This study introduces a unified real-time fluid simulation framework for virtual surgical simulation. In the context of this work, we aim to simulate thermo- and fluid dynamics in our Virtual Rotator Cuff Arthroscopic Skill Trainer (ViRCAST). Fluid Dynamics directly dictates the flow of the irrigation solutions, and thermodynamics dictates the electrocautery procedure in arthroscopic procedures. An efficient and generalized numerical approach is essential for robust modeling of liquid and heat flow, as the medium of fluid flow changes continuously as the simulation progresses. In addition, these physics models should work in harmony while interacting. We used a numerical computational technique called Smoothed Particle Hydrodynamics (SPH) to model heat transfer and liquid simulation under a single framework. Our formulation intends to virtually simulate the complete fluid motion of the irrigation solution and the mechanism of conduction and convection of heat flow within the shoulder cavity in real time. In addition to the core function of the systems, our study also uses various methods to simulate the interaction between the physical systems and models. In this study, we present the preliminary results of our unified SPH approach to simulate the electrocautery process and liquid flow in the context of arthroscopic rotator cuff surgery in ViRCAST. We investigated the solver's performance by recording the average frames per second (FPS) for the simulation, which is executed for a minute. The average FPS was 202 Hz. We parallelized our framework at the loop level using OpenMP, which resulted in an increase of 46% in FPS.

BACKGROUND: Dermoscopy is one of the common and effective imaging techniques in diagnosis of skin cancer, especially for pigmented lesions. Accurate skin lesion border detection is the key to extract important dermoscopic features of the skin lesion. In current clinical settings, border delineation is performed manually by dermatologists. Operator based assessments lead to intra- and inter-observer variations due to its subjective nature. Moreover it is a tedious process. Because of aforementioned hurdles, the automation of lesion boundary detection in dermoscopic images is necessary. In this study, we address this problem by developing a novel skin lesion border detection method with a robust edge indicator function, which is based on a meshless method. RESULT: Our results are compared with the other image segmentation methods. Our skin lesion border detection algorithm outperforms other state-of-the-art methods. Based on dermatologist drawn ground truth skin lesion borders, the results indicate that our method generates reasonable boundaries than other prominent methods having Dice score of 0.886 ±0.094 and Jaccard score of 0.807 ±0.133. CONCLUSION: We prove that smoothed particle hydrodynamic (SPH) kernels can be used as edge features in active contours segmentation and probability map can be employed to avoid the evolving contour from leaking into the object of interest.

BACKGROUND: This paper presents a novel approach for Generative Anatomy Modeling Language (GAML). This approach automatically detects the geometric partitions in 3D anatomy that in turn speeds up integrated non-linear optimization model in GAML for 3D anatomy modeling with constraints (e.g. joints). This integrated non-linear optimization model requires the exponential execution time. However, our approach effectively computes the solution for non-linear optimization model and reduces computation time from exponential to linear time. This is achieved by grouping the 3D geometric constraints into communities. METHODS: Various community detection algorithms (k-means clustering, Clauset Newman Moore, and Density-Based Spatial Clustering of Applications with Noise) were used to find communities and partition the non-linear optimization problem into sub-problems. GAML was used to create a case study for 3D shoulder model to benchmark our approach with up to 5000 constraints. RESULTS: Our results show that the computation time was reduced from exponential time to linear time and the error rate between the partitioned and non-partitioned approach decreases with the increasing number of constraints. For the largest constraint set (5000 constraints), speed up was over 2689-fold whereas error was computed as low as 2.2%. CONCLUSION: This study presents a novel approach to group anatomical constraints in 3D human shoulder model using community detection algorithms. A case study for 3D modeling for shoulder models developed for arthroscopic rotator cuff simulation was presented. Our results significantly reduced the computation time in conjunction with a decrease in error using constrained optimization by linear approximation, non-linear optimization solver.

