计算机科学 ›› 2017, Vol. 44 ›› Issue (10): 45-50.doi: 10.11896/j.issn.1002-137X.2017.10.008

• 生物信息学 • 上一篇    下一篇

基于一种改进的LBP算法和超限学习机的肝硬化识别

雷一鸣,赵希梅,王国栋,于可歆   

  1. 青岛大学计算机科学技术学院 青岛266071,青岛大学计算机科学技术学院 青岛266071;山东省数字医学与计算机辅助手术重点实验室 青岛266000,青岛大学计算机科学技术学院 青岛266071,加州大学洛杉矶分校 洛杉矶90015
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金项目:计算机辅助肝纤维化无创诊断(61303079),国家自然科学基金项目:空变运动模糊图像的盲复原变分模型及其快速算法(61305045)资助

Cirrhosis Recognition Based on Improved LBP Algorithm and Extreme Learning Machine

LEI Yi-ming, ZHAO Xi-mei, WANG Guo-dong and YU Ke-xin   

  • Online:2018-12-01 Published:2018-12-01

摘要: 肝硬化的计算机辅助诊断对肝脏疾病的早期治疗和诊断具有重要意义。针对B超图像中肝硬化病变区域边缘模糊和回声不均匀、尺度因素影响等问题,提出了改进的LBP算法并提取了相应的SLBP特征。该特征较传统的纹理特征更准确地描述了B超图像中肝硬化病变的特征,结合二维Gabor变换,解决了上述难题。鉴于传统的机器学习方法的训练时间较长,采用基于超限学习机的训练方法,并首次将其应用于肝硬化识别。实验结果表明,所提方法对测试集的分类准确率达到95.4%,在时间效率上较传统方法有很大提高。ROC曲线表明,提出的分类方法在准确率和泛化能力上均优于传统方法,有助于肝硬化的临床诊断。

关键词: 肝硬化,超限学习机,改进的LBP算法,SLBP特征,Gabor变换,ROC曲线

Abstract: Computer aided diagnosis of cirrhoisis has great meaning for the early treatment and diagnosis of liver di-sease.For the issues that edge blurring and nonuniform of echo in cirrhosis lesion area and influence of scale factor in B-mode ultrasound images,we proposed an improved LBP algorithm and extracted the corresponding SLBP feature which depicts the lesion area of cirrhosis more precisely than traditional texture features.Through the combination of SLBP and two-dimensional Gabor transform,we solved the difficulties above.Due to the long training time of conventional machine learning methods,we adopted extreme learning machine based method and firstly applied it in cirrhosis recognition.Experimental results show that classification accuracy on test set reaches 95.4%,and time efficiency has further improved compared with traditional method.The comparison between the proposed method and conventional methods,via ROC(Receiver Operating Characteristic) curve,demonstrates that the proposed method possesses the advantages both in accuracy and generalization performance.The proposed method will be helpful for clinical diagnosis of cirrhosis.

Key words: Cirrhosis,Extreme learning machine,Improved LBP algorithm,SLBP feature,Gabor transform,ROC curve

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