Multimedia Tools and Applications

, Volume 76, Issue 3, pp 3505–3517 | Cite as

Iterative quadtree decomposition based automatic selection of the seed point for ultrasound breast tumor images



Based on seed region growing method, lesion segmentation for ultrasound breast tumor images often requires manual selection of the seed point, which is both time-consuming and laborious. To overcome this limit, this paper attempts to explore an automatic method for finding the seed point inside the tumor. Two criteria combining iterative quadtree decomposition (QTD) and the gray characteristics of the lesion are thus designed to locate the seed point. One is to seek the biggest homogenous region and the other is to select the seed region where the seed point is found. Furthermore, this study validates the proposed algorithm through 110 ultrasonic breast tumor images (including 58 malignant tumor images and 52 benign tumor images). According to the needs of the seed region growing algorithm, if the seed point is found inside the tumor, it means the proposed method is correct. Otherwise, it means that the method is a failure. As the quantitative experiment results show, the proposed method in this paper can automatically find the seed point inside the tumor with an accuracy rate of 97.27 %.


Seed region growing Iterative quadtree Ultrasonic breast tumor Anisotropic diffusion 


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Modern Optics SystemUniversity of Shanghai for Science and TechnologyShanghaiChina
  2. 2.Department of Medical Information EngineeringJining Medical UniversityShandongChina
  3. 3.Taian City Central HospitalShandongChina
  4. 4.SIATChinese Academy of ScienceShenzhenChina

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