3D segmentation of breast tumor in ultrasound images

被引:4
作者
Kwak, JI [1 ]
Jung, MN [1 ]
Kim, SH [1 ]
Kim, NC [1 ]
机构
[1] Kyungpook Natl Univ, Dept Elect Engn, Taegu 702701, South Korea
来源
MEDICAL IMAGING 2003: ULTRASONIC IMAGING AND SIGNAL PROCESSING | 2003年 / 5035卷
关键词
3D segmentation; ultrasound images; region-based segmentation; split-and-merge; region growing;
D O I
10.1117/12.479903
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
This paper proposes a three-dimensional (3D) region-based segmentation algorithm for extracting a diagnostic tumor from ultrasound images by using a split-and-merge and seeded region growing with a distortion-based homogeneity cost. In the proposed algorithm, 2D cutting planes are first obtained by the equiangular revolution of a cross sectional plane on a reference axis for a 3D volume data. In each cutting plane, an elliptic seed mask that is included tightly in a tumor of interest is set. At the same time, each plane is finely segmented using the split-and-merge with a distortion-based cost. In the result segmented finely, all of the regions that are across or contained in the elliptic seed mask are then merged. The merged region is taken as a seed region for the seeded region growing. In the seeded region growing, the seed region is recursively merged with adjacent regions until a predefined condition is reached. Then, the contour of the final seed region is extracted as a contour of the tumor. Finally, a 3D volume of the tumor is rendered from the set of tumor contours obtained for the entire cutting planes. Experimental results for a 3D artificial volume data show that the proposed method yields maximum three times reduction in error rate over the Krivanek's method. For a real 3D ultrasonic volume data, the error rates of the proposed-method are shown to be lower than 17% when the results obtained manually are used as a reference data. It also is found that the contours of the tumor extracted by the proposed algorithm coincide closely with those estimated by human vision.
引用
收藏
页码:193 / 200
页数:8
相关论文
共 11 条
[1]   SEEDED REGION GROWING [J].
ADAMS, R ;
BISCHOF, L .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1994, 16 (06) :641-647
[2]   IMAGE SEGMENTATION TECHNIQUES [J].
HARALICK, RM ;
SHAPIRO, LG .
COMPUTER VISION GRAPHICS AND IMAGE PROCESSING, 1985, 29 (01) :100-132
[3]   PICTURE SEGMENTATION BY A TREE TRAVERSAL ALGORITHM [J].
HOROWITZ, SL ;
PAVLIDIS, T .
JOURNAL OF THE ACM, 1976, 23 (02) :368-388
[4]   Ovarian ultrasound image analysis: Follicle segmentation [J].
Krivanek, A ;
Sonka, M .
IEEE TRANSACTIONS ON MEDICAL IMAGING, 1998, 17 (06) :935-944
[5]   RECENT RESULTS IN HIGH-COMPRESSION IMAGE-CODING [J].
KUNT, M ;
BENARD, M ;
LEONARDI, R .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS, 1987, 34 (11) :1306-1336
[6]  
LANGO T, 2001, IEEE T ULTRASONICS F, V48
[7]  
Lim CW, 2000, IEEE T CIRC SYST VID, V10, P1121
[8]  
MORRIS OJ, 1986, IEE PROC-F, V133, P146, DOI 10.1049/ip-f-1.1986.0025
[9]  
Ogawa S, 1998, ULTRASON, P1677, DOI 10.1109/ULTSYM.1998.765271
[10]   Signal acquisition and processing in medical diagnostic ultrasound [J].
Quistgaard, JU .
IEEE SIGNAL PROCESSING MAGAZINE, 1997, 14 (01) :67-74