New ultrasound image-segmentation algorithm based on an early vision model and discrete snake model

Chung Ming Chen*, Horng-Shing Lu, Yu Chen Lin

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


Segmentation is a fundamental step in many quantitative analysis tasks for clinical ultrasound images. However, due to the speckle noises and the ill-defined edges of the object of interest, the classic image segmentation techniques are frequently ineffective in segmenting ultrasound images. It is either difficult to identify the actual edges or the derived boundaries are disconnected in the images. In this paper, we present a novel algorithm for segmentation of general ultrasound images, which is composed of two major techniques, namely the early vision model and the discrete snake model. By simulating human early vision, the early vision model can highlight the edges and, at the same time, suppress the speckle noises in an ultrasound image. The discrete snake model carries out energy minimization on the distance map rather than performing snake deformation on the original image as other snake models did. Moreover, instead of searching the next position for a snaxel along its searching path pixel by pixel, the discrete model only consider the local maxima as the searching space. The new segmentation algorithm has been verified on clinical ultrasound images and the derived boundaries of the object of interest are quite consistent with those specified by medical doctors.

Original languageAmerican English
Pages (from-to)959-970
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - 24 Jun 1998
EventMedical Imaging 1998: Image Processing - San Diego, CA, United States
Duration: 23 Feb 199823 Feb 1998


  • Discrete snake model
  • Early vision model
  • Image segmentation
  • Snake
  • Speckles
  • Texture
  • Ultrasound


Dive into the research topics of 'New ultrasound image-segmentation algorithm based on an early vision model and discrete snake model'. Together they form a unique fingerprint.

Cite this