BACKGROUND: Abruptness of pigment patterns at the periphery of a skin lesion is one of the most important dermoscopic features for detection of malignancy. In current clinical setting, abrupt cutoff of a skin lesion determined by an examination of a dermatologist. This process is subjective, nonquantitative, and error-prone. We present an improved computational model to quantitatively measure abruptness of a skin lesion over our previous method. To achieve this, we quantitatively analyze the texture features of a region within the lesion boundary. This region is bounded by an interior border line of the lesion boundary which is determined using level set propagation (LSP) method. This method provides a fast border contraction without a need for extensive boolean operations. Then, we build feature vectors of homogeneity, standard deviation of pixel values, and mean of the pixel values of the region between the contracted border and the original border. These vectors are then classified using neural networks (NN) and SVM classifiers. RESULTS: As lower homogeneity indicates sharp cutoffs, suggesting melanoma, we carried out our experiments on two dermoscopy image datasets, which consist of 800 benign and 200 malignant melanoma cases. LSP method helped produce better results than Kaya et al., 2016 study. By using texture homogeneity at the periphery of a lesion border determined by LSP, as a classification results, we obtained 87% f1-score and 78% specificity; that we obtained better results than in the previous study. We also compared the performances of two different NN classifiers and support vector machine classifier. The best results obtained using combination of RGB color spaces with the fully-connected multi-hidden layer NN. CONCLUSIONS: Computational results also show that skin lesion abrupt cutoff is a reliable indicator of malignancy. Results show that computational model of texture homogeneity along the periphery of skin lesion borders based on LSP is an effective way of quantitatively measuring abrupt cutoff of a lesion.

BACKGROUND: Automated skin lesion border examination and analysis techniques have become an important field of research for distinguishing malignant pigmented lesions from benign lesions. An abrupt pigment pattern cutoff at the periphery of a skin lesion is one of the most important dermoscopic features for detection of neoplastic behavior. In current clinical setting, the lesion is divided into a virtual pie with eight sections. Each section is examined by a dermatologist for abrupt cutoff and scored accordingly, which can be tedious and subjective. METHODS: This study introduces a novel approach to objectively quantify abruptness of pigment patterns along the lesion periphery. In the proposed approach, first, the skin lesion border is detected by the density based lesion border detection method. Second, the detected border is gradually scaled through vector operations. Then, along gradually scaled borders, pigment pattern homogeneities are calculated at different scales. Through this process, statistical texture features are extracted. Moreover, different color spaces are examined for the efficacy of texture analysis. RESULTS: The proposed method has been tested and validated on 100 (31 melanoma, 69 benign) dermoscopy images. Analyzed results indicate that proposed method is efficient on malignancy detection. More specifically, we obtained specificity of 0.96 and sensitivity of 0.86 for malignancy detection in a certain color space. The F-measure, harmonic mean of recall and precision, of the framework is reported as 0.87. CONCLUSIONS: The use of texture homogeneity along the periphery of the lesion border is an effective method to detect malignancy of the skin lesion in dermoscopy images. Among different color spaces tested, RGB color space's blue color channel is the most informative color channel to detect malignancy for skin lesions. That is followed by YCbCr color spaces Cr channel, and Cr is closely followed by the green color channel of RGB color space.

BACKGROUND: Dermoscopy is a highly effective and noninvasive imaging technique used in diagnosis of melanoma and other pigmented skin lesions. Many aspects of the lesion under consideration are defined in relation to the lesion border. This makes border detection one of the most important steps in dermoscopic image analysis. In current practice, dermatologists often delineate borders through a hand drawn representation based upon visual inspection. Due to the subjective nature of this technique, intra- and inter-observer variations are common. Because of this, the automated assessment of lesion borders in dermoscopic images has become an important area of study. METHODS: Fast density based skin lesion border detection method has been implemented in parallel with a new parallel technology called WebCL. WebCL utilizes client side computing capabilities to use available hardware resources such as multi cores and GPUs. Developed WebCL-parallel density based skin lesion border detection method runs efficiently from internet browsers. RESULTS: Previous research indicates that one of the highest accuracy rates can be achieved using density based clustering techniques for skin lesion border detection. While these algorithms do have unfavorable time complexities, this effect could be mitigated when implemented in parallel. In this study, density based clustering technique for skin lesion border detection is parallelized and redesigned to run very efficiently on the heterogeneous platforms (e.g. tablets, SmartPhones, multi-core CPUs, GPUs, and fully-integrated Accelerated Processing Units) by transforming the technique into a series of independent concurrent operations. Heterogeneous computing is adopted to support accessibility, portability and multi-device use in the clinical settings. For this, we used WebCL, an emerging technology that enables a HTML5 Web browser to execute code in parallel for heterogeneous platforms. We depicted WebCL and our parallel algorithm design. In addition, we tested parallel code on 100 dermoscopy images and showed the execution speedups with respect to the serial version. Results indicate that parallel (WebCL) version and serial version of density based lesion border detection methods generate the same accuracy rates for 100 dermoscopy images, in which mean of border error is 6.94%, mean of recall is 76.66%, and mean of precision is 99.29% respectively. Moreover, WebCL version's speedup factor for 100 dermoscopy images' lesion border detection averages around ~491.2. CONCLUSIONS: When large amount of high resolution dermoscopy images considered in a usual clinical setting along with the critical importance of early detection and diagnosis of melanoma before metastasis, the importance of fast processing dermoscopy images become obvious. In this paper, we introduce WebCL and the use of it for biomedical image processing applications. WebCL is a javascript binding of OpenCL, which takes advantage of GPU computing from a web browser. Therefore, WebCL parallel version of density based skin lesion border detection introduced in this study can supplement expert dermatologist, and aid them in early diagnosis of skin lesions. While WebCL is currently an emerging technology, a full adoption of WebCL into the HTML5 standard would allow for this implementation to run on a very large set of hardware and software systems. WebCL takes full advantage of parallel computational resources including multi-cores and GPUs on a local machine, and allows for compiled code to run directly from the Web Browser.

