Reconstruction of volumetric ultrasound panorama based on improved 3D SIFT

Ni, Dong; Chui, Yim Pan; Qu, Yingge; Yang, Xuan; Qin, Jing; Wong, Tak‐Ming; Ho, Simon S.H.; Heng, Pheng‐Ann

doi:10.1016/j.compmedimag.2009.05.006

Cited by 52 publications

(56 citation statements)

References 20 publications

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“…We found the success rate to vary between 85% and 97%, with a higher success rate (.95%) in those cases when we either considered a higher number of slice pairs or limited the search range in the axial direction. The results are comparable with those of Ni et al [11], who demonstrated 83% success for non-clinical data and 85% for clinical data. These results were only based on one clinical data set, rather than a range of patients [11].…”

Section: Discussionsupporting

confidence: 82%

“…The results are comparable with those of Ni et al [11], who demonstrated 83% success for non-clinical data and 85% for clinical data. These results were only based on one clinical data set, rather than a range of patients [11]. Our results, from 47 clinical data sets, are therefore more reflective of the clinical scenario when a registration algorithm would be required to work on a range of patients.…”

Section: Discussionsupporting

confidence: 82%

“…However, imagebased similarity measures can be very overlap-dependent [10]. This, combined with the fact that position sensors may not be well suited to routine clinical use, has led to the development of a modified version of 3D SIFT (scale-invariant feature transform) to globally register ultrasound volumes [11], which, rather than using an intensity-based approach, uses common features (blobs and corners) to match two volumes without using any position tracker. The disadvantage to this method is that it requires the pre-processing of volumes to remove artefacts.…”

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confidence: 99%

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A clinical system for three-dimensional extended-field-of-view ultrasound

Dyer

Ijaz²,

Housden³

et al. 2012

BJR

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Objective: This work is concerned with the creation of three-dimensional (3D) extended-field-of-view ultrasound from a set of volumes acquired using a mechanically swept 3D probe. 3D volumes of ultrasound data can be registered by attaching a position sensor to the probe; this can be an inconvenience in a clinical setting. A position sensor can also cause some misalignment due to patient movement and respiratory motion. We propose a combination of three-degrees-of-freedom image registration and an unobtrusively integrated inertial sensor for measuring orientation. The aim of this research is to produce a reliable and portable ultrasound system that is able to register 3D volumes quickly, making it suitable for clinical use. Method: As part of a feasibility study we recruited 28 pregnant females attending for routine obstetric scans to undergo 3D extended-field-of-view ultrasound. A total of 49 data sets were recorded. Each registered data set was assessed for correct alignment of each volume by two independent observers. Results: In 77-83% of the data sets more than four consecutive volumes registered. The successful registration relies on good overlap between volumes and is adversely affected by advancing gestational age and foetal movement. Conclusion: The development of reliable 3D extended-field-of-view ultrasound may help ultrasound practitioners to demonstrate the anatomical relation of pathology and provide a convenient way to store data. Three-dimensional (3D) ultrasound has become increasingly popular in recent years, providing an efficient framework for safe, low-cost volumetric imaging. The main clinical applications are at present in obstetrics, in areas such as assessment of cleft lip [1,2]. Other research groups have explored 3D ultrasound in areas such as cardiac assessment [3,4] and evaluation of the foetal brain [2, 5] using a single 3D volume. In our experience at a tertiary referral centre, 3D ultrasound is not routinely used despite it being readily available. The combination of 3D ultrasound and extended field of view may facilitate a wider use of 3D ultrasound by allowing ultrasound practitioners to image larger structures. It is well recognised that ultrasound image acquisition and interpretation is more operator-dependent than other imaging modalities such as CT or MRI. Providing 3D extended-field-of-view ultrasound may aid the interpretation of ultrasound images by demonstrating the anatomical relations of pathology pictorially to clinicians.There are three ways to acquire 3D data [6]. The most obvious one is to have a 2D array of transducer elements in contact with the patient. This can record 3D data directly by using different combinations of elements to scan the volume of interest. Because dense arrays of 2D transducers are difficult to build, alternative approaches have been developed using 1D transducer arrays to produce 2D B-scan images at known locations in 3D space. The second group of techniques, called freehand 3D ultrasound, involves letting the clinician sweep the 1D transdu...

