基于ABUS冠状面图像的乳头位置自动检测算法
|
摘要
乳腺疾病发生在以乳头为中心划分的区域中,乳头的准确定位对乳腺疾病的诊断具有重要的临床意义.提出一种基于自动化三维乳腺超声(ABUS)冠状面图像的乳头位置自动检测算法.首先采用图像掩模提取感兴趣区域(ROI);然后对ROI进行图像预处理操作以提高目标区域识别的精确度和运算效率;最后通过霍夫变换圆检测和排除误判圆操作获得乳头的圆心坐标和半径.结果表明:本算法能有效检测到乳头位置,且检测准确率可达94.7%.
Breast disease occurs in the divided area with the nipple as the center. Accurate positioning of the nipple has important clinical value for the diagnosis of breast disease. In this paper, an algorithm for the nipple location automatic detection is proposed based on automated 3 D breast ultrasound(ABUS) coronal images. Firstly,a mask is applied to find the region of interest(ROI). Then, the image pre-processing operations are used to improve the accuracy and efficiency of the target area identification. Finally, central coordinate and radius of the nipple are obtained by using the circle Hough transform and the exclusion of misjudgment circles. It is shown in the results that the proposed algorithm can effectively detect the location of the nipple, and the detection accuracy can achieve 94.7%.
Breast disease occurs in the divided area with the nipple as the center. Accurate positioning of the nipple has important clinical value for the diagnosis of breast disease. In this paper, an algorithm for the nipple location automatic detection is proposed based on automated 3 D breast ultrasound(ABUS) coronal images. Firstly,a mask is applied to find the region of interest(ROI). Then, the image pre-processing operations are used to improve the accuracy and efficiency of the target area identification. Finally, central coordinate and radius of the nipple are obtained by using the circle Hough transform and the exclusion of misjudgment circles. It is shown in the results that the proposed algorithm can effectively detect the location of the nipple, and the detection accuracy can achieve 94.7%.
引文
[1]武广海,王助新,张杰.肿瘤所在不同象限乳腺癌行前哨淋巴结活检的临床研究[J].临床和实验医学杂志,2015(5):388-391.DOI:10.3969/j.issn.1671-4695.2015.05.018.Wu G H,Wang ZH X,Zhang J.Clinical study on the sentinel lymph node biopsy of breast cancer in different quadrants[J].Journal of Clinical and Experimental Medicine,2015(5):388-391.
[2]Zheng F Y,Lu Q,Huang B J,et al.Imaging features of automated breast volume scanner:Correlation with molecular subtypes of breast cancer[J].European Journal of Radiology,2017,86:267-275.DOI:1 0.1016/j.ejrad.2016.11.032.
[3]Wohrle N K,Hellerhof K,Notohamiprodjo M,et al.Automated breast volume scanner(ABVS)[J].Der Onkologe,2011,17(5):364-376.DOI:10.1007/s00761-011-2040-x.
[4]颜光前,赵柳,吴俊,等.基于ABUS图像的轻量型切口疝补片计算机辅助检测与评估算法[J].云南大学学报:自然科学版,2017,39(5):768-779.Yan G Q,Zhao L,Wu J,et al.Computer-aided detection and evaluation algorithm of lightweight incisional hernia mesh on ABUS images[J].Journal of Yunnan University:Natural Sciences Edition,2017,39(5):768-779.
[5]闵一迪,吴俊,汪源源,等.三维ABUS图像多平面联合显示平台的Matlab实现方法[J].国外电子测量技术,2017,36(2):73-77.DOI:10.3969/j.issn.1002-8978.2017.02.015.Min Y D,Wu J,Wang Y Y,et al.Matlab implementation scheme of multiplanar visualization platform for3D ABUS images[J].Foreign Electronic Measurement Technology,2017,36(2):73-77.
[6]Moghaddam M J,Tan T,Karssemeijer N,et al.Automatic nipple detection on 3D images of an automated breast ultrasound system(ABUS)[C].Proc SPIE Medical Ima- ging 2014,903405.DOI:10.1117/12/2043780.
[7]Petroudi S,Brady M.Automatic nipple detection on mammograms[J].Lecture Notes in Computer Science,2003,2879:971-972.DOI:10.1007/b93811.
[8]Abdel-Nasser M,Saleh A,Moreno A,et al.Automatic nipple detection in breast thermograms[J].Expert Systems with Applications,2016,64(C):365-374.
[9]Tan T,Platel B,Huisman H,et al.Computer-aided lesion diagnosis in automated 3-D breast ultrasound using coronal spiculation[J].IEEE Transactions on Medical Imaging,2012,31(5):1 034-1 042.DOI:10.1109/TMI.2012.2184549.