This paper presents a massively parallel implementation of a prominent network clustering algorithm, the structural clustering algorithm for networks (SCAN), on a graphical processing unit (GPU). SCAN is a fast and efficient clustering technique for finding hidden communities and isolating hubs/outliers within a network. However, for very large networks, it still takes considerable amount of time. With the introduction of massively parallel Compute Unified Device Architecture (CUDA) by Nvidia, applications properly employing GPUs are demonstrating high speed up. In current study, GPUSCAN, a CUDA based parallel implementation of SCAN, is presented. SCAN's computation steps have been carefully redesigned to run very efficiently on the GPU by transforming SCAN into a series of highly regular and independent concurrent operations. All intermediate data structures are created in the GPU to efficiently benefit from GPU's memory hierarchy. How these structures reformed and represented in the GPU memory hierarchy are illustrated. Now, through GPUSCAN, a large network or a batch of disjoint networks can be offloaded to the GPU for very fast and equivalent structural clustering. The performance of the GPU accelerated structural clustering has been shown to be much faster than the sequential CPU implementation. Both GPUSCAN and SCAN are tested on different size artificial and real-world networks. Results indicate that network becomes larger GPUSCAN significantly over performs SCAN. In tested datasets, speed-up of over 500-fold is achieved. For instance, calculating structural similarity and clustering of 5.5 million edges of the California road network in GPUSCAN is 513-fold faster than the serial version of SCAN.

Community detection algorithms play an important role in discovering knowledge in networks. The Structural Clustering Algorithm for Network (SCAN) is a community detection algorithm which is capable of detecting hubs and outliers, in addition to cluster members. The term hub means node with the ability of collecting and delivering information among clusters while outlier is considered as a noise in the data. Currently, researchers use exhaustive search to determine the structural similarity threshold value (ε) in the SCAN. This paper reports a new approach of using interval ε value to narrow the searching domain for proper ε value for the SCAN. The approach first adopts computational results produced by the Fast Modularity and the Walktrap algorithms to bind the number of clusters of a network and then determine the interval for ε value. For each of our test datasets, the interval prediction reliably finds the true number of clusters. More importantly, the proposed prediction method helps users to eliminate an average of 67.7% of inappropriate ε values used to generate clusters.

BACKGROUND: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. In current practice, dermatologists determine lesion area by manually drawing lesion borders. Therefore, automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. One of the most important steps in dermoscopy image analysis is automated detection of lesion borders. To our knowledge, in our 2010 study we achieved one of the highest accuracy rates in the automated lesion border detection field by using modified density based clustering algorithm. In the previous study, we proposed a novel method which removes redundant computations in well-known spatial density based clustering algorithm, DBSCAN; thus, in turn it speeds up clustering process considerably. FINDINGS: Our previous study was heavily dependent on the pre-processing step which creates a binary image from original image. In this study, we embed a new distance measure to the existing algorithm. This provides twofold benefits. First, since new approach removes pre-processing step, it directly works on color images instead of binary ones. Thus, very important color information is not lost. Second, accuracy of delineated lesion borders is improved on 75% of 100 dermoscopy image dataset. CONCLUSION: Previous and improved methods are tested within the same dermoscopy dataset along with the same set of dermatologist drawn ground truth images. Results revealed that the improved method directly works on color images without any pre-processing and generates more accurate results than existing method.

