Recent developments and trends in point set registration methods

Maiseli, Baraka; Gu, Yanfeng; Gao, Huijun

doi:10.1016/j.jvcir.2017.03.012

Cited by 112 publications

(51 citation statements)

References 127 publications

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“…Robustness, speed, convergence, and stability are also frequently used to evaluate and compare the performance of numerous PMs [3]. The design of the evaluation image data sets, the definition of the corresponding ground truth and accuracy, the selection of evaluation criteria, the design of evaluation metrics and the design of the evaluation protocol are prerequisites in the standardization of medical image evaluation methodology [79].…”

Section: Assessment Of the Geometric Transformationmentioning

confidence: 99%

“…Transformation models are usually chosen according to different criteria, including the deformation of point sets, time and accuracy requirements of the registration, and the signal to noise ratio. Point set matching algorithms are the fundamental methods used to find the geometric transformation between two point sets, and have gained immense developments in image registration [3,4].…”

Section: Introductionmentioning

confidence: 99%

“…Markelj et al [1] surveyed the techniques used in 3D/2D registration methods for image-guided vascular interventions where CT, MRI, and X-ray images are the fundamental resources. In their paper in 2017, they also covered numerous available PMs as well as methods to assess them [3]. Matl et al [2] contributed a living review of vascular image registration in which abundant vessel based registration methods were discussed from variant aspects.…”

Section: Introductionmentioning

confidence: 99%

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A Review of Point Feature Based Medical Image Registration

Guan

Wang

Cai

et al. 2018

Chin. J. Mech. Eng.

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Point features, as the basis of lines, surfaces, and bodies, are commonly used in medical image registration. To obtain an elegant spatial transformation of extracted feature points, many point set matching algorithms (PMs) have been developed to match two point sets by optimizing multifarious distance functions. There are ample reviews related to medical image registration and PMs which summarize their basic principles and main algorithms separately. However, to data, detailed summary of PMs used in medical image registration in different clinical environments has not been published. In this paper, we provide a comprehensive review of the existing key techniques of the PMs applied to medical image registration according to the basic principles and clinical applications. As the core technique of the PMs, geometric transformation models are elaborated in this paper, demonstrating the mechanism of point set registration. We also focus on the clinical applications of the PMs and propose a practical classification method according to their applications in different clinical surgeries. The aim of this paper is to provide a summary of pointfeature-based methods used in medical image registration and to guide doctors or researchers interested in this field to choose appropriate techniques in their research.

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Section: Assessment Of the Geometric Transformationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Review of Point Feature Based Medical Image Registration

Guan

Wang

Cai

et al. 2018

Chin. J. Mech. Eng.

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“…e point cloud registration approaches have several applications in computer vision, pattern recognition, object detection, pose estimation, medical image analysis, modeling, and feature extraction [10]. ese algorithms present several advantages: they have lower computational cost compared to intensity-based methods, they can be minimally sensitive to missing data or outliers, and because they do not consider information intensity, they are immune to additive or multiplicative noise, which are common artefacts in US fetal images.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Learning Coherent Point Drift for 3D Ultrasound Fetal Head Registration

Perez‐Gonzalez

Cosı́o

Huegel

et al. 2020

Computational and Mathematical Methods in Medicine

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Quantification of brain growth is crucial for the assessment of fetal well being, for which ultrasound (US) images are the chosen clinical modality. However, they present artefacts, such as acoustic occlusion, especially after the 18th gestational week, when cranial calcification appears. Fetal US volume registration is useful in one or all of the following cases: to monitor the evolution of fetometry indicators, to segment different structures using a fetal brain atlas, and to align and combine multiple fetal brain acquisitions. This paper presents a new approach for automatic registration of real 3D US fetal brain volumes, volumes that contain a considerable degree of occlusion artefacts, noise, and missing data. To achieve this, a novel variant of the coherent point drift method is proposed. This work employs supervised learning to segment and conform a point cloud automatically and to estimate their subsequent weight factors. These factors are obtained by a random forest-based classification and are used to appropriately assign nonuniform membership probability values of a Gaussian mixture model. These characteristics allow for the automatic registration of 3D US fetal brain volumes with occlusions and multiplicative noise, without needing an initial point cloud. Compared to other intensity and geometry-based algorithms, the proposed method achieves an error reduction of 7.4% to 60.7%, with a target registration error of only 6.38 ± 3.24 mm. This makes the herein proposed approach highly suitable for 3D automatic registration of fetal head US volumes, an approach which can be useful to monitor fetal growth, segment several brain structures, or even compound multiple acquisitions taken from different projections.

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“…For non-contact inspection, the most widely used point set matching method is based on the iterative closest point (ICP) algorithm (Maiseli et al, 2017). Recent years, some scholars have developed improved ICP algorithm and applied them to point cloud matching (Du et al, 2016;Maier-Hein et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Posture adjustment of workpiece based on stepwise matching by self-adaptive differential evolution algorithm

Jiang

Zou

et al. 2018

Mech. Sci.

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Abstract. The workpiece contour errors from previous process affect current and subsequent process. In order to improve the uniformity of workpiece allowance distribution, a stepwise workpiece matching adjustment method with contact inspection is developed. This method includes two stepwise registration processes. At first, some pairs of measured points on the theoretical and actual surfaces are selected to build the corresponding local coordinate systems, and then the rough matching matrixes are obtained by the coordinate systems alignment. During the fine matching process, the objective function based on the least square method is established by the measured point sets adjusted through the rough matching. The fine matching matrixes can be obtained by selfadaptive differential evolution algorithm. The posture adjustment can be realized by transforming coordinate systems of 5 axis machine tool, of which adjusted values can be calculated by the matching matrixes and the machine tool topology. At last, some experiments were presented to demonstrate the performance of the method.

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Recent developments and trends in point set registration methods

Cited by 112 publications

References 127 publications

A Review of Point Feature Based Medical Image Registration

A Review of Point Feature Based Medical Image Registration

Probabilistic Learning Coherent Point Drift for 3D Ultrasound Fetal Head Registration

Posture adjustment of workpiece based on stepwise matching by self-adaptive differential evolution algorithm

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