Key Laboratory of Education Ministry for Image Processing and Intelligence Control

Wuhan, China

Key Laboratory of Education Ministry for Image Processing and Intelligence Control

Wuhan, China
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Liu H.,Huazhong University of Science and Technology | Liu H.,Key Laboratory of Education Ministry for Image Processing and Intelligence Control | Yan M.,Huazhong University of Science and Technology | Yan M.,Key Laboratory of Education Ministry for Image Processing and Intelligence Control | And 13 more authors.
IET Image Processing | Year: 2017

The multi-Atlas patch-based label fusion method (MAS-PBM) has emerged as a promising technique for the magnetic resonance imaging (MRI) image segmentation. The state-of-the-art MAS-PBM approach measures the patch similarity between the target image and each atlas image using the features extracted from images intensity only. It is well known that each atlas consists of both MRI image and labelled image (which is also called the map). In other words, the map information is not used in calculating the similarity in the existing MAS-PBM. To improve the segmentation result, the authors propose an enhanced MAS-PBM in which the maps will be used for similarity measure. The first component of the proposed method is that an initial segmentation result (i.e. an appropriate map for the target) is obtained by using either the non-local-patch-based label fusion method (NPBM) or the sparse patch-based label fusion method (SPBM) based on the grey scales of patches. Then, the SPBM is applied again to obtain the finer segmentation based on the labels of patches. The authors called these two versions of the proposed fusion method as MAS-PBM-NPBM and MAS-PBM-SPBM. Experimental results show that more accurate segmentation results are achieved compared with those of the majority voting, NPBM, SPBM, STEPS and the hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition. © The Institution of Engineering and Technology.


Liu H.,Huazhong University of Science and Technology | Liu H.,Key Laboratory of Education ministry for Image Processing and Intelligence Control | Yan M.,Huazhong University of Science and Technology | Yan M.,Key Laboratory of Education ministry for Image Processing and Intelligence Control | And 12 more authors.
Magnetic Resonance Imaging | Year: 2016

Myocardial motion estimation of tagged cardiac magnetic resonance (TCMR) images is of great significance in clinical diagnosis and the treatment of heart disease. Currently, the harmonic phase analysis method (HARP) and the local sine-wave modeling method (SinMod) have been proven as two state-of-the-art motion estimation methods for TCMR images, since they can directly obtain the inter-frame motion displacement vector field (MDVF) with high accuracy and fast speed. By comparison, SinMod has better performance over HARP in terms of displacement detection, noise and artifacts reduction. However, the SinMod method has some drawbacks: 1) it is unable to estimate local displacements larger than half of the tag spacing; 2) it has observable errors in tracking of tag motion; and 3) the estimated MDVF usually has large local errors. To overcome these problems, we present a novel motion estimation method in this study. The proposed method tracks the motion of tags and then estimates the dense MDVF by using the interpolation. In this new method, a parameter estimation procedure for global motion is applied to match tag intersections between different frames, ensuring specific kinds of large displacements being correctly estimated. In addition, a strategy of tag motion constraints is applied to eliminate most of errors produced by inter-frame tracking of tags and the multi-level b-splines approximation algorithm is utilized, so as to enhance the local continuity and accuracy of the final MDVF. In the estimation of the motion displacement, our proposed method can obtain a more accurate MDVF compared with the SinMod method and our method can overcome the drawbacks of the SinMod method. However, the motion estimation accuracy of our method depends on the accuracy of tag lines detection and our method has a higher time complexity. © 2016 Elsevier Inc.


Liu H.,Huazhong University of Science and Technology | Liu H.,Key Laboratory of Education Ministry for Image Processing and Intelligence Control | Wang J.,Huazhong University of Science and Technology | Wang J.,Key Laboratory of Education Ministry for Image Processing and Intelligence Control | And 10 more authors.
Magnetic Resonance Imaging | Year: 2014

A robust and accurate center-frequency (CF) estimation (RACE) algorithm for improving the performance of the local sine-wave modeling (SinMod) method, which is a good motion estimation method for tagged cardiac magnetic resonance (MR) images, is proposed in this study. The RACE algorithm can automatically, effectively and efficiently produce a very appropriate CF estimate for the SinMod method, under the circumstance that the specified tagging parameters are unknown, on account of the following two key techniques: (1) the well-known mean-shift algorithm, which can provide accurate and rapid CF estimation; and (2) an original two-direction-combination strategy, which can further enhance the accuracy and robustness of CF estimation. Some other available CF estimation algorithms are brought out for comparison. Several validation approaches that can work on the real data without ground truths are specially designed. Experimental results on human body in vivo cardiac data demonstrate the significance of accurate CF estimation for SinMod, and validate the effectiveness of RACE in facilitating the motion estimation performance of SinMod. © 2014 © Elsevier Inc. All rights reserved.

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