MOTIVATION: The medical imaging and image processing techniques, ranging from microscopic to macroscopic, has become one of the main components of diagnostic procedures to assist dermatologists in their medical decision-making processes. Computer-aided segmentation and border detection on dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopic images have become an important research field mainly because of inter- and intra-observer variations in human interpretations. In this study, a novel approach-graph spanner-for automatic border detection in dermoscopic images is proposed. In this approach, a proximity graph representation of dermoscopic images in order to detect regions and borders in skin lesion is presented. RESULTS: Graph spanner approach is examined on a set of 100 dermoscopic images whose manually drawn borders by a dermatologist are used as the ground truth. Error rates, false positives and false negatives along with true positives and true negatives are quantified by digitally comparing results with manually determined borders from a dermatologist. The results show that the highest precision and recall rates obtained to determine lesion boundaries are 100%. However, accuracy of assessment averages out at 97.72% and borders errors' mean is 2.28% for whole dataset.

BACKGROUND: Until quite recently spinal disorder problems in the U.S. have been operated by fusing cervical vertebrae instead of replacement of the cervical disc with an artificial disc. Cervical disc replacement is a recently approved procedure in the U.S. It is one of the most challenging surgical procedures in the medical field due to the deficiencies in available diagnostic tools and insufficient number of surgical practices For physicians and surgical instrument developers, it is critical to understand how to successfully deploy the new artificial disc replacement systems. Without proper understanding and practice of the deployment procedure, it is possible to injure the vertebral body. Mixed reality (MR) and virtual reality (VR) surgical simulators are becoming an indispensable part of physicians' training, since they offer a risk free training environment. In this study, MR simulation framework and intricacies involved in the development of a MR simulator for the rasping procedure in artificial cervical disc replacement (ACDR) surgery are investigated. The major components that make up the MR surgical simulator with motion tracking system are addressed. FINDINGS: A mixed reality surgical simulator that targets rasping procedure in the artificial cervical disc replacement surgery with a VICON motion tracking system was developed. There were several challenges in the development of MR surgical simulator. First, the assembly of different hardware components for surgical simulation development that involves knowledge and application of interdisciplinary fields such as signal processing, computer vision and graphics, along with the design and placements of sensors etc . Second challenge was the creation of a physically correct model of the rasping procedure in order to attain critical forces. This challenge was handled with finite element modeling. The third challenge was minimization of error in mapping movements of an actor in real model to a virtual model in a process called registration. This issue was overcome by a two-way (virtual object to real domain and real domain to virtual object) semi-automatic registration method. CONCLUSIONS: The applicability of the VICON MR setting for the ACDR surgical simulator is demonstrated. The main stream problems encountered in MR surgical simulator development are addressed. First, an effective environment for MR surgical development is constructed. Second, the strain and the stress intensities and critical forces are simulated under the various rasp instrument loadings with impacts that are applied on intervertebral surfaces of the anterior vertebrae throughout the rasping procedure. Third, two approaches are introduced to solve the registration problem in MR setting. Results show that our system creates an effective environment for surgical simulation development and solves tedious and time-consuming registration problems caused by misalignments. Further, the MR ACDR surgery simulator was tested by 5 different physicians who found that the MR simulator is effective enough to teach the anatomical details of cervical discs and to grasp the basics of the ACDR surgery and rasping procedure.

BACKGROUND: Computer-aided segmentation and border detection in dermoscopic images is one of the core components of diagnostic procedures and therapeutic interventions for skin cancer. Automated assessment tools for dermoscopy images have become an important research field mainly because of inter- and intra-observer variations in human interpretation. In this study, we compare two approaches for automatic border detection in dermoscopy images: density based clustering (DBSCAN) and Fuzzy C-Means (FCM) clustering algorithms. In the first approach, if there exists enough density--greater than certain number of points--around a point, then either a new cluster is formed around the point or an existing cluster grows by including the point and its neighbors. In the second approach FCM clustering is used. This approach has the ability to assign one data point into more than one cluster. RESULTS: Each approach is examined on a set of 100 dermoscopy images whose manually drawn borders by a dermatologist are used as the ground truth. Error rates; false positives and false negatives along with true positives and true negatives are quantified by comparing results with manually determined borders from a dermatologist. The assessments obtained from both methods are quantitatively analyzed over three accuracy measures: border error, precision, and recall. CONCLUSION: As well as low border error, high precision and recall, visual outcome showed that the DBSCAN effectively delineated targeted lesion, and has bright future; however, the FCM had poor performance especially in border error metric.

