Medical images often suffer from low contrast, irregular gray-level spacing and contain a lot of uncertainties due to constraints of imaging devices and environment (various lighting conditions) when capturing images. In order to achieve any clinical-diagnosis method for medical imaging with better comprehensibility, image contrast enhancement algorithms would be appropriate to improve the visual quality of medical images. In this paper, an automated image enhancement method is presented for colonoscopy images based on the intuitionistic fuzzy soft set. The fuzzy soft set is used to model the intuitionistic fuzzy soft image matrix based on a set of soft features of the colonoscopy images. The technique decomposes the fuzzy image into multiple blocks and estimates a soft-score based on an adaptive soft parametric hesitancy map by using the hesitant entropy for each block to quantify the uncertainties. In the processing stage, an adaptive intensity modification process is done for each block according to its soft-score. These scores are accurately addressed the gray-level ambiguities in colonoscopy images that lead to better results. Finally, the enhanced image achieved by performing a defuzzification together with all unprocessed blocks. Qualitative and quantitative assessments demonstrate that the proposed method improves image contrast and region-of-interest of polyps in colonogram. Experimental results on enhancing a large CVC-Clinic-DB and ASU-Mayo clinic colonoscopy benchmark datasets show that the proposed method outperforms the state-of-the-art medical image enhancement methods.

医学图像通常会遇到对比度低，灰度间距不规则的问题，并且由于在捕获图像时成像设备和环境（各种照明条件）的限制，因此存在很多不确定性。为了实现具有更好的可理解性的用于医学成像的任何临床诊断方法，图像对比度增强算法将适合于改善医学图像的视觉质量。本文提出了一种基于直觉模糊软集的结肠镜检查图像自动增强方法。模糊软集用于基于结肠镜检查图像的一组软特征对直觉模糊软图像矩阵建模。该技术将模糊图像分解为多个块，并通过使用每个块的犹豫熵来量化不确定性，基于自适应软参数犹豫图来估计软分数。在处理阶段，根据每个块的软得分对它们进行自适应强度修改处理。这些分数可以准确解决结肠镜检查图像中的灰度歧义，从而获得更好的结果。最后，通过对所有未处理的块执行去模糊处理来获得增强的图像。定性和定量评估表明，该方法可改善结肠造影中息肉的图像对比度和关注区域。