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Medical image registration using sparse coding of image patches.

Maryam Afzali¹, Aboozar Ghaffari¹, Emad Fatemizadeh¹

¹Department of Electrical Engineering, Biomedical Signal and Image Processing Laboratory (BiSIPL), Sharif University of Technology, Tehran, Iran.

Computers in Biology and Medicine

|April 18, 2016

Summary

This study introduces a novel sparse similarity measure for medical image registration, robustly handling intensity distortions. The method accurately aligns images, outperforming existing techniques even with bias field distortions.

Keywords:

Analysis K-SVD algorithm Non-rigid image registration Sparse coding

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Area of Science:

Medical Image Processing
Computer Vision
Scientific Computing

Background:

Image registration is crucial for medical applications like group analysis and atlas construction.
Existing similarity measures often fail with intensity distortions, particularly bias fields.
Robust registration methods are needed to handle non-stationary and spatially-varying distortions.

Purpose of the Study:

To propose a novel sparse-based similarity measure for mono-modal medical image registration.
To address limitations of current methods in handling intensity distortions and bias fields.
To develop a robust registration technique for 2D and 3D medical images.

Main Methods:

A sparse similarity measure is proposed, utilizing an analysis dictionary trained with image patches.
Analysis K-SVD is employed for dictionary training and sparse coefficient computation.
Non-rigid transformation is achieved using Free Form Deformation (FFD) guided by the sparse similarity measure.

Main Results:

The proposed method demonstrates robust registration of 2D and 3D images in simulated and real scenarios.
Experimental results show superior performance compared to state-of-the-art similarity measures.
The method significantly reduces transformation error and aligns images effectively even with bias field distortions, without preprocessing.

Conclusions:

The developed sparse similarity measure offers a robust solution for medical image registration, particularly in the presence of intensity distortions.
This approach enhances accuracy and reduces errors in non-rigid transformations.
The method's ability to handle bias fields without preprocessing marks a significant advancement.