An efficient level set method for simultaneous intensity inhomogeneity correction and segmentation of MR images

2016 ◽  
Vol 48 ◽  
pp. 9-20 ◽  
Author(s):  
Tatyana Ivanovska ◽  
René Laqua ◽  
Lei Wang ◽  
Andrea Schenk ◽  
Jeong Hee Yoon ◽  
...  
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Inhomogeneity Correction ◽  
Efficient Level

scholarly journals A weighted region-based level set method for image segmentation with intensity inhomogeneity

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255948
Author(s):  
Haiping Yu ◽  
Ping Sun ◽  
Fazhi He ◽  
Zhihua Hu
Keyword(s):  
Image Segmentation ◽  
Level Set Method ◽  
Level Set ◽  
State Of The Art ◽  
Intensity Inhomogeneity ◽  
Curve Evolution ◽  
Pressure Force ◽  
The Stability ◽  
Level Set Framework ◽  
Robust To Noise

Image segmentation is a fundamental task in image processing and is still a challenging problem when processing images with high noise, low resolution and intensity inhomogeneity. In this paper, a weighted region-based level set method, which is based on the techniques of local statistical theory, level set theory and curve evolution, is proposed. Specifically, a new weighted pressure force function (WPF) is first presented to flexibly drive the closed contour to shrink or expand outside and inside of the object. Second, a faster and smoother regularization term is added to ensure the stability of the curve evolution and that there is no need for initialization in curve evolution. Third, the WPF is integrated into the region-based level set framework to accelerate the speed of the curve evolution and improve the accuracy of image segmentation. Experimental results on medical and natural images demonstrate that the proposed segmentation model is more efficient and robust to noise than other state-of-the-art models.

An Atlas Based Performance Evaluation of Inhomogeneity Correcting Effects

MACRo 2015 ◽  
2015 ◽  
Vol 1 (1) ◽  
pp. 79-90 ◽  
Author(s):  
László Lefkovits ◽  
Szidónia Lefkovits ◽  
Mircea-Florin Vaida
Keyword(s):  
Performance Measures ◽  
Intensity Inhomogeneity ◽  
Normal Brain ◽  
Mr Images ◽  
Brain Images ◽  
Inhomogeneity Correction ◽  
Segmented Images ◽  
Fuzzy C Means Clustering ◽  
Quantitative Performance ◽  
Automated Image Processing

AbstractIn automated image processing the intensity inhomogeneity of MR images causes significant errors. In this work we analyze three algorithms with the purpose of intensity inhomogeneity correction. The well-known N3 algorithm is compared to two more recent approaches: a modified level set method, which is able to deal with intensity inhomogeneity and it is, as well, compared to an adaptation of the fuzzy c-means clustering with intensity inhomogeneity compensation techniques. We evaluate the outcomes of these three algorithms with quantitative performance measures. The measurements are done on the bias fields and on the segmented images. We consider normal brain images obtained from the Montreal Simulated Brain Database.

SEGMENTATION OF UTERINE FIBROID ON MR IMAGES BASED ON CHAN–VESE LEVEL SET METHOD AND SHAPE PRIOR MODEL

2014 ◽  
Vol 26 (02) ◽  
pp. 1450030 ◽  
Author(s):  
Hassan Khotanlou ◽  
Alireza Fallahi ◽  
Mohammad Ali Oghabian ◽  
Mohammad Pooyan
Keyword(s):  
Level Set Method ◽  
Level Set ◽  
Uterine Fibroids ◽  
Shape Prior ◽  
Mr Images ◽  
Initial Region ◽  
Artery Embolization ◽  
Shape Model ◽  
Prior Model ◽  
Accurate Treatment

Uterine fibroids are common tumors of female pelvis. Uterine artery embolization (UAE) is an effective treatment of symptomatic uterine fibroids by shrinkage of the size of these tumors. Segmentation of the fibroid region is essential for an accurate treatment strategy. Complex fibroids anatomy, nonhomogeneity region and missing boundary in some cases make this task very challenging. In this paper, we present a method to robustly segment these fibroids on magnetic resonance image (MRI). Our method is based on combination of two steps; Chan–Vese level set method and geometric shape prior model. By calculating an initial region inside the fibroid using Chan–Vese level sets method, rough segmentation can be obtained followed by a prior shape model. We found this algorithm efficient, which provides good and reliable result.

An efficient level set method based on multi-scale image segmentation and hermite differential operator

2016 ◽  
Vol 188 ◽  
pp. 90-101 ◽  
Author(s):  
Xiao-Feng Wang ◽  
Hai Min ◽  
Le Zou ◽  
Yi-Gang Zhang ◽  
Yuan-Yan Tang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Differential Operator ◽  
Level Set Method ◽  
Level Set ◽  
Multi Scale ◽  
Efficient Level
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