Journal of Computer Applications ›› 2015, Vol. 35 ›› Issue (10): 2803-2807.DOI: 10.11772/j.issn.1001-9081.2015.10.2803

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

ZHOU Tao1, LU Huiling1, MA Miao2, YANG Pengfei3   

  1. 1. School of Science, Ningxia Medical University, Yingchuan Ningxia 750004, China;
    2. College of Computer Science, Shaanxi Normal University, Xi'an Shaanxi 710062, China;
    3. Department of Nuclear Medicine, General Hospital of Ningxia Medical University, Yinchuan Ningxia 750004, China
  • Received:2015-06-01 Revised:2015-06-19 Online:2015-10-10 Published:2015-10-14

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

周涛1, 陆惠玲1, 马苗2, 杨鹏飞3   

  1. 1. 宁夏医科大学 理学院, 银川 750004;
    2. 陕西师范大学 计算机学院, 西安 710072;
    3. 宁夏医科大学总医院 核医学科, 银川 750004
  • 通讯作者: 周涛(1977-),男,宁夏同心人,教授,博士,主要研究方向:基于影像的计算机辅助诊断、医学大数据分析、智能算法,zhoutaonxmu@126.com
  • 作者简介:陆惠玲(1976-),女,河北定兴人,副教授,主要研究方向:医学图像分析与处理、智能算法;马苗(1977-),女,山东聊城人,教授,博士,主要研究方向:图像处理、智能算法;杨鹏飞(1982-),男,湖北恩施人,工程师,硕士生,主要研究方向:PET影像处理、SPECT影像处理。
  • 基金资助:
    国家自然科学基金资助项目(81160183,61561040);宁夏自然科学基金资助项目(NZ12179,NZ14085);宁夏高校科研项目(NGY2013062);陕西省语音与图像信息处理重点实验室开放课题资助项目(SJ2013003);宁夏医科大学特殊人才项目(XT2011004)。

Abstract: 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.

Key words: prostate tumor, Region of Interest (ROI), inconsistent decision, certainty degree, inclusion degree, cover degree

摘要: 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(ItoC-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。

关键词: 前列腺肿瘤, 感兴趣区域, 不一致性决策, 一致度, 覆盖度, 包含度

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