Robust Self-Similarity Descriptor for Multimodal Image Registration

Borvornvitchotikarn, Thuvanan; Kurutach, Werasak

doi:10.1109/iwssip.2018.8439707

Cited by 4 publications

(4 citation statements)

References 13 publications

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“…However, in this experimentation on the registrations of T1-T2, T1-PD, and T2-PD using various approaches, it has been found that the proposed method, SLBD, has the least errors. In comparison, the miLBP [25], RSSD [31], and miRID [32] have the high speeds of MIR and also have the robustness of modality-independent. The miRID [32] could perform the best performance in MIR both rigid and non-rigid registration.…”

Section: Discussionmentioning

confidence: 99%

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Multi-modal Brain MR Image Registration using A Novel Local Binary Descriptor based on Statistical Approach

Borvornvitchotikarn

2022

IJACSA

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Medical image registration (MIR) has played an important role in medical image processing during the last decade. Its main objective is to integrate information inherent in two images, from different scanning sources, of the same object for guiding medical treatments such as diagnostic, surgery and therapy. A challenging task of MIR arises from the complex relationships of image intensities between the two images. Its performance is primarily depending on a chosen similarity measure technique. In this work, a statistical local binary descriptor (SLBD) is proposed as novel local descriptor of similarity measure, which is simple for computation and can handle Multi-modal registration more effectively. The proposed SLBD employs two statistical values, i.e., the mean and the standard deviation, of all intensities within the image patch for its computation. Finally, these experimental results have shown that SLBD outperforms other descriptors in terms of registration accuracy. In addition, SLBD has demonstrated that SLBD is robust to different modalities.

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Section: Discussionmentioning

confidence: 99%

“…However, RWMI is sensitive to registration with very small overlap and small intensity variance. Furthermore, Borvornvitchotikarn and Kurutach [31] have improved miLBP, which combines miLBP and DRLBP. This method achieves the best results for registration with rotational transformations.…”

Section: Introductionmentioning

confidence: 99%

Multi-modal Brain MR Image Registration using A Novel Local Binary Descriptor based on Statistical Approach

Borvornvitchotikarn

2022

IJACSA

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“…At present, when comparing registration algorithms, researchers use target registration error (TRE) [35,49] as the evaluation standard in most situations, which is defined as the distance between the registered image and the physical target position. It can be expressed by the following formula:…”

Section: Evaluation Standardmentioning

confidence: 99%

“…The registration accuracy in multimodal image registration is generally better than SSC. Subsequently, the robust self-similarity descriptor (RSSD) was proposed by Kurutach et al [35] to overcome the shortcoming that the miLBP descriptor does not have rotational invariance, which further improves the accuracy of multimodal image registration.…”

Section: Introductionmentioning

confidence: 99%

Brain MR Multimodal Medical Image Registration Based on Image Segmentation and Symmetric Self-similarity

Yang¹,

Kuang²,

Yang³

et al. 2020

KSII TIIS

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With the development of medical imaging technology, image registration has been widely used in the field of disease diagnosis. The registration between different modal images of brain magnetic resonance (MR) is particularly important for the diagnosis of brain diseases. However, previous registration methods don't take advantage of the prior knowledge of bilateral brain symmetry. Moreover, the difference in gray scale information of different modal images increases the difficulty of registration. In this paper, a multimodal medical image registration method based on image segmentation and symmetric self-similarity is proposed. This method uses modal independent self-similar information and modal consistency information to register images. More particularly, we propose two novel symmetric self-similarity constraint operators to constrain the segmented medical images and convert each modal medical image into a unified modal for multimodal image registration. The experimental results show that the proposed method can effectively reduce the error rate of brain MR multimodal medical image registration with rotation and translation transformations (average 0.43mm and 0.60mm) respectively, whose accuracy is better compared to state-of-the-art image registration methods.

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Multi-modal image registration in the presence of spatially varying intensity distortion using structural representation

Aghajani

2021

Multimed Tools Appl

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Robust Self-Similarity Descriptor for Multimodal Image Registration

Cited by 4 publications

References 13 publications

Multi-modal Brain MR Image Registration using A Novel Local Binary Descriptor based on Statistical Approach

Multi-modal Brain MR Image Registration using A Novel Local Binary Descriptor based on Statistical Approach

Brain MR Multimodal Medical Image Registration Based on Image Segmentation and Symmetric Self-similarity

Multi-modal image registration in the presence of spatially varying intensity distortion using structural representation

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