TY - JOUR
T1 - Adaptive windowed range-constrained Otsu method using local information
AU - Zheng, Jia
AU - Zhang, Dinghua
AU - Huang, Kuidong
AU - Sun, Yuanxi
AU - Tang, Shaojie
N1 - Publisher Copyright:
© 2016 SPIE and IS&T.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - An adaptive windowed range-constrained Otsu method using local information is proposed for improving the performance of image segmentation. First, the reason why traditional thresholding methods do not perform well in the segmentation of complicated images is analyzed. Therein, the influences of global and local thresholdings on the image segmentation are compared. Second, two methods that can adaptively change the size of the local window according to local information are proposed by us. The characteristics of the proposed methods are analyzed. Thereby, the information on the number of edge pixels in the local window of the binarized variance image is employed to adaptively change the local window size. Finally, the superiority of the proposed method over other methods such as the range-constrained Otsu, the active contour model, the double Otsu, the Bradley's, and the distance-regularized level set evolution is demonstrated. It is validated by the experiments that the proposed method can keep more details and acquire much more satisfying area overlap measure as compared with the other conventional methods.
AB - An adaptive windowed range-constrained Otsu method using local information is proposed for improving the performance of image segmentation. First, the reason why traditional thresholding methods do not perform well in the segmentation of complicated images is analyzed. Therein, the influences of global and local thresholdings on the image segmentation are compared. Second, two methods that can adaptively change the size of the local window according to local information are proposed by us. The characteristics of the proposed methods are analyzed. Thereby, the information on the number of edge pixels in the local window of the binarized variance image is employed to adaptively change the local window size. Finally, the superiority of the proposed method over other methods such as the range-constrained Otsu, the active contour model, the double Otsu, the Bradley's, and the distance-regularized level set evolution is demonstrated. It is validated by the experiments that the proposed method can keep more details and acquire much more satisfying area overlap measure as compared with the other conventional methods.
KW - adaptive
KW - image segmentation
KW - local window
KW - Otsu method
KW - thresholding
UR - http://www.scopus.com/inward/record.url?scp=84959210020&partnerID=8YFLogxK
U2 - 10.1117/1.JEI.25.1.013034
DO - 10.1117/1.JEI.25.1.013034
M3 - 文章
AN - SCOPUS:84959210020
SN - 1017-9909
VL - 25
JO - Journal of Electronic Imaging
JF - Journal of Electronic Imaging
IS - 1
M1 - 013034
ER -