A detector of structural similarity for multi-modal microscopic image registration
- Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun
- Date: 2018
- Type: Text , Journal article
- Relation: Multimedia Tools and Applications Vol. 77, no. 6 (2018), p. 7675-7701
- Full Text: false
- Reviewed:
- Description: This paper presents a Detector of Structural Similarity (DSS) to minimize the visual differences between brightfield and confocal microscopic images. The context of this work is that it is very challenging to effectively register such images due to a low structural similarity in image contents. To address this issue, DSS aims to maximize the structural similarity by utilizing the intensity relationships among red-green-blue (RGB) channels in images. Technically, DSS can be combined with any multi-modal image registration technique in registering brightfield and confocal microscopic images. Our experimental results show that DSS significantly increases the visual similarity in such images, thereby improving the registration performance of an existing state-of-the-art multi-modal image registration technique by up to approximately 27%. © 2017, Springer Science+Business Media New York.
COREG : A corner based registration technique for multimodal images
- Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun
- Date: 2018
- Type: Text , Journal article
- Relation: Multimedia Tools and Applications Vol. 77, no. 10 (2018), p. 12607-12634
- Full Text: false
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- Description: This paper presents a COrner based REGistration technique for multimodal images (referred to as COREG). The proposed technique focuses on addressing large content and scale differences in multimodal images. Unlike traditional multimodal image registration techniques that rely on intensities or gradients for feature representation, we propose to use contour-based corners. First, curvature similarity between corners are for the first time explored for the purpose of multimodal image registration. Second, a novel local descriptor called Distribution of Edge Pixels Along Contour (DEPAC) is proposed to represent the edges in the neighborhood of corners. Third, a simple yet effective way of estimating scale difference is proposed by making use of geometric relationships between corner triplets from the reference and target images. Using a set of benchmark multimodal images and multimodal microscopic images, we will demonstrate that our proposed technique outperforms a state-of-the-art multimodal image registration technique. © 2017, Springer Science+Business Media, LLC.
Enhancing image registration performance by incorporating distribution and spatial distance of local descriptors
- Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun
- Date: 2018
- Type: Text , Journal article
- Relation: Pattern Recognition Letters Vol. 103, no. (2018), p. 46-52
- Full Text: false
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- Description: A data dependency similarity measure called mp-dissimilarity has been recently proposed. Unlike ℓp-norm distance which is widely used in calculating the similarity between vectors, mp-dissimilarity takes into account the relative positions of the two vectors with respect to the rest of the data. This paper investigates the potential of mp-dissimilarity in matching local image descriptors. Moreover, three new matching strategies are proposed by considering both ℓp-norm distance and mp-dissimilarity. Our proposed matching strategies are extensively evaluated against ℓp-norm distance and mp-dissimilarity on a few benchmark datasets. Experimental results show that mp-dissimilarity is a promising alternative to ℓp-norm distance in matching local descriptors. The proposed matching strategies outperform both ℓp-norm distance and mp-dissimilarity in matching accuracy. One of our proposed matching strategies is comparable to ℓp-norm distance in terms of recall vs 1-precision. © 2018 Elsevier B.V.
Enhancing SIFT-based image registration performance by building and selecting highly discriminating descriptors
- Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun
- Date: 2016
- Type: Text , Journal article
- Relation: Pattern Recognition Letters Vol. 84, no. (2016), p. 156-162
- Full Text: false
- Reviewed:
- Description: In this paper we will investigate the gradient utilization in building SIFT (Scale Invariant Feature Transform)-like descriptors for image registration. There are generally two types of gradient information, i.e. gradient magnitude and gradient occurrence, which can be used for building SIFT-like descriptors. We will provide a theoretical analysis on the effectiveness of each of the two types of gradient information when used individually. Based on our analysis, we will propose a novel technique which systematically uses both types of gradient information together for image registration. Moreover, we will propose a strategy to select keypoint matches with a higher discrimination. The proposed technique can be used for both mono-modal and multi-modal image registration. Our experimental results show that the proposed technique improves registration accuracy over existing SIFT-like descriptors. © 2016 Elsevier B.V.
