Username: Password:
Menu 
VPA
Computer Vision And Pattern Analysis Laboratory Home Page  Home
People  People
Publications  Publications
Publications  Databases
Contact Information  Contact
Research
Supported Research Projects  Supported Research Projects
Research Activites  Research Activites
Research Groups
SPIS - Signal Processing and Information Systems Lab.SPIS - Signal Processing and Information Systems Lab.
Medical Vision and Analysis Group  Medical Research Activities
Biometrics Research Group  Biometrics Research Group
SPIS - Signal Processing and Information Systems Lab.MISAM - Machine Intelligence for Speech Audio and Multimedia.
Knowledge Base
  Paper Library
  VPA Lab Inventory
  Databases in VPALAB
  Recordings
Calendar
<<December 2017>>
Mo Tu We Th Fr Sa Su
1 2 3
4 5 6 7 8 9 10
11 12 13 14 15 16 17
18 19 20 21 22 23 24
25 26 27 28 29 30 31
Upcoming Events:
None

A Joint Classification And Segmentation Approach For Dendritic Spine Segmentation in 2-photon Microscopy Images
Authors: Ertunc Erdil, A. Ozgur Argunsah, Tolga Tasdizen, Devrim Unay, Mujdat Cetin
Published in: IEEE International Symposium Biomedical Imaging, ISBI 2015, New York
Publication year: 2015
Abstract: Shape priors have been successfully used in challenging biomedical imaging problems. However when the shape distribution involves multiple shape classes, leading to a multimodal shape density, effec-tive use of shape priors in segmentation becomes more challenging. In such scenarios, knowing the class of the shape can aid the segmentation process, which is of course unknown a priori. In this paper, we propose a joint classification and segmentation approach for dendritic spine segmentation which infers the class of the spine during segmentation and adapts the remaining segmentation process
accordingly. We evaluate our proposed approach on 2-photon mi-croscopy images containing dendritic spines and compare its perfor-mance quantitatively to an existing approach based on nonparamet-ric shape priors. Both visual and quantitative results demonstrate the effectiveness of our approach in dendritic spine segmentation.
  download full paper
Download

Home Back