuzzy sets (special case of type II fuzzy sets), and second, we provide an adaptive version of both regular (type I) and interval-value (type II) fuzzy lambda-enhancement. Using breast ultrasound images, we demonstrate the enhancement effect and compare them with the well-established CLAHE method (contrast-limited adaptive histogram equalization).
机译:Tizhoosh等人介绍的lambda增强技术是一种对比度调整技术,该技术使用渐进模糊补码来找到最佳灰度转换,以提高图像对比度。应用于医学图像上,λ增强可以在视觉上改善物体背景辨别力方面提供良好的结果。在这项工作中,我们提供了lambda增强的两个扩展。首先,我们将其扩展为采用区间值模糊集(II型模糊集的特殊情况),其次,我们提供了常规(I型)和区间值(II型)模糊lambda增强的自适应版本。使用乳房超声图像,我们演示了增强效果,并将其与公认的CLAHE方法(对比度受限的自适应直方图均衡化)进行比较。

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