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Section: Discussionsupporting

confidence: 82%

Section: Discussionsupporting

confidence: 82%

mentioning

confidence: 99%

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A clinical system for three-dimensional extended-field-of-view ultrasound

Dyer

Ijaz²,

Housden³

et al. 2012

BJR

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show abstract

“…The extension of the SIFT approach to three dimensional data has been attempted by several researchers [2,6,7,16,17,19]. Scovanner et al [19] created a 3D SIFT descriptor for application to action recognition in video volumes and additionally work has been encountered in the application of 3D SIFT to medical registration [2,6,17] or panoramic medical image stitching [7,16]. The use of SIFT for 2D object recognition relies on objects having textures internal to their boundary such that these regions can be reliably described from one image to another.…”

Section: Introductionmentioning

confidence: 99%

Object Recognition using 3D SIFT in Complex CT Volumes

Flitton¹,

Breckon²,

Bouallagu³

2010

Procedings of the British Machine Vision Conference 2010

101

116

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The automatic detection of objects within complex volumetric imagery is becoming of increased interest due to the use of dual energy Computed Tomography (CT) scanners as an aviation security deterrent. These devices produce a volumetric image akin to that encountered in prior medical CT work but in this case we are dealing with a complex multi-object volumetric environment including significant noise artefacts. In this work we look at the application of the recent extension to the seminal SIFT approach to the 3D volumetric recognition of rigid objects within this complex volumetric environment. A detailed overview of the approach and results when applied to a set of exemplar CT volumetric imagery is presented.

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“…Ni improved the SIFT (scale-invariant feature transform) algorithm to register ultrasound volumes acquired globally from dedicated ultrasound probes [7]. Bulatov assumed that camera matrices and a sparse set of 3D points are available and computed dense 3D point clouds from a sequential set of images [8].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Method for Terrain Reconstruction Based on Descent Images

Zheng

et al. 2016

J. Eng. Technol. Sci.

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Abstract. An optimization method is proposed to perform high-accuracy terrain reconstruction of the landing area of Chang'e III. First, feature matching is conducted using geometric model constraints. Then, the initial terrain is obtained and the initial normal vector of each point is solved on the basis of the initial terrain. By changing the vector around the initial normal vector in small steps a set of new vectors is obtained. By combining these vectors with the direction of light and camera, the functions are set up on the basis of a surface reflection model. Then, a series of gray values is derived by solving the equations. The new optimized vector is recorded when the obtained gray value is closest to the corresponding pixel. Finally, the optimized terrain is obtained after iteration of the vector field. Experiments were conducted using the laboratory images and descent images of Chang'e III. The results showed that the performance of the proposed method was better than that of the classical feature matching method. It can provide a reference for terrain reconstruction of the landing area in subsequent moon exploration missions. Keywords:Chang'e III; descent images; feature point extraction; geometric constraint; terrain reconstruction. IntroductionIn lunar exploration, high-precision terrain reconstruction of the landing area has significance for achieving the scientific objectives of future projects and is important for safety and efficient path planning of the rover [1]. The Chang'e II detector acquired a large number of lunar images and other information in previous lunar exploration projects. Digital orthophoto maps with 1.5 m resolution and digital elevation models with 4 m resolution are currently being produced using the existing images captured by the Chang'e II satellite [2][3][4]. However, a terrain reconstruction with this accuracy can only be used for macro scientific investigation and cannot be used for path planning of the rover of Chang'e III [5]. The navigation cameras attached to the Chang'e III rover have a high resolution and can provide the basis for high-precision terrain reconstruction. However, the use of navigation cameras was not conducive to

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Reconstruction of volumetric ultrasound panorama based on improved 3D SIFT

Cited by 52 publications

References 20 publications

A clinical system for three-dimensional extended-field-of-view ultrasound

A clinical system for three-dimensional extended-field-of-view ultrasound

Object Recognition using 3D SIFT in Complex CT Volumes

An Optimized Method for Terrain Reconstruction Based on Descent Images

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