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Robust adaptive principal component analysis based on intergraph matrix for medical image registration.

Leng C, Xiao J, Li M, Zhang H - Comput Intell Neurosci (2015)

Bottom Line: Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects.Secondly, the robust similarity measure is proposed based on adaptive principal component.The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Nondestructive Testing, Ministry of Education, School of Mathematics and Information Science, Nanchang Hangkong University, Nanchang 330063, China ; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

ABSTRACT
This paper proposes a novel robust adaptive principal component analysis (RAPCA) method based on intergraph matrix for image registration in order to improve robustness and real-time performance. The contributions can be divided into three parts. Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects. Secondly, the robust similarity measure is proposed based on adaptive principal component. Finally, the robust registration algorithm is derived based on the RAPCA. The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images.

No MeSH data available.


Matching results for different feature points. Top row: matching results based on Caelli's method. Bottom row: matching results based on our method.
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Related In: Results  -  Collection


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fig1: Matching results for different feature points. Top row: matching results based on Caelli's method. Bottom row: matching results based on our method.

Mentions: To test RAPCA algorithm, we applied it to medical images. Figure 1 shows the comparison of matching results using our method and Caelli's method [17] to test on T1 and T2 of the 24th slice of a magnetic resonance imaging (MRI) sequence. The 18 feature points, 25 feature points, and 29 feature points are extracted by the Harris Corner Detector [21] from Figures 1(a) and 1(d), 1(b) and 1(e), and 1(c) and 1(f) respectively. From Figures 1(a) and 1(d), we can see that the feature points matching are one-to-one correspondence with Caelli's method and our method. With increase in the number of feature points, Caelli's method produces more many-to-one correspondence as shown in Figures 1(b) and 1(c). However, our method still achieves a one-to-one correspondence as shown in Figures 1(e) and 1(f). Our RAPCA algorithm has high matching ability by projecting intranormalized Laplacian graph matrix into the same lower dimensional feature space based on intergraph matrix, which can reveal the internal geometrical structure information of two point sets. Caelli's method produces some many-to-one correspondence because the distance between some points is very close, which are considered to be in the same class. In addition, Caelli's method is not also stable and can produce different matching results with different feature points extracted. These MRI images are examples to illustrate that the features matching of our method is better than Caelli's method.


Robust adaptive principal component analysis based on intergraph matrix for medical image registration.

Leng C, Xiao J, Li M, Zhang H - Comput Intell Neurosci (2015)

Matching results for different feature points. Top row: matching results based on Caelli's method. Bottom row: matching results based on our method.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4417574&req=5

fig1: Matching results for different feature points. Top row: matching results based on Caelli's method. Bottom row: matching results based on our method.
Mentions: To test RAPCA algorithm, we applied it to medical images. Figure 1 shows the comparison of matching results using our method and Caelli's method [17] to test on T1 and T2 of the 24th slice of a magnetic resonance imaging (MRI) sequence. The 18 feature points, 25 feature points, and 29 feature points are extracted by the Harris Corner Detector [21] from Figures 1(a) and 1(d), 1(b) and 1(e), and 1(c) and 1(f) respectively. From Figures 1(a) and 1(d), we can see that the feature points matching are one-to-one correspondence with Caelli's method and our method. With increase in the number of feature points, Caelli's method produces more many-to-one correspondence as shown in Figures 1(b) and 1(c). However, our method still achieves a one-to-one correspondence as shown in Figures 1(e) and 1(f). Our RAPCA algorithm has high matching ability by projecting intranormalized Laplacian graph matrix into the same lower dimensional feature space based on intergraph matrix, which can reveal the internal geometrical structure information of two point sets. Caelli's method produces some many-to-one correspondence because the distance between some points is very close, which are considered to be in the same class. In addition, Caelli's method is not also stable and can produce different matching results with different feature points extracted. These MRI images are examples to illustrate that the features matching of our method is better than Caelli's method.

Bottom Line: Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects.Secondly, the robust similarity measure is proposed based on adaptive principal component.The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images.

View Article: PubMed Central - PubMed

Affiliation: Key Laboratory of Nondestructive Testing, Ministry of Education, School of Mathematics and Information Science, Nanchang Hangkong University, Nanchang 330063, China ; State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.

ABSTRACT
This paper proposes a novel robust adaptive principal component analysis (RAPCA) method based on intergraph matrix for image registration in order to improve robustness and real-time performance. The contributions can be divided into three parts. Firstly, a novel RAPCA method is developed to capture the common structure patterns based on intergraph matrix of the objects. Secondly, the robust similarity measure is proposed based on adaptive principal component. Finally, the robust registration algorithm is derived based on the RAPCA. The experimental results show that the proposed method is very effective in capturing the common structure patterns for image registration on real-world images.

No MeSH data available.