基于一致度、覆盖度和包含度的感兴趣区域不一致性决策算法

Inconsistent decision algorithm in region of interest based on certainty degree, inclusion degree and cover degree

医学影像感兴趣区域(ROI)的噪声和疾病误判是一个典型的不一致性决策问题,同时也是困扰临床诊断的一个难题。针对这个问题,基于宏观与微观结合、全局与局部相结合的思想,提出了基于一致度、覆盖度和包含度的磁共振成像(MRI)前列腺肿瘤ROI不一致决策算法(Ito C-CIC)。首先提取MRI前列腺肿瘤ROI的高维特征,得到完备不一致决策信息表;然后通过计算不一致度找到不一致样本所在的等价类;再计算不一致等价类的覆盖度和包含度得到Score值,利用Score值筛选不一致样本,实现不一致性决策向一致性决策的转换;最后通过典型算例、UCI数据集和实验提取的前列腺肿瘤ROI特征构成的不一致决策信息表等进行验证。实验结果表明,所提算法能有效地找到并筛选掉不一致性样本。

Noisy data and disease misjudgment in Region of Interest （ROI） of medical image is a typical inconsistent decision question of Inconsistent Decision System （ IDS）, and it is becoming huge challenge in clinical diagnosis. Focusing on this problem, based on certainty degree, inclusion degree and cover degree, a decision algorithm named ItoC-CIC was proposed for ROI of prostate tumor Magnetic Resonance Imaging （MRI） combined with macro-and-micro characteristics and global-and-local characteristics. Firstly, high-dimensional features for ROI of prostate tumor MRI were extracted to construct complete inconsistent decision table. Secondly, the equivalent classes possessing inconsistent samples were found by calculating certainty degree. Thirdly, the Score value was obtained by calculating inclusion degree and cover degree of inconsistent equivalent classes respectively, which was used to filter inconsistent samples, making inconsistent decision convert to consistent decision. Finally, test experiments of inconsistent decision tables were conducted on typical examples, UCI data and 102 features of MRI prostate tumor ROI. The experimental results illustrate that this algorithm is effective and feasible, and the conversion rate can reach 100% from inconsistent decision to consistent decision.