TY - JOUR
T1 - Image segmentation based on fuzzy connectedness using dynamic weights
AU - Pednekar, Amol S.
AU - Kakadiaris, Ioannis A.
N1 - Funding Information:
Manuscript received June 24, 2004; revised April 8, 2005. This work was supported in part by the National Science Foundation (NSF) under Grant IIS-0431144, Grant IIS-0335578, Grant IIS-035578, and Grant IIS-9985482. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Luca Lucchese.
PY - 2006/6
Y1 - 2006/6
N2 - Traditional segmentation techniques do not quite meet the challenges posed by inherently fuzzy medical images. Image segmentation based on fuzzy connectedness addresses this problem by attempting to capture both closeness, based on characteristic intensity, and "hanging togetherness," based on intensity homogeneity, of image elements to the target object. This paper presents a modification and extension of previously published image segmentation algorithms based on fuzzy connectedness, which is computed as a linear combination of an object-feature-based and a homogeneity-based component using fixed weights. We provide a method, called fuzzy connectedness using dynamic weights (DyW), to introduce directional sensitivity to the homogeneity-based component and to dynamically adjust the linear weights in the functional form of fuzzy connectedness. Dynamic computation of the weights relieves the user of the exhaustive search process to find the best combination of weights suited to a particular application. This is critical in applications such as analysis of cardiac cine magnetic resonance (MR) images, where the optimal combination of affinity component weights can vary for each slice, each phase, and each subject, in spite of data being acquired from the same MR scanner with identical protocols. We present selected results of applying DyW to segment phantom images and actual MR, computed tomography, and infrared data. The accuracy of DyW is assessed by comparing it to two different formulations of fuzzy connectedness. Our method consistently achieves accuracy of more than 99.15% for a range of image complexities: contrast 5%-65%, noise-to-contrast ratio of 6%-18%, and bias field of four types with maximum gain factor of up to 10%.
AB - Traditional segmentation techniques do not quite meet the challenges posed by inherently fuzzy medical images. Image segmentation based on fuzzy connectedness addresses this problem by attempting to capture both closeness, based on characteristic intensity, and "hanging togetherness," based on intensity homogeneity, of image elements to the target object. This paper presents a modification and extension of previously published image segmentation algorithms based on fuzzy connectedness, which is computed as a linear combination of an object-feature-based and a homogeneity-based component using fixed weights. We provide a method, called fuzzy connectedness using dynamic weights (DyW), to introduce directional sensitivity to the homogeneity-based component and to dynamically adjust the linear weights in the functional form of fuzzy connectedness. Dynamic computation of the weights relieves the user of the exhaustive search process to find the best combination of weights suited to a particular application. This is critical in applications such as analysis of cardiac cine magnetic resonance (MR) images, where the optimal combination of affinity component weights can vary for each slice, each phase, and each subject, in spite of data being acquired from the same MR scanner with identical protocols. We present selected results of applying DyW to segment phantom images and actual MR, computed tomography, and infrared data. The accuracy of DyW is assessed by comparing it to two different formulations of fuzzy connectedness. Our method consistently achieves accuracy of more than 99.15% for a range of image complexities: contrast 5%-65%, noise-to-contrast ratio of 6%-18%, and bias field of four types with maximum gain factor of up to 10%.
KW - Dynamic weights
KW - Fuzzy connectedness
KW - Image segmentation
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U2 - 10.1109/TIP.2006.871165
DO - 10.1109/TIP.2006.871165
M3 - Article
C2 - 16764280
AN - SCOPUS:33646879982
VL - 15
SP - 1555
EP - 1562
JO - IEEE Transactions on Image Processing
JF - IEEE Transactions on Image Processing
SN - 1057-7149
IS - 6
ER -