P6D-1 3D/4D Ultrasound Registration of Bone

Schers, Jonathan; Troccaz, Jocelyne; Daanen, Vincent; Fouard, Céline; Plaskos, Christopher; Kilian, Pascal

doi:10.1109/ultsym.2007.634

Cited by 4 publications

(7 citation statements)

References 11 publications

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“…Oscillatory functions were used to reduce noise in [17]. Another common filter to reduce noise, especially speckle noise, is median filtering [18][19][20][21][22]. A median filter works as a non-linear lowpass filter, assigning to each pixel the median of its local (2D or 3D) neighboring values.…”

Section: Noise Removalmentioning

confidence: 99%

“…Then, a fusion of the output of Otsu's method and the detected horizontal bone interface is performed. This fused result is dilated to produce the final segmented regions of interest with structural information [22]. An interactive live-wire segmentation algorithm was featured in [17] where the user selected seed points on breast and kidney ultrasound images in order to extract structural outlines of specific objects in the images.…”

Section: Image Segmentation and Feature Detectionmentioning

confidence: 99%

“…NCC calculates the correlation between two functions, and it is considered to work the best with mono-modality registration between two images acquired with the same characteristic curves (e.g., same gamma curve). The NCC metric has been incorporated in both rigid [22] and non-rigid registration algorithms [53,27] with good results.…”

Section: Well Established Similarity Metrics Used In Both Rigid and Non-rigid Registrationmentioning

confidence: 99%

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Ultrasound registration: A review

Che

Mathai

Galeotti

2017

Methods

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This article is a review of registration algorithms for use between ultrasound images (monomodal image-based ultrasound registration). Ultrasound is safe, inexpensive, and real-time, providing many advantages for clinical and scientific use on both humans and animals, but ultrasound images are also notoriously noisy and subject to several unique artifacts/distortions. This paper introduces the topic and unique aspects of ultrasound-to-ultrasound image registration, providing a broad introduction and summary of the literature and the field. Both theoretical and practical aspects are introduced. The first half of the paper is theoretical, organized according to the basic components of a registration framework, namely preprocessing, image-similarity metrics, optimizers, etc. It further subdivides these methods between those suitable for elastic (non-rigid) vs. inelastic (matrix) transforms. The second half of the paper is organized by anatomy and is practical in nature, presenting and discussing the complete published systems that have been validated for registration in specific anatomic regions.

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Section: Noise Removalmentioning

confidence: 99%

Section: Image Segmentation and Feature Detectionmentioning

confidence: 99%

Section: Well Established Similarity Metrics Used In Both Rigid and Non-rigid Registrationmentioning

confidence: 99%

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Ultrasound registration: A review

Che

Mathai

Galeotti

2017

Methods

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“…They are thus prone to identifying erroneous, suboptimal solutions if not initialized correctly, particularly if there is no smooth geometric transformation that models the differences between the images, e.g., following brain tumor resection. These methods may require initialization within a “capture range” of the correct solution, where initialization is typically provided via external labels or segmentation of regions of interest within ultrasound images and the alignment is optimized primarily for the specific labels [26,27]. Alternatively, generic salient image features have been applied using a wide variety of methods [28–32], and distinctive local neighborhoods surrounding edges and texture features can be used to identify image-to-image correspondences prior to registration [33].…”

Section: Introductionmentioning

confidence: 99%

Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching

et al. 2018

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“…Schers et al [26] developed a method for registering ultrasound volumes. In their method, a segmentation of the interface between the bone and soft tissues was obtained.…”

Section: Ultrasound Image Registrationmentioning

confidence: 99%

Motion Estimation and Registration of B-Mode Ultrasound Images for Improved Visualisation

Awwad¹

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Medical imaging is an important aspect in today's life. Different imaging techniques are used for diagnostic purposes which help in the early detection of diseases before they develop into more serious issues and cause more damage. Some of these imaging modalities are Magnetic Resonance Imaging (MRI), X-ray, Computed Tomography (CT-scan) and Ultrasound Imaging (US). Ultrasound imaging is known to be the safest among the other imaging modalities since it doesn't send ionised radiation. This thesis presents a method for motion tracking between ultrasound images that originated from moving an ultrasound probe. The motion tracking is done in two steps: In the first step, a modified block-matching search is used to track the motion between frames by tracking the speckle and the tissues in the images. From the modified blockmatching approach, the global motion vector can be seen. In the second step, areas with motion vectors in the direction of the probes' motion are put through another motion tracking algorithm called phase correlation. Doing so will improve the overall motion estimation. Phase correlation provides a smoother vector field compared to block-matching. Combining the modified blockmatching with phase correlation will increase the accuracy for motion estimation. Finally, after the motion estimation is performed successfully, image registration and averaging is performed to obtain an extended field-of-view-frame. This extended field-of-view shows the information contained in all the separate frames, in one single frame while reducing the overall noise.iii Acknowledgments I would like to thank my two supervisors, Dr. Chris Joslin and Dr. Yuu Ono for their help and support throughout the period the research was conducted. Without their help, this work would have not been possible or finished. Their guidance, comments and suggestions were of extreme help to me throughout the period of the research.

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P6D-1 3D/4D Ultrasound Registration of Bone

Cited by 4 publications

References 11 publications

Ultrasound registration: A review

Ultrasound registration: A review

Non-rigid registration of 3D ultrasound for neurosurgery using automatic feature detection and matching

Motion Estimation and Registration of B-Mode Ultrasound Images for Improved Visualisation

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