Department of Electronics and Communication Engineering, PSNA College of Engineering and Technology, Dindigul, Tamilnadu, 624622, India.
J Med Syst. 2020 Apr 19;44(6):103. doi: 10.1007/s10916-020-01568-9.
Contrast enhancement methods are used to reduce image noise and increase the contrast of structures of interest. In medical images where the distinction between normal and abnormal tissue is subtle, accurate interpretation may become difficult if noise levels are relatively high. To provide accurate interpretation and clearer image for the observer with reduced noise levels "a novel adaptive fuzzy gray level difference histogram equalization algorithm" is proposed. At first, gray level difference of an input image is calculated using the binary similar patterns. Then, the gray level differences are fuzzified in order to deal the uncertainties present in the input image. Following the fuzzification, fuzzy gray level difference clip limit is computed to control the insignificant contrast enhancement. Finally, a fuzzy clipped histogram is equalized to obtain the contrast-enhanced MR medical image. The proposed algorithm is analysed both visually and analytically to calculate its performance against the other existing algorithms. Visual and analytical results on various test images affirm that the proposed algorithm outperforms all other existing algorithms and provide a clear path to analyse the fine details and infected portions effectively.
对比增强方法用于降低图像噪声并提高感兴趣结构的对比度。在医学图像中，如果正常组织和异常组织之间的区别很细微，那么如果噪声水平相对较高，准确的解释可能会变得困难。为了在降低噪声水平的情况下为观察者提供准确的解释和更清晰的图像，提出了一种新的自适应模糊灰度差分直方图均衡算法。首先，使用二进制相似模式计算输入图像的灰度差分。然后，对灰度差分进行模糊化，以处理输入图像中存在的不确定性。模糊化后，计算模糊灰度差分剪辑限制以控制不重要的对比度增强。最后，均衡模糊剪辑直方图以获得对比度增强的磁共振医学图像。对各种测试图像进行了视觉和分析分析，以计算其相对于其他现有算法的性能。视觉和分析结果表明，该算法优于所有其他现有算法，并为有效分析细节和受感染部分提供了清晰的途径。
