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Shape registration is the process of aligning one 3D model to another. Most previous methods to align shapes with no known correspondences attempt to solve for both the transformation and correspondences iteratively. We present a shape registration approach that solves for the transformation using fuzzy correspondences to maximize the overlap betwe...
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3D city model is a representation of urban area in digital format that contains building and other information. The current approaches are using photogrammetry and laser scanning to develop 3D city model. However, these techniques are time consuming and quite costly. Besides that, laser scanning and photogrammetry need professional skills and exper...
Citations
... Modeling this uncertainty, thus, seems reasonable during the process of motion estimation in order to reliably quantify functional characteristics of the heart. A suitable framework for performing this task is 'fuzzy theory' which can handle both intrinsic and extrinsic uncertainties in such a process and has a wide range of applications in different image processing fields including feature extraction [3][4][5], image thresholding [6], image segmentation [7], curve alignment [8,9], motion estimation [10] and point-set matching [11][12][13][14]. Considering the potential of the fuzzy theory and also the uncertainties (i.e., fuzziness) associated with the 3D echocardiographic cardiac motion estimation, a fuzzy non-rigid registration algorithm is proposed for efficient quantification of cardiac function. ...
... was modified through the first modification rule (14). (14) where and were determined by solving the following optimization problem: (15) Let ...
... was modified through the first modification rule (14). (14) where and were determined by solving the following optimization problem: (15) Let ...
Background and Objective
: Non-rigid image registration is a well-established method for estimating cardiac motion on 3D echocardiographic images. However, such images have relatively poor spatio-temporal resolution making registration challenging. Some of the main challenges are extracting features relevant to the registration problem and defining a suitable geometrical transformation to be applied. The latter can be tackled using a fuzzy inference system considering its potential in transformation modeling. From this point of view, feature-based image registration can be considered an identification problem in which the transformation parameters are computed through an optimization process. This study, thus, aims to estimate cardiac motion on 3D echocardiographic images based on feature-based non-rigid image registration through sets of modified fuzzy rules.
Methods
: The 3D volume features were extracted with the popular scale-invariant feature transform (SIFT) descriptors in 3D space. Sets of fuzzy rules were generated according to the extracted features to register every two consecutive frames. Finally, some supplementary rules modified the registration rule for estimating cardiac motion.
Results
: Applying the fuzzy feature-based inference system on the STRAUS synthetic database showed the proposed method to be competitive with other well-established registration algorithms in terms of tracking error and accuracy of strain estimates. The proposed algorithm yielded a tracking error of 1 mm and a relative circumferential strain error of 0.82±4.69%. In addition, the potential of the proposed algorithm for clinical applications was confirmed by evaluating its performance on an in-vivo database called CETUS. Conclusion: This paper proposes a novel registration method based on fuzzy logic which was shown to enable tracking complex cardiac deformations in 3D echocardiographic images with high accuracy.
... [12] We created 3-D mesh models using novel computer vision algorithms to perform shape reconstruction, shape modeling and registration, and generate data files, which were then used to render 3-D models using the Unreal Engine version 4 software. [8,[13][14][15][16] The 3D models were reconstructed by the segmentation process by first fitting a hybrid shape model to the segmented contours, and then using the model parameters to render a 3-D mesh of desired resolution. [8,14] All structures were color-coded and visualized either as 3-D wireframes or full 3-D mesh surfaces. ...
... The application also allowed us to record short-stereo laparoscopic video clips from the surgery, use the stereo images to perform 3-D reconstruction of the scene, align it to the 3-D models reconstructed from preoperative MRI data, and visualize them together (Figure 3 and 4). [8,16] Experimental setup and performance A recording system was connected to the stereo Digital Visual Interface output ports of the da Vinci surgical system using Startech ® Universal Serial Bus (USB) 3.0 video capture devices, which allowed us to record short uncompressed, synchronized stereo videos at 60 frames per second. Two different machines were used in developing this application, we will call these the (i) imaging platform, which featured a Intel ® quad-core i7 4710HQ with 24 gigabytes of random-access memory (RAM) and (ii) a visualization platform, which featured a quad-core i7 6700K with 32 GB of RAM and an NVIDIA ® GTX 980 GPU. ...
Objective:
Increased computational power and improved visualization hardware have generated more opportunities for virtual reality (VR) applications in healthcare. In this study, we test the feasibility of a VR-assisted surgical navigation system for robotic-assisted radical prostatectomy.
Material and methods:
The prostate, all magnetic resonance imaging (MRI) visible tumors, and important anatomic structures like the neurovascular bundles, seminal vesicles, bladder, and rectum were contoured on a multiparametric MRI using an in-house segmentation software. Three-dimensional (3-D) VR models were rendered and evaluated in a side room of the operating room. While interacting with the VR platform, a real-time stereo video capture of the in situ prostate was obtained to render a second 3-D model. The MRI-based model was then overlaid on the real-time model by using an automated alignment algorithm.
Results:
Ten patients were included in this study. All MRI-based VR models were examined by surgeons immediately prior to surgery and at important steps where visualization of the tumors and their proximity to surrounding anatomic structures were critical. This was mainly during the preparation of the prostatic pedicles, neurovascular plexus, the apex, and bladder neck. All participants found the system useful, especially for tumors with locally aggressive growth patterns. For small and centrally located tumors, the system was not considered beneficial due to lack of integration into the robotic console. A fully integrated system with real-time overlays within the robotic stereo viewer was found to be the ideal scenario.
Conclusion:
We deployed a preliminary VR-assisted surgical navigation tool for robotic-assisted radical prostatectomies.
... Once we have a set of fuzzy correspondences, alignment is done by a ray-point registration method outlined in [18]. This technique handles noisy, non-uniform alignment by minimizing the distance of the source shape to the target shape while maximizing matched area on target shape. ...
3D Speckle tracking techniques are used to quantify cardiac deformation in 3D echocardiographic images. Elastic image registration methods are successful in solving 3D speckle tracking problems. However, a suitable solution should be exploited to deal with the poor spatio-temporal resolution in the echocardiographic images. That is why the registration problem may encounter some challenges in representing accurate features and defining suitable geometric transformation. The strong modeling ability of a fuzzy rule-based inference system can aid the challenge in geometric modeling. This paper, thus, aims to solve the 3D speckle tracking problem in a new scheme through a fuzzy modeling procedure. The algorithm begins to work by extracting a well-suited local feature descriptor, scale- invariant feature transform (SIFT). Then, the relevant features are aligned with sets of fuzzy rules the optimum parameters of which are adaptively learned in the hybrid learning process of adaptive-neuro fuzzy inference system (ANFIS) structure. Applying the adaptive fuzzy method on STRAUS synthetic dataset yields an acceptable tracking error below 1 mm. Further, strain analysis indicates the capacity of the proposed method in discriminating pathological diagnosis from a healthy one.
