Title :
A curve evolution method for identifying weak edges with applications to the segmentation of magnetic resonance images of the knee
Author :
Pang, Jincheng ; Miller, Eric ; Driban, Jeffrey ; Tassinari, Anna ; McAlindon, Timothy
Author_Institution :
Dept. of Electr. & Comput. Eng., Tufts Univ., Medford, MA, USA
fDate :
March 30 2011-April 2 2011
Abstract :
Motivated by the analysis of knee MRI data arising in the study of osteoarthritis, this paper presents a new active contour-based segmentation algorithm which combines external force field ideas and local region based methods in a consistent way. The approach not only has a large capture range as is common with curve evolution techniques based on static force fields such as the gradient vector flow (GVF) and vector field convolution (VFC) methods, but also can distinguish small details as local region based methods do. The feasibility of the new algorithm is demonstrated on both synthetic images as well as real knee MRI data where the goal is to identify the tibia and femur as part of a larger osteoarthritis image analysis problem.
Keywords :
biomedical MRI; bone; diseases; edge detection; image segmentation; medical image processing; MRI segmentation; active contour-based segmentation algorithm; curve evolution method; curve evolution techniques; external force field ideas; femur; gradient vector flow; knee MRI data; local region based methods; magnetic resonance images; osteoarthritis image analysis problem; static force fields; synthetic images; tibia; vector field convolution; weak edge identification; Active contours; Bones; Force; Image edge detection; Image segmentation; Knee; Pixel; active contours; gradient vector flow; image segmentation; level set methods; vector field convolution;
Conference_Titel :
Biomedical Imaging: From Nano to Macro, 2011 IEEE International Symposium on
Conference_Location :
Chicago, IL
Print_ISBN :
978-1-4244-4127-3
Electronic_ISBN :
1945-7928
DOI :
10.1109/ISBI.2011.5872664