Kernel Graph Cuts Segmentation for MR Images with Intensity Inhomogeneity Correction

2013 ◽  
Vol 333-335 ◽  
pp. 938-943
Author(s):  
Qing Luo ◽  
Wen Jian Qin ◽  
Jia Gu
Keyword(s):  
Image Data ◽  
Graph Cuts ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Mr Image ◽  
Fcm Algorithm ◽  
The Real ◽  
Inhomogeneity Correction ◽  
Segmentation Approach ◽  
Correction Step

Since the phenomena of intensity inhomogeneity in MR images are prominent and adversely affect quantitative image analysis .In this paper ,we propose a novel magnetic resonance (MR) image segmentation approach based on the kernel graph cuts technique .Because of automatic multiregion segmentation and global energy minimization ,the kernel graph cuts method can be applied to many kinds of images segmentation ,such as MR images and so on . To reduce or eliminate intensity inhomogeneity in MR images ,we add the intensity inhomogeneity correction step which is based on fuzzy c-means (FCM) algorithm before the segment procedure .Firstly , the real MR image data obtained after bias corrected by FCM algorithm .Secondly ,we segment the real MR image data by kernel graph cuts method .Experiments show that the kernel graph cuts method with intensity inhomogeneity correction have a better segment result in accuracy and over-segmentation .

scholarly journals A Review on MR Image Intensity Inhomogeneity Correction

2006 ◽  
Vol 2006 ◽  
pp. 1-11 ◽  
Author(s):  
Zujun Hou
Keyword(s):  
Statistical Models ◽  
Low Frequency ◽  
Intensity Inhomogeneity ◽  
Computer Memory ◽  
Mr Image ◽  
Cpu Time ◽  
Inhomogeneity Correction ◽  
Recent Developments ◽  
Frequency Components ◽  
Overall Performance

Intensity inhomogeneity (IIH) is often encountered in MR imaging, and a number of techniques have been devised to correct this artifact. This paper attempts to review some of the recent developments in the mathematical modeling of IIH field. Low-frequency models are widely used, but they tend to corrupt the low-frequency components of the tissue. Hypersurface models and statistical models can be adaptive to the image and generally more stable, but they are also generally more complex and consume more computer memory and CPU time. They are often formulated together with image segmentation within one framework and the overall performance is highly dependent on the segmentation process. Beside these three popular models, this paper also summarizes other techniques based on different principles. In addition, the issue of quantitative evaluation and comparative study are discussed.

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.

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

Fuzzy Expectation Maximum Algorithm for Magnetic Resonance Image Segmentation

2010 ◽  
Vol 439-440 ◽  
pp. 1618-1623
Author(s):  
Yong Yang
Keyword(s):  
Image Segmentation ◽  
Magnetic Resonance ◽  
Em Algorithm ◽  
Expectation Maximization ◽  
Magnetic Resonance Image ◽  
Clustering Algorithm ◽  
Mr Images ◽  
Mr Image ◽  
Fcm Algorithm ◽  
Mr Image Segmentation

Fuzzy clustering algorithm especially the fuzzy c-means (FCM) algorithm has been widely used for segmentation of brain magnetic resonance (MR) images. However, the conventional FCM algorithm has a very serious shortcoming, i.e., the algorithm tends to balance the number of points in each cluster during the classification. Therefore, when this algorithm is applied to segment the MR images with quite different size of objects, it will lead to bad segmentation. To overcome this problem, a novel fuzzy expectation maximization (FEM) algorithm is presented in this paper. The algorithm is developed by extending the classical hard EM algorithm into soft EM algorithm through integrating the fuzzy and statistical techniques. Compared with the FCM algorithm, the experimental results on MR image segmentation clearly indicate that the proposed FEM algorithm has better performance for the segmentation.

INTERLEAVED EM SEGMENTATION FOR MR IMAGE WITH INTENSITY INHOMOGENEITY

2014 ◽  
Vol 26 (05) ◽  
pp. 1450058
Author(s):  
Jingjing Gao ◽  
Mei Xie ◽  
Yan Zhou
Keyword(s):  
Image Segmentation ◽  
Em Algorithm ◽  
Bias Field ◽  
Intensity Inhomogeneity ◽  
Mr Images ◽  
Tissue Segmentation ◽  
Mr Image ◽  
Em Method ◽  
Field Estimation ◽  
Mr Image Segmentation

Expectation–maximization (EM) algorithm has been extensively applied in brain MR image segmentation. However, the conventional EM method usually leads to severe misclassifications MR images with bias field, due to the significant intensity inhomogeneity. It limits the applications of the conventional EM method in MR image segmentation. In this paper, we proposed an interleaved EM method to perform tissue segmentation and bias field estimation. In the proposed method, the tissue segmentation is performed by the modified EM classification, and the bias field estimation is accomplished by an energy minimization. Moreover, the tissue segmentation and bias field estimation are performed in an interleaved process, and the two processes potentially benefit from each other during the iteration. A salient advantage of the proposed method is that it overcomes the misclassifications from the conventional EM classification for the MR images with bias field. Furthermore, the modified EM algorithm performs the soft segmentation in our method, which is more suitable for MR images than the hard segmentation achieved in Li et al.'s12 method. We have tested our method in the synthetic images with different levels of bias field and different noise, and compared with two baseline methods. Experimental results have demonstrated the effectiveness and advantages of the proposed algorithm.

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