Protein sequence motifs information is crucial to the analysis of biologically significant regions. The conserved regions have the potential to determine the role of the proteins. Many algorithms or techniques to discover motifs require a predefined fixed window size in advance. Due to the fixed size, these approaches often deliver a number of similar motifs simply shifted by some bases or including mismatches. To confront the shifted motifs problem, we cooperate the Super-Rule-Tree (SRT) concept, which is designed for solving the mismatched motifs problem, and propose a new Positional Association Rules algorithm. In Positional Association Rules algorithm, a new parameter named distance assurance is created to search frequent distances appearing in association rules. By analyzing the motifs results generated by our approach on our dataset, we provide the optimal minimum support, confidence, and distance assurance. We believe the Positional Association Super-Rules algorithm can play an important role in similar researches which requires predefined fixed window size.

One of the essential components of a virtual reality surgical simulation is deformation. Deformations in computer graphics and surgical simulations are commonly modeled with three different approaches e.g. geometry based methods, Finite Element method (FEM), and Mass-spring Method (MSM). The geometry based methods are quite fast and visually appealing. Late two methods take the physics of deformation into consideration. Even though the FEM results in more physically realistic deformations, a significant drawback of this method is its expensive computation cost and vulnerability to surgical procedures such as incision. However, MSM is relatively computationally inexpensive.Because of the real-time physics based behavior of MSM, it is widely accepted by surgical simulation community. This study summarizes deformation simulation methods and their pros and cons for surgical simulations. Moreover, because of wide usage of the MSM, optimized data structures for MSM are provided and analyzed under different deformation settings.

In the last two decades virtual reality (VR) simulations have had revolutionary effects on many fields such as medicine, architecture, science, financial, and military applications. On the contrary, in medicine, the applications of VR are not as extensive as in other fields. However, realistic VR surgery simulations are in high demand as a result of offering risk-free training environments for physicians. The necessity of realism compels researchers to adopt physics-based models and haptic renderings that place extra computation burdens on real-time rendering pipelines. In this study, we investigate the usability of already built-in physics-based models in physics/or game engine PhysX library, for scooping operations in artificial cervical disc replacement (ACDR) surgery. The motivation behind our work is twofold. First, intricacies involved in ACDR surgery are introduced and the fundamental components towards the development of a surgical simulator are addressed. Second, an on-going framework development is introduced for the scooping action based on the PhysX library which provides optimized and physics-based methods for a plausible simulation. Our simulator framework integrates a haptic device into the PhysX environment for force feedback in order to increase plausibility.

Augmented reality (AR) system is a virtual environment that overlays the computer generated graphics on real-world view. A typical AR system consists of optical motion tracking and capturing. The main problem in such a system is minimizing error of mapping the movements of the actor in real domain to the virtual model. To do that, the position of markers attached to the actor needs to be carefully selected and mapped to the virtual counterpart. This one-to-one mapping process is called registration. In this regard, virtual and real domains should be aligned correctly. Any minute error in registration of these two domains results in degrading of the realism e.g. wrongly registered objects floating through each other. Current registration method for optical motion capture systems requires time-consuming and tedious manual processing. To overcome this problem, we present two-way (virtual object to real domain and real domain to virtual object) semi-automatic registration method. With our method instead of priory determining positions of markers on the physical object, we determine markers' positions (extreme points found by our application) on the virtual object and then locate markers at the approximate positions on the real object. By tracking the markers in the following step, we get markers' positions in real domain and change virtual domain's markers' positions accordingly to reduce error. Results show that our system solves the registration problem and prevents unrealistic jittering and flickering effects due to misalignment.

A process of determining whether two or more bodies are making contact at one or more points is called collision detection or intersection detection. Collision detection is inseparable part of the computer graphics, surgical simulations, and robotics. There are varieties of methods for collision detection. We will review some of the most common ones. Algorithms for contact determination can be grouped into two general parts: broad-phase and narrow-phase. This paper provides a comprehensive classification of a collision detection literature into the two phases. Moreover, we have attempted to explain some of the existing algorithms which are not easy to interpret. Also, we have tried to keep sections self-explanatory without sacrificing depth of coverage.