计算机科学 ›› 2018, Vol. 45 ›› Issue (3): 241-246.doi: 10.11896/j.issn.1002-137X.2018.03.038

所属专题: 医学图像

• 图形图像与模式识别 • 上一篇    下一篇

基于多模态局部转向核的脑部多发性硬化检测算法研究

郭杨,秦品乐   

  1. 中北大学计算机与控制工程学院 太原030051,中北大学计算机与控制工程学院 太原030051
  • 出版日期:2018-03-15 发布日期:2018-11-13

Change Detection of Multiple Sclerosis in Brain Based on Multi-modal Local Steering Kernel

GUO Yang and QIN Pin-le   

  • Online:2018-03-15 Published:2018-11-13

摘要: 容积效应和伪影现象是MR影像处理中的重要影响因素,单模态处理方法易受两者影响。提出一种改进的基于多模态局部转向核的方法来检测大脑中的多发性硬化。该方法利用多模态脑MR影像和大脑近似轴对称的先验知识来进行大脑情况的变化检测。局部转向核能够度量像素与其周围环境的相似程度,因此该方法将局部转向核作为特征,用余弦相似性来衡量差异性。实验结果表明,多模态的引入减少了容积效应和伪影现象,改善了检测效果。

关键词: 容积效应,伪影,MR影像,多模态,局部转向核,变化检测

Abstract: Volume effect and artifact are important influence factors in MR image processing and single-modal methods can be easily affected.This paper proposed an improved method based on multi-modal local steering kernel to detect the multiple sclerosis in the brain.This method utilizes multi-modal brain MR images and the approximate symmetry of the brain for change detection of the brain.Local steering kernel can measure the similarity between pixels and their surroundings.The proposed method takes the local steering kernel as the feature and measures the dissimilarity by cosine similarity.The experimental results show that the introduction of multi-modal reduces the volume effect and artifact in the MRI,improving the detection effect.

Key words: Volume effect,Artifact,MR image,Multi-modal,Local steering kernel,Change detection

[1] KIT O,LüDEKE M.Automated detection of slum area change in Hyderabad,India using multitemporal satellite imagery[J].Isprs Journal of Photogrammetry & Remote Sensing,2013,83(9):130-137.
[2] WANG Q,ATKINSON P M,SHI W.Fast Subpixel Mapping Algorithms for Subpixel Resolution Change Detection[J].IEEE Transactions on Geoscience & Remote Sensing,2014,53(4):1692-1706.
[3] TIAN J,REINARTZ P,D’ANGELO P,et al.Region-basedautomatic building and forest change detection on Cartosat-1 stereoimagery[J].Isprs Journal of Photogrammetry & Remote Sensing,2013,79(330):226-239.
[4] HAME T,HEILER I,MIGUEL-AYANZ J S.An unsupervised change detection and recognition system for forestry[J].International Journal of Remote Sensing,1998,19(6):1079-1099.
[5] LLAD X,GANILER O,OLIVER A,et al.Automated detection of multiple sclerosis lesions in serial brain MRI[J].Neuroradiology,2012,54(8):787-807.
[6] RADKE R J,ANDRA S,AL-KOFAHI O,et al.Image changedetection algorithms:a systematic survey[J].IEEE Transactions on Image Processing,2005,14(3):294-307.
[7] PEI L,REZA S M S,IFTEKHARUDDIN K M.Improved brain tumor growth prediction and segmentation in longitudinal brain MRI[C]∥IEEE International Conference on Bioinformatics and Biomedicine.IEEE,2015:421-424.
[8] MORAAL B,MEIER D S,POPPE P A,et al.Subtraction MR Images in a Multiple Sclerosis Multicenter Clinical Trial Setting[J].Radiology,2009,250(2):506-514.
[9] PECOT T,KERVRANN C.Patch-based markov models forchange detection in image sequence analysis[C]∥The International Workshop on Local and Non-Local Approximation in Ima-ge Processing.2008.
[10] SEO H J,MILANFAR P.Using local regression kernels for statistical object detection[C]∥IEEE International Conference on Image Processing.IEEE,2008:2380-2383.
[11] SEO H J,MILANFAR P.Training-free,generic object detection using locally adaptive regression kernels[J].IEEE Transactions on Pattern Analysis & Machine Intelligence,2010,32(9):1688-1704.
[12] FU Y,YAN S,HUANG T S.Correlation Metric for Generalized Feature Extraction[J].IEEE Transactions on Pattern Analysis &Machine Intelligence,2008,30(12):2229-2235.
[13] BOSC M,HEITZ F,ARMSPACH J P,et al.Automatic change detection in multimodal serial MRI:application to multiple sclerosis lesion evolution[J].Neuroimage,2003,20(2):643-656.
[14] SIMES R,SLUMP C.Change detection and classification in brain MR images using change vector analysis.[C]∥2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.2011:7803-7807.
[15] HSU Y Z,NAGEL H H,REKERS G.New likelihood testmethods for change detection in image sequences[J].Computer Vision Graphics & Image Processing,1984,26(1):73-106.
[16] MITRA A,DE A,BHATTACHARJEE A K.Detection of Progression of Lesions in MRI Using Change Detection[C]∥Proceedings of the International Conference on Frontiers of Intelligent Computing:Theory and Applications (FICTA) 2013.Springer International Publishing,2014:467-473.
[17] PATRIARCHE J W,ERICKSON B J.Automated Change Detection and Characterization in Serial MR Studies of Brain-Tumor Patients[J].Journal of Digital Imaging,2007,20(3):203-222.
[18] MA Y,LAO S,TAKIKAWA E,et al.Discriminant analysis in correlation similarity measure space[C]∥ Proceedings of the Twenty-Fourth International Conference on Machine Learning(DBLP).2007:577-584.
[19] NIKA V,BABYN P,ZHU H.Change detection of medical images using dictionary learning techniques and principal component analysis[J].Medical Imaging:Computer-aided Diagnosis,2014,5(2):024502.
[20] GONG M,ZHAO J,LIU J,et al.Change Detection in Synthetic Aperture Radar Images Based on Deep Neural Networks[J].IEEE Transactions on Image Processing,2016,4(4):2141-2151 .

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