This article represents a colonogram enhancement approach using intuitionistic fuzzy set (IFS) and fuzzy soft set (FSS) to improve the visual quality and highlight the local details in enhanced images without artifacts. The proposed method has the advantages of IFSs and fuzzy soft sets (FSSs) which can deal with gray-level ambiguity in colonoscopy images. The combination of IFS and FSS works on intricate color variations with an adaptive parametric maps. First, the S-membership function has been applied on the images to map into intuitionistic fuzzy space. We obtain intuitionistic fuzzy image from source image followed by decomposition of several blocks. Thereafter, the FSS is employed to get subsequent intuitionistic fuzzy soft matrix from individual image block. The FSS utilizes a class of parametric coefficients to obtain the hesitant score of individual pixels in each block through the soft-score measure (SSM). Finally, the proposed method intensifies each membership degree in all blocks in the fuzzy plane using SSM and achieves an enhanced colonogram via defuzzification. Extensive experiment involving large data sets shows that the designed method exhibits better performance in contrast enhancement and visual quality improvement of colonograms for malformation detection (such as a polyps, malignant precancerous cells) in comparison with the state-of-art methods.

中文翻译：

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一种新的基于直觉模糊软集的结肠息肉定位增强

本文介绍了一种使用直觉模糊集 (IFS) 和模糊软集 (FSS) 的结肠图增强方法，以提高视觉质量并突出增强图像中的局部细节而没有伪影。所提出的方法具有IFSs和模糊软集（FSSs）的优点，可以处理结肠镜图像中的灰度模糊。IFS 和 FSS 的组合使用自适应参数贴图处理复杂的颜色变化。首先，S-membership 函数已应用于图像以映射到直觉模糊空间。我们从源图像中获得直观的模糊图像，然后分解几个块。此后，使用FSS从单个图像块中获得后续的直觉模糊软矩阵。FSS利用一类参数系数通过软评分度量（SSM）获得每个块中单个像素的犹豫分数。最后，所提出的方法使用 SSM 加强模糊平面中所有块的每个隶属度，并通过去模糊化实现增强的结肠图。涉及大数据集的大量实验表明，与最先进的方法相比，所设计的方法在用于畸形检测（如息肉、恶性癌前细胞）的结肠图的对比度增强和视觉质量改善方面表现出更好的性能。