3D point cloud sensors for low-cost medical in-situ visualization

Placitelli, Alessio Pierluigi; Gallo, Luigi

doi:10.1109/bibmw.2011.6112435

Cited by 10 publications

(3 citation statements)

References 5 publications

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“…Local features are weighted, synthesized and aggregated into global descriptors as well as coordinates of control points, which are mean coordinates across local subsets of points. Utilizing control points instead of key-points allows the available geometrical data to be better exploited [16]…”

Section: Overviewmentioning

confidence: 99%

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Point Reg Net: Invariant Features for Point Cloud Registration Using in Image-Guided Radiation Therapy

Ma¹,

Liu²,

Zhou³

et al. 2018

JCC

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In image-guided radiation therapy, extracting features from medical point cloud is the key technique for multimodality registration. This novel framework, denoted Control Point Net (CPN), provides an alternative to the common applications of manually designed key-point descriptors for coarse point cloud registration. The CPN directly consumes a point cloud, divides it into equally spaced 3D voxels and transforms the points within each voxel into a unified feature representation through voxel feature encoding (VFE) layer. Then all volumetric representations are aggregated by Weighted Extraction Layer which selectively extracts features and synthesize into global descriptors and coordinates of control points. Utilizing global descriptors instead of local features allows the available geometrical data to be better exploited to improve the robustness and precision. Specifically, CPN unifies feature extraction and clustering into a single network, omitting time-consuming feature matching procedure. The algorithm is tested on point cloud datasets generated from CT images. Experiments and comparisons with the state-of-the-art descriptors demonstrate that CPN is highly discriminative, efficient, and robust to noise and density changes.

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Section: Overviewmentioning

confidence: 99%

“…[19] presents an 2-D/3-D registration based on CNN regression using for image-guided interventions. [16] explores the use of 3D point cloud sensors in medical augmented reality applications.…”

Section: Related Workmentioning

confidence: 99%

Point Reg Net: Invariant Features for Point Cloud Registration Using in Image-Guided Radiation Therapy

Ma¹,

Liu²,

Zhou³

et al. 2018

JCC

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“…However, optical methods require the use of a complicated image and signal analysis algorithm [14,15] to obtain the vibration frequency, and lighting conditions are also critical in the measurement [8,16]. e development of the depth sensor has unlocked new opportunities for researchers to utilize depth information to provide a device the capability to observe and detect realworld targets beyond human recognition; for instance, highaccuracy object recognition and tracking [17], SLAM application [18][19][20], high security level face recognition [21,22], augmented reality [23], human postural recognition, and distant medic [24][25][26]. In recent years, the use of low-cost consumer level depth sensing input devices such as Intel RealSense and Microsoft Kinect have received significant research attention thereby extending the range of application of depth sensors.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network for Vibration Frequency Measurement Using Kinect V2

Liu

Yang

2019

Shock and Vibration

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Optical measurement can substantially reduce the required amount of labor and simplify the measurement process. Furthermore, the optical measurement method can provide full-field measurement results of the target object without affecting the physical properties of the measurement target, such as stiffness, mass, or damping. The advent of consumer grade depth cameras, such as the Microsoft Kinect, Intel RealSence, and ASUS Xtion, has attracted significant research attention owing to their availability and robustness in sampling depth information. This paper presents an effective method employing the Kinect sensor V2 and an artificial neural network for vibration frequency measurement. Experiments were conducted to verify the performance of the proposed method. The proposed method can provide good frequency prediction within acceptable accuracy compared to an industrial vibrometer, with the advantages of contactless process and easy pipeline implementation.

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