A Review on Medical Image Registration as an Optimization Problem

Song, Guoli; Han, Jianda; Zhao, Yiwen; Wang, Zheng; Du, Huibin

doi:10.2174/1573405612666160920123955

Cited by 65 publications

(56 citation statements)

References 97 publications

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“…In imaging processing, methods that provide the ability to view objects within the human body are of interest. Advances in computer technology have led to the advance of accurate and operative image processing systems, which are helpful in the medical field for diagnosis, treatment, planning, and research [9]. Incorporation of valuable data received from distinct images is frequently desirable for clinical diagnosis.…”

Section: Background and Literature Reviewmentioning

confidence: 99%

Algorithm Selection in Multimodal Medical Image Registration

Elkeshreu

Basir

2020

IRA-JAS

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Over the past few decades, fast<strong>-</strong>growth has occurred in the area of medical image acquisition devices, and physicians now rely on the utilization of medical images for the diagnosis, treatment plans, and surgical guidance. Researchers have classified medical images according to two structures: anatomical and functional structures. Due to this classification, the data obtained from two or more images of the same object frequently provide complementary and more abundant information through a process known as multimodal medical model registration. Image registration is spatially mapping the coordinate system of the two images obtained from a different viewpoint and utilizing various sensors. Several automatic multimodal medical image registration algorithms have been introduced based on types of medical images and their applications to increase the reliability, robustness, and accuracy. Due to the diversity in imaging and the different demands<strong> </strong>for applications, there is no single registration algorithm that can do that. This paper introduces a novel method for developing a multimodal medical image registration system that can select the most accepted registration algorithm from a group of registration algorithms for a variety of input datasets. The method described here is based on a machine learning technique that selects the most promising candidate. Several experiments have been conducted, and the results reveal that the novel approach leads to considerably faster reliability, accuracy, and more robustness registration algorithm selection.

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Section: Background and Literature Reviewmentioning

confidence: 99%

Algorithm Selection in Multimodal Medical Image Registration

Elkeshreu

Basir

2020

IRA-JAS

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“…Image registration techniques align two or more images of interest through transformation of the coordinates of one or more source images to the coordinate system of a target image 13 . These techniques have been successfully applied across many biomedical image domains including: X-Ray, CT scans and MRI modalities amongst many others 14 . Most image registration techniques can be recast as optimization problems where the similarity between two images is optimized after a series of affine transformations and deformations.…”

Section: Introductionmentioning

confidence: 99%

PathFlow-MixMatch for Whole Slide Image Registration: An Investigation of a Segment-Based Scalable Image Registration Method

Levy

Jackson

Haudenschild

et al. 2020

Preprint

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Image registration involves finding the best alignment between different images of the same object. In these tasks, the object in question is viewed differently in each of the images (e.g. different rotation or light conditions, etc.). In digital pathology, image registration aligns correspondent regions of tissue from different stereotactic viewpoints (e.g. subsequent deeper sections of the same tissue). These comparisons are important for histological analysis and can facilitate previously unavailable manipulations, such as 3D tissue reconstruction and cell-level alignment of immunohistochemical (IHC) and special stains. Several benchmarks have been established for evaluating image registration techniques for histological tissue; however, little work has evaluated the impact of scaling registration techniques to Giga-Pixel Whole Slide Images (WSI), which are large enough for significant memory limitations, and contain recurrent patterns and deformations that hinder traditional alignment algorithms. Furthermore, as tissue sections often contain multiple, discrete, smaller tissue fragments, it is unnecessary to align an entire image when the bulk of the image is background whitespace and tissue fragments' orientations are often agnostic of each other. We present a methodology for circumventing large-scale image registration issues in histopathology and accompanying software. By removing background pixels, parsing the slide into discrete tissue segments, and matching, orienting and registering smaller segment pairs, we recovered registrations with lower Target Registration Error (TRE) when compared to utilizing the unmanipulated WSI. We tested our technique by having a pathologist annotate landmarks from 13 pairs of differently stained liver biopsy slides, performing WSI and segment-based registration techniques, and comparing overall TRE. Preliminary results demonstrate superior performance of registering segment pairs versus registering WSI (difference of median TRE of 44 pixels, p<0.001). Segment matching within WSI is an effective solution for histology image registration but requires further testing and validation to ensure its viability for stain translation and 3D histology analysis.

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“…Image registration is related by the process to find the optimal mapping function between two images to be aligned [3]. In recent years, the registration algorithms using the similarity based on information theory have been attracted more and more attention in medical image registration, among which maximization of MI was early reported for registration of medical images from different modalities by Collignon et al [4], Maes et al [5], Wells et al [6].…”

Section: Introductionmentioning

confidence: 99%

Nonrigid Medical Image Registration Using an Information Theoretic Measure Based on Arimoto Entropy with Gradient Distributions

Liu

et al. 2019

Entropy

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This paper introduces a new nonrigid registration approach for medical images applying an information theoretic measure based on Arimoto entropy with gradient distributions. A normalized dissimilarity measure based on Arimoto entropy is presented, which is employed to measure the independence between two images. In addition, a regularization term is integrated into the cost function to obtain the smooth elastic deformation. To take the spatial information between voxels into account, the distance of gradient distributions is constructed. The goal of nonrigid alignment is to find the optimal solution of a cost function including a dissimilarity measure, a regularization term, and a distance term between the gradient distributions of two images to be registered, which would achieve a minimum value when two misaligned images are perfectly registered using limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) optimization scheme. To evaluate the test results of our presented algorithm in non-rigid medical image registration, experiments on simulated three-dimension (3D) brain magnetic resonance imaging (MR) images, real 3D thoracic computed tomography (CT) volumes and 3D cardiac CT volumes were carried out on elastix package. Comparison studies including mutual information (MI) and the approach without considering spatial information were conducted. These results demonstrate a slight improvement in accuracy of non-rigid registration.

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A Review on Medical Image Registration as an Optimization Problem

Cited by 65 publications

References 97 publications

Algorithm Selection in Multimodal Medical Image Registration

Algorithm Selection in Multimodal Medical Image Registration

PathFlow-MixMatch for Whole Slide Image Registration: An Investigation of a Segment-Based Scalable Image Registration Method

Nonrigid Medical Image Registration Using an Information Theoretic Measure Based on Arimoto Entropy with Gradient Distributions

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