基于最大内切圆的肝影像自动分割方法
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摘要
针对肝脏区域提取中的手动选择种子点以及提取时的准确性和完整性问题,提出了一种基于最大内切圆的肝影像自动分割算法。采用最大区域面积测量法锁定二值化后的肝脏区域,通过寻找锁定肝脏区域的最大内切圆的圆心来自动获取种子点位置;采用改进的区域生长算法进行图像分割。实验结果表明,该方法有效地解决了区域生长的种子点手动选取问题,并且能够精确、完整地分割出肝脏组织,避免了受主观因素影响而将种子点选取在边缘或噪声等错误位置。
Aiming to the problem of selecting the seed points manually and the accuracy and integrity of extracting the liver region, an automatic segmentation method based on the maximum inscribed circle is proposed in this paper. Firstly,the method uses the maximum area measurement method to lock the binarized liver region, and then automatically acquires the seed point position by finding the center of the largest inscribed circle that locks the liver region. Finally, the improved region growing algorithm is used for image segmentation. The experimental results show that the method effectively solves the problem of manually selecting seed points for regional growth, and can accurately and completely segment the liver tissue. In addition, it avoids subjective factors to select seed points at the wrong positions such as edges or noise.
Aiming to the problem of selecting the seed points manually and the accuracy and integrity of extracting the liver region, an automatic segmentation method based on the maximum inscribed circle is proposed in this paper. Firstly,the method uses the maximum area measurement method to lock the binarized liver region, and then automatically acquires the seed point position by finding the center of the largest inscribed circle that locks the liver region. Finally, the improved region growing algorithm is used for image segmentation. The experimental results show that the method effectively solves the problem of manually selecting seed points for regional growth, and can accurately and completely segment the liver tissue. In addition, it avoids subjective factors to select seed points at the wrong positions such as edges or noise.
引文
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[4]Wu Xuejun.Review of theory and methods of image segmentation[J].Agricultural Biotechnology,2018,7(4):136-141.
[5]王伏增,汪西原,宋佳乾.基于形态学滤波的标记分水岭脑肿瘤图像分割[J].宁夏大学学报(自然科学版),2017,38(1):27-32.
[6]刘鑫,陈永健,万洪林,等.基于两阶段区域生长的肝内血管分割算法[J].计算机工程与应用,2015,51(12):194-197.
[7]Lee J,Kim N,Lee H,et al.Efficient liver segmentation using a level-set method with optimal detection of the initial liver boundary from level-set speed images[J].Computer Methods Programs in Biomedicine,2007,88(1):26-38.
[8]Suzuki K,Kohlbrenner R,Epstein M L,et al.Computeraided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with levelset algorithms[J].Medical Physics,2010,37(5):2159-2166.
[9]Chen X,Udupa J K,Bagci U,et al.Medical image segmentation by combining graph cuts and oriented active appearance models[J].IEEE Transactions on Image Processing,2012,21(4):2035-2046.
[10]Mustaqeem A,Javed A,Fatima T.An efficient brain tumor detection algorithm using watershed&thresholding based segmentation[J].International Journal of Image Graphics&Signal Processing,2012,4(10):34-39.
[11]Jentzen W,Freudenberg L,Eising E G,et al.Segmentation of PET volumes by iterative image thresholding[J].Journal of Nuclear Medicine Official Publication Society of Nuclear Medicine,2007,48(1):108-114.
[12]吴一全,孟天亮,吴诗婳.图像阈值分割方法研究进展20年(1994-2014)[J].数据采集与处理,2015,30(1):1-23.
[13]阴国富.基于阈值法的图像分割技术[J].现代电子技术,2007(23):107-108.
[14]Drever L,Roa W,Mcewan A,et al.Iterative threshold segmentation for PET target volume delineation[J].Medical Physics,2007,34(4):1253-1265.
[15]Xi T,Schreurs R,Heerink W J,et al.A novel regiongrowing based semi-automatic segmentation protocol for three-dimensional condylar reconstruction using Cone Beam Computed Tomography(CBCT)[J].Plos One,2014,9(11).
[16]Wang L,Gao X,Zhang G.3D region growing algorithm driven by morphological dilation for airway tree segmentation in image guided therapy[J].Journal of Biomedical Engineering,2013,30(4):679-683.
[17]刘健庄,栗文青.灰度图象的二维OTSU自动阈值分割法[J].自动化学报,1993(1):101-105.
[18]Zucker S W.Region growing:childhood and adolescence[J].Computer Graphics&Image Processing,1976,5(3):382-399.
[19]黄谊,任毅.基于阈值法和区域生长法的图像分割算法研究[J].电子测试,2012(10):23-25.
[20]彭丰平,鲍苏苏,曾碧卿.基于自适应区域生长算法的肝脏分割[J].计算机工程与应用,2010,46(33):198-200.
[21]张荣华,吴益,王亨,等.基于几何主动轮廓模型的图像分割方法[J].天津工业大学学报,2018,37(3):61-66.