A novel multi-modal image registration method based on corners
- Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun
- Date: 2015
- Type: Text , Conference proceedings
- Relation: 2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014, Wollongong, New South Wales, 25th-27th November 2014
- Full Text: false
- Description: This paper presents a novel method for registering multi-modal images, based on corners. The proposed method is motivated by the fact that large content differences are likely to occur in multi-modal images. Unlike traditional multi-modal image registration methods that utilize intensities or gradients for feature representation, we propose to use curvatures of corners. Moreover, a novel local descriptor called Distribution of Edge Pixels Along Contour (DEPAC) is proposed to represent the neighborhood of corners. Curvature and DEPAC similarities are combined in our method to improve the registration accuracy. Using a set of benchmark multi-modal images and multi-modal microscopic images, we demonstrate that our proposed method outperforms an existing state-of-the-art image registration method.
Detection of structural similarity for multimodal microscopic image registration
- Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun , Lackmann, Martin
- Date: 2013
- Type: Text , Conference paper
- Relation: 2013 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2013
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- Reviewed:
- Description: In this paper we propose a novel method to detect the structural similarity in registering color and confocal microscopic images. Our prior work [1] presented the basic idea of detecting the structural similarity of such images, which utilizes the intensity relationships among red-green-blue color channels. The work in this paper will make the detection of structural similarity automatic and adaptive to each individual color microscopic image. The experimental results will demonstrate the effectiveness of the proposed method in detecting the structural similarity of these images and significant improvements in the registration performance.
Maximizing structural similarity in multimodal biomedical microscopic images for effective registration
- Authors: Lv, Guohua , Teng, Shyh , Lu, Guojun , Lackmann, Martin
- Date: 2013
- Type: Text , Conference paper
- Relation: 2013 IEEE International Conference on Multimedia and Expo (ICME)
- Full Text: false
- Reviewed:
- Description: Multimodal image registration (MMIR) is the alignment of contents in images captured from different sensors or instruments. MMIR is important in medical applications as it enables the visualization of the complementary contents in biomedical microscopic images. The registration for such images can be challenging as the structures of their contents are usually only partially similar. Thus in this paper, we propose a new method to maximize the structural similarity of the contents in such images by utilizing intensity relationships among Red-Green-Blue color channels. Our experimental results will demonstrate that our proposed method substantially improves the accuracy of registering such images as compared to the state-of-the-art methods.
Improved symmetric-SIFT for Multi-modal image registration
- Authors: Hossain, Md. Tanvir , Lv, Guohua , Teng, Shyh , Lu, Guojun , Lackmann, Martin
- Date: 2011
- Type: Text , Conference paper
- Relation: 2011 International Conference on Digital Image Computing: Techniques and Applications p. 197-202
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- Description: Multi-modal image registration has received significant research attention over the past decade. SymmetricSIFT is a recently proposed local description technique that can be used for registering multi-modal images. It is based on a well-known general image registration technique named Scale Invariant Feature Transform (SIFT). Symmetric-SIFT, however, achieves this invariance to multi-modality at the cost of losing important information. In this paper, we show how this loss may adversely affect the accuracy of registration results. We then propose an improvement to Symmetric-SIFT to overcome the problem. Our experimental results show that the proposed technique can improve the number of true matches by up to 10 times and overall matching accuracy by up to 30%.
Improving SIFT's performance by incorporating appropriate gradient information
- Authors: Lv, Guohua , Hossain, Md. Tanvir , Teng, Shyh , Lu, Guojun , Lackmann, Martin
- Date: 2011
- Type: Text , Conference paper
- Relation: 26th Image and Vision Computing New Zealand Conference (IVCNZ 2011) p. 381 - 386
- Full Text: false
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- Description: Scale Invariant Feature Transform (SIFT) has been applied in numerous applications especially in the domain of computer vision. In these applications, image information used for building the SIFT descriptor can have a significant impact on its performance. When building orientation histograms for descriptors, a critical step is how to increment the values in the orientation bins. The original scheme for this step in SIFT was improved in [6]. Two different types of gradient information are used for building orientation histograms. The limitations of the two schemes are identified in this paper and we then propose three new schemes which use both types of gradient information in the feature description and matching stages. Our experimental results show that the proposed schemes can achieve better registration performances than the schemes proposed in SIFT and [6].