Distance Transform Research Articles

Computational tool for morphological analysis of cultured neonatal rat cardiomyocytes.

This study describes the development and evaluation of a semiautomatic myocyte edge-detector using digital image processing. The algorithm was developed in Matlab 6.0 using the SDC Morphology Toolbox. Its conceptual basis is the mathematical morphology theory together with the watershed and Euclidean distance transformations. The algorithm enables the user to select cells within an image for automatic detection of their borders and calculation of their surface areas; these areas are determined by adding the pixels within each myocyte's boundaries. The algorithm was applied to images of cultured ventricular myocytes from neonatal rats. The edge-detector allowed the identification and quantification of morphometric alterations in cultured isolated myocytes induced by 72 hours of exposure to a hypertrophic agent (50 μM phenylephrine). There was a significant increase in the mean surface area of the phenylephrine-treated cells compared with the control cells (p<;0.05), corresponding to cellular hypertrophy of approximately 50%. In conclusion, this edge-detector provides a rapid, repeatable and accurate measurement of cell surface areas in a standardized manner. Other possible applications include morphologic measurement of other types of cultured cells and analysis of time-related morphometric changes in adult cardiac myocytes.

Characterization of Gas‐Liquid Two‐Phase Flows Using Laser Patterns

Two‐phase flows play a vital role in refrigeration, air conditioning, and other industrial applications. This necessitates the development of precise techniques to characterize various two‐phase flow regimes. In the present work, characterization of two‐phase flow in horizontal tubes of diameters 4.7 mm and 3.4 mm is done by analyzing laser patterns. Laser patterns are recorded using a high‐speed camera. The area occupied by laser patterns for air‐water and air‐oil flows is analyzed by applying grayscale analysis and distance transformation techniques in image processing. A technique based on the movement of the centre of intensity of the laser pattern is used to characterize two‐phase flow regimes. Centre of intensity of a laser pattern is the point with maximum pixel intensity in the processed image. Probability density estimation together with the position‐time graph for centre of intensity is used to characterize two‐phase flow patterns. Bubbly, slug, and stratified flow regimes are observed and analyzed. The slug length and velocity is calculated by analyzing laser patterns. The two‐phase flