Analysis of the feasibility of using active shape models for segmentation of gray scale images

Gilberto Zamora, Hamed Sari-Sarraf, Sunanda Mitra, Rodney Long

Research output: Contribution to journalConference articlepeer-review


Active Shape Models (ASM) have been used extensively to segment images where the objects of interest show little to moderate shape variability across a training set. It is well known that the efficacy of this technique relies heavily on the quality of the training set and the initialization of the mean shape on the target image. However, little has been said about the validity of the assumptions under which the two core components of ASM, i.e. the shape model and the gray level model, are built. We explore these assumptions and test their validity with respect to both shape and gray level models. In this study, we use different training sets of real and synthetic gray scale images and investigate the reasons for their success or failure in the context of shape and gray level modeling. We show that the shape model performance is not affected by small changes in the distribution of the shapes. Furthermore, we show that a reason for segmentation failure is the lack of features in the mean profiles of gray level values that causes localization errors even under ideal conditions.

Original languageEnglish
Pages (from-to)1370-1381
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume4684 III
StatePublished - 2002
EventMedical Imaging 2002: Image Processing - San Diego, CA, United States
Duration: Feb 24 2002Feb 28 2002


  • Active shape models
  • Deformable models
  • Gray-level modeling
  • Segmentation
  • Shape modeling
  • X-ray imaging


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