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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 modality images. 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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fig3: Matching results for different modality images. Top row: matching results based on Caelli's method. Bottom row: matching results based on our method.

Mentions: To further test our algorithm, we applied the proposed method to the 217 × 181 medical images from the same patient of different modality from the brain datasets [22]. Figure 3 (top row and bottom row) gives matching results for different modality images with Caelli's method and our method, respectively. The first and second column give T1 and PD matching results, and the third and fourth column give PD and T2 matching results, respectively. Figures 3(a) and 3(b) give T1 and PD matching results with different feature points, which produce the different results, and the matching results are bad as shown in Figure 3(b). Figures 3(c) and 3(d) give PD and T2 matching results with different feature points, which also produce the different results, and the matching results are also bad as shown in Figure 3(c). Therefore, Caelli's method is not stable with different feature points from the top row of Figure 3. However, our method can find correct feature correspondences, which show that the proposed method is effective and feasible for different modality images. The reason is that we incorporate the intergraph matrix to capture the common structure pattern and obtain the adaptive principal component based on error analysis theorem. Meanwhile, the robust similarity measure is proposed based on robust principal component by projecting both the reference image and the sensed image into the same lower dimensional feature space to reduce computational complexity. Table 1 also shows the comparison of computation time which indicates that the computation time of our method is less 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 modality images. 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

fig3: Matching results for different modality images. Top row: matching results based on Caelli's method. Bottom row: matching results based on our method.
Mentions: To further test our algorithm, we applied the proposed method to the 217 × 181 medical images from the same patient of different modality from the brain datasets [22]. Figure 3 (top row and bottom row) gives matching results for different modality images with Caelli's method and our method, respectively. The first and second column give T1 and PD matching results, and the third and fourth column give PD and T2 matching results, respectively. Figures 3(a) and 3(b) give T1 and PD matching results with different feature points, which produce the different results, and the matching results are bad as shown in Figure 3(b). Figures 3(c) and 3(d) give PD and T2 matching results with different feature points, which also produce the different results, and the matching results are also bad as shown in Figure 3(c). Therefore, Caelli's method is not stable with different feature points from the top row of Figure 3. However, our method can find correct feature correspondences, which show that the proposed method is effective and feasible for different modality images. The reason is that we incorporate the intergraph matrix to capture the common structure pattern and obtain the adaptive principal component based on error analysis theorem. Meanwhile, the robust similarity measure is proposed based on robust principal component by projecting both the reference image and the sensed image into the same lower dimensional feature space to reduce computational complexity. Table 1 also shows the comparison of computation time which indicates that the computation time of our method is less 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.