Medical image registration using sparse coding of image patches

详细信息    查看全文

• 作者：Maryam Afzali^a ; ^{afzali@ee.sharif.edu" class="auth_mail" title="E-mail the corresponding author} ; Aboozar Ghaffari^a ; ^{aboozar412@gmail.com" class="auth_mail" title="E-mail the corresponding author} ; Emad Fatemizadeh^a ; ^{fatemizadeh@sharif.edu" class="auth_mail" title="E-mail the corresponding author} ; Hamid Soltanian-Zadeh^b ; ^c ; ^d ; ^{hszadeh@ut.ac.ir" class="auth_mail" title="E-mail the corresponding author} ; ^{hsoltan1@hfhs.org" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Non-rigid image registration ; Analysis K-SVD algorithm ; Sparse coding
• 刊名：Computers in Biology and Medicine
• 出版年：2016
• 出版时间：1 June 2016
• 年：2016
• 卷：73
• 期：Complete
• 页码：56-70
• 全文大小：2733 K

文摘

Image registration is a basic task in medical image processing applications like group analysis and atlas construction. Similarity measure is a critical ingredient of image registration. Intensity distortion of medical images is not considered in most previous similarity measures. Therefore, in the presence of bias field distortions, they do not generate an acceptable registration. In this paper, we propose a sparse based similarity measure for mono-modal images that considers non-stationary intensity and spatially-varying distortions. The main idea behind this measure is that the aligned image is constructed by an analysis dictionary trained using the image patches. For this purpose, we use “Analysis K-SVD” to train the dictionary and find the sparse coefficients. We utilize image patches to construct the analysis dictionary and then we employ the proposed sparse similarity measure to find a non-rigid transformation using free form deformation (FFD). Experimental results show that the proposed approach is able to robustly register 2D and 3D images in both simulated and real cases. The proposed method outperforms other state-of-the-art similarity measures and decreases the transformation error compared to the previous methods. Even in the presence of bias field distortion, the proposed method aligns images without any preprocessing.