[10]李攀,黄黎青,宋允东.基于掩膜预处理的稀疏表示和压缩感知图像重建[J].电子测量技术,2015(8):79-81.DOI:10.3969/j.issn.1002-7300.2015.08.019.Li P,Huang L Q,Song Y D.The reconstruction of compressive sensing and sparse representation based mask pretreatment[J].Electronic Measurement Technology,2015(8):79-81.
[11]Otsu N.A threshold selection method from gray-level histograms[J].IEEE Transactions on Systems,Man,and Cybernetics,1979,9(1):62-66.DOI:10.1109/TS-MC.1979.4310076.
[12]赵柳,吴俊,陈悦,等.自动化三维超声图像无探头耦合区域的计算机辅助排除算法[J].电子测量技术,2017,40(7):113-116.DOI:10.3969/j.issn.1002-7300.2017.07.025.Zhao L,Wu J,Chen Y,et al.Computer-aided exclusion algorithm for areas without probe coupling of automated 3D ultrasound images[J].Electronic Measurement Technology,2017,40(7):113-116.
[13]Yang J,Fan J,Ai D,et al.Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image[J].Neurocomputing,2016,195(C):88-95.
[14]Noble J A.Reflections on ultrasound image analysis[J].Medical Image Analysis,2016,33:33.DOI:10.1016/j.media.2016.06.015.
[15]张年,钱盛友,谭乔来,等.基于SURF算法的HIFU治疗前后超声图像配准研究[J].电子测量与仪器学报,2016,30(6):917-922.Zhang N,Qian S Y,Tan Q L,et al.Study on ultrasonic image registration before and after HIFU treatment based on SURF algorithm[J].Journal of Electronic Measurement and Instrumentation,2016,30(6):917-922.
[16]Wu J,Wang Y Y,Yu J H,et al.Intelligent speckle reducing anisotropic diffusion algorithm for automated 3-D ultrasound images[J].Journal of the Optical Society of America A Optics Image Science&Vision,2015,32(2):248-257.
[17]吴俊,汪源源,陈悦,等.基于同质区域自动选取的各向异性扩散超声图像去噪[J].光学精密工程,2014,22(5):1 312-1 321.Wu J,Wang Y Y,Chen Y,et al.Speckle reduction of ultrasound images with anisotropic diffusion based on homogeneous region automatic selection[J].Optics and Precision Engineering,2014,22(5):1 312-1 321.
[18]Spratling M W.A neural implementation of the Hough transform and the advantages of explaining away[J].Image&Vision Computing,2016,52:15-24.
[19]何扬名,戴曙光.提高霍夫变换识别圆形物体准确率的算法[J].微计算机信息,2009,25(4):279-281.He Y M,Dai S G.An algorithm for improving the precision of detecting round object by Hough Transform[J].Microcomputer Information,2009,25(4):279-281.
[20]赵振兵,徐磊,戚银城,等.基于Hough检测和C-V模型的航拍绝缘子自动协同分割方法[J].仪器仪表学报,2016,37(2):395-403.DOI:10.3969/j.issn.0254-3087.2016.02.021.Zhao Z B,Xu L,Qi Y CH,et al.Automatic co-segmentation method for aerial insulator based on Hough detection and C-V model[J].Chinese Journal of Scientific Instrument,2016,37(2):395-403.
[21]Smerek K M,Duleba I.Circular object detection using a modified Hough transform[J].International Journal of Applied Mathematics&Computer Science,2008,18(1):85-91.
[2]Zheng F Y,Lu Q,Huang B J,et al.Imaging features of automated breast volume scanner:Correlation with molecular subtypes of breast cancer[J].European Journal of Radiology,2017,86:267-275.DOI:1 0.1016/j.ejrad.2016.11.032.
[3]Wohrle N K,Hellerhof K,Notohamiprodjo M,et al.Automated breast volume scanner(ABVS)[J].Der Onkologe,2011,17(5):364-376.DOI:10.1007/s00761-011-2040-x.
[4]颜光前,赵柳,吴俊,等.基于ABUS图像的轻量型切口疝补片计算机辅助检测与评估算法[J].云南大学学报:自然科学版,2017,39(5):768-779.Yan G Q,Zhao L,Wu J,et al.Computer-aided detection and evaluation algorithm of lightweight incisional hernia mesh on ABUS images[J].Journal of Yunnan University:Natural Sciences Edition,2017,39(5):768-779.
[5]闵一迪,吴俊,汪源源,等.三维ABUS图像多平面联合显示平台的Matlab实现方法[J].国外电子测量技术,2017,36(2):73-77.DOI:10.3969/j.issn.1002-8978.2017.02.015.Min Y D,Wu J,Wang Y Y,et al.Matlab implementation scheme of multiplanar visualization platform for3D ABUS images[J].Foreign Electronic Measurement Technology,2017,36(2):73-77.
