Integrating Morphological Edge Detection and Mutual Information for Nonrigid Registration of Medical Images

Author(s): Vivek Aggarwal, Anupama Gupta*.

Journal Name: Current Medical Imaging
Formerly: Current Medical Imaging Reviews

Volume 15 , Issue 3 , 2019

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Graphical Abstract:


Background: Medical images are widely used within healthcare and medical research. There is an increased interest in precisely correlating information in these images through registration techniques for investigative and therapeutic purposes. This work proposes and evaluates an improved measure function for registration of carotid ultrasound and magnetic resonance images (MRI) taken at different times.

Methods: To achieve this, a morphological edge detection operator has been designed to extract the vital edge information from images which is integrated with the Mutual Information (MI) to carry out the registration process. The improved performance of proposed registration measure function is demonstrated using four quality metrics: Correlation Coefficient (CC), Structural Similarity Index (SSIM), Visual Information Fidelity (VIF) and Gradient Magnitude Similarity Deviation (GMSD). The qualitative validation has also been done through visual inspection of the registered image pairs by clinical radiologists.

Results: The experimental results showed that the proposed method outperformed the existing method (based on integrated MI and standard edge detection) for both ultrasound and MR images in terms of CC by about 4.67%, SSIM by 3.21%, VIF by 18.5%, and decreased GMSD by 37.01%. Whereas, in comparison to the standard MI based method, the proposed method has increased CC by 16.29%, SSIM by 16.13%, VIF by 52.56% and decreased GMSD by 66.06%, approximately.

Conclusion: Thus, the proposed method improves the registration accuracy when the original images are corrupted by noise, have low intensity values or missing data.

Keywords: Image registration, ultrasound, magnetic resonance imaging, mutual information, morphological edge detection, edge correlative deviation.

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Article Details

Year: 2019
Page: [292 - 300]
Pages: 9
DOI: 10.2174/1573405614666180103163430
Price: $65

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