The use of computer aided diagnosis (CAD) systems, which are computer based tools for the automatic analysis of medical images such as mammogram and prostate MRI, can assist in the early detection and diagnosis of developing cancer. In the process of CAD for mammogram, the task of image processing (IP) plays a fundamental role in providing promising diagnostic results, by exploiting high-quality features extracted from the mammographic images. Normally, an IP procedure for mammographic images involves three mechanisms: region of interest (ROI) extraction, image enhancement (IE) and feature extraction (FE). However, an improper utilisation of IE may lead to an inferior composition of the features due to unexpected enhancement of any irrelevant or useless information in ROI. In order to overcome this problem, a fuzzy-rough refined IP (FRIP) framework is presented in this paper to improve the quality of mammographic image features hierarchically. Following the proposed framework, the ROI of each mammographic image is segmented and enhanced locally in the area of the block which is of the highest value of fuzzy positive region (FPR). Here, FPR implies a positive dependency relationship between the block and the decision with regard to the given feature set. The higher a block’s FPR value the more certain its underlying image category. To attain a high quality of the image enhancement procedure, the winner block will be further improved by a multi-round strategy to create a pool of IE results. As such, for a mammographic image, after embedding the candidate enhanced blocks into the original ROI, the respectively extracted features from the locally enhanced ROI are compared against each other on the basis of the value of FPR. A given image is therefore represented by a set of features which are supported by the premier FPR among all of the resulting extracted features. The quality of the extracted features by FRIP is compared against that of those directly extracted from the original images, from the globally enhanced images or from the randomly locally enhanced images in performing classification tasks. The experimental results demonstrate that the mammographic risk assessment results based on the features achieved by the proposed framework are much improved over those by the alternatives.

计算机辅助诊断（CAD）系统的使用是基于计算机的工具，用于自动分析医学图像（如乳房X线照片和前列腺MRI），可以帮助早期发现和诊断癌症。在用于乳腺X线照片的CAD过程中，图像处理（IP）的任务是通过利用从乳腺X线照片中提取的高质量特征来提供有希望的诊断结果。通常，用于乳腺X线摄影图像的IP程序涉及三种机制：关注区域（ROI）提取，图像增强（IE）和特征提取（FE）。但是，由于ROI中任何不相关或无用信息的意外增强，IE的不正确使用可能导致功能组合不良。为了克服这个问题，本文提出了一种模糊粗糙的精制IP（FRIP）框架，以提高乳腺X线图像特征的质量。按照提出的框架，每个乳腺X线摄影图像的ROI在该区域中被分割并局部增强，该区域具有最大的模糊正区域（FPR）值。在此，FPR表示针对给定功能集的块与决策之间的正相关关系。块的FPR值越高，其基础图像类别就越确定。为了获得高质量的图像增强程序，优胜者块将通过创建IE结果库的多轮策略得到进一步改善。因此，对于乳房X射线照片，在将候选增强块嵌入原始ROI之后，基于FPR的值，将从本地增强的ROI中分别提取的特征相互比较。因此，给定图像由一组特征表示，这些特征由所有最终提取的特征中的主要FPR支持。在执行分类任务时，将FRIP提取的特征的质量与直接从原始图像，全局增强的图像或随机局部增强的图像中直接提取的特征进行比较。实验结果表明，基于提议框架实现的功能的乳腺X线摄影风险评估结果比替代方案具有很大的改进。因此，给定图像由一组特征表示，这些特征由所有最终提取的特征中的主要FPR支持。在执行分类任务时，将通过FRIP提取的特征的质量与直接从原始图像，全局增强的图像或随机局部增强的图像中直接提取的特征进行比较。实验结果表明，基于提议框架实现的功能的乳腺X线摄影风险评估结果比替代方案具有很大的改进。因此，给定图像由一组特征表示，这些特征由所有最终提取的特征中的主要FPR支持。在执行分类任务时，将FRIP提取的特征的质量与直接从原始图像，全局增强的图像或随机局部增强的图像中直接提取的特征进行比较。实验结果表明，基于该框架实现的特征的乳腺X线摄影风险评估结果比替代方案的评估结果有了很大的改善。从全局增强图像或随机局部增强图像中执行分类任务。实验结果表明，基于该框架实现的特征的乳腺X线摄影风险评估结果比替代方案的评估结果有了很大的改善。从全局增强图像或随机局部增强图像中执行分类任务。实验结果表明，基于提议框架实现的功能的乳腺X线摄影风险评估结果比替代方案具有很大的改进。