[6]Moghaddam M J,Tan T,Karssemeijer N,et al.Automatic nipple detection on 3D images of an automated breast ultrasound system(ABUS)[C].Proc SPIE Medical Ima- ging 2014,903405.DOI:10.1117/12/2043780.
[7]Petroudi S,Brady M.Automatic nipple detection on mammograms[J].Lecture Notes in Computer Science,2003,2879:971-972.DOI:10.1007/b93811.
[8]Abdel-Nasser M,Saleh A,Moreno A,et al.Automatic nipple detection in breast thermograms[J].Expert Systems with Applications,2016,64(C):365-374.
[9]Tan T,Platel B,Huisman H,et al.Computer-aided lesion diagnosis in automated 3-D breast ultrasound using coronal spiculation[J].IEEE Transactions on Medical Imaging,2012,31(5):1 034-1 042.DOI:10.1109/TMI.2012.2184549.
[10]李攀,黄黎青,宋允东.基于掩膜预处理的稀疏表示和压缩感知图像重建[J].电子测量技术,2015(8):79-81.DOI:10.3969/j.issn.1002-7300.2015.08.019.Li P,Huang L Q,Song Y D.The reconstruction of compressive sensing and sparse representation based mask pretreatment[J].Electronic Measurement Technology,2015(8):79-81.
[11]Otsu N.A threshold selection method from gray-level histograms[J].IEEE Transactions on Systems,Man,and Cybernetics,1979,9(1):62-66.DOI:10.1109/TS-MC.1979.4310076.
[12]赵柳,吴俊,陈悦,等.自动化三维超声图像无探头耦合区域的计算机辅助排除算法[J].电子测量技术,2017,40(7):113-116.DOI:10.3969/j.issn.1002-7300.2017.07.025.Zhao L,Wu J,Chen Y,et al.Computer-aided exclusion algorithm for areas without probe coupling of automated 3D ultrasound images[J].Electronic Measurement Technology,2017,40(7):113-116.
[13]Yang J,Fan J,Ai D,et al.Local statistics and non-local mean filter for speckle noise reduction in medical ultrasound image[J].Neurocomputing,2016,195(C):88-95.
[14]Noble J A.Reflections on ultrasound image analysis[J].Medical Image Analysis,2016,33:33.DOI:10.1016/j.media.2016.06.015.
[15]张年,钱盛友,谭乔来,等.基于SURF算法的HIFU治疗前后超声图像配准研究[J].电子测量与仪器学报,2016,30(6):917-922.Zhang N,Qian S Y,Tan Q L,et al.Study on ultrasonic image registration before and after HIFU treatment based on SURF algorithm[J].Journal of Electronic Measurement and Instrumentation,2016,30(6):917-922.
[16]Wu J,Wang Y Y,Yu J H,et al.Intelligent speckle reducing anisotropic diffusion algorithm for automated 3-D ultrasound images[J].Journal of the Optical Society of America A Optics Image Science&Vision,2015,32(2):248-257.
[17]吴俊,汪源源,陈悦,等.基于同质区域自动选取的各向异性扩散超声图像去噪[J].光学精密工程,2014,22(5):1 312-1 321.Wu J,Wang Y Y,Chen Y,et al.Speckle reduction of ultrasound images with anisotropic diffusion based on homogeneous region automatic selection[J].Optics and Precision Engineering,2014,22(5):1 312-1 321.
[18]Spratling M W.A neural implementation of the Hough transform and the advantages of explaining away[J].Image&Vision Computing,2016,52:15-24.
[19]何扬名,戴曙光.提高霍夫变换识别圆形物体准确率的算法[J].微计算机信息,2009,25(4):279-281.He Y M,Dai S G.An algorithm for improving the precision of detecting round object by Hough Transform[J].Microcomputer Information,2009,25(4):279-281.
[20]赵振兵,徐磊,戚银城,等.基于Hough检测和C-V模型的航拍绝缘子自动协同分割方法[J].仪器仪表学报,2016,37(2):395-403.DOI:10.3969/j.issn.0254-3087.2016.02.021.Zhao Z B,Xu L,Qi Y CH,et al.Automatic co-segmentation method for aerial insulator based on Hough detection and C-V model[J].Chinese Journal of Scientific Instrument,2016,37(2):395-403.
[21]Smerek K M,Duleba I.Circular object detection using a modified Hough transform[J].International Journal of Applied Mathematics&Computer Science,2008,18(1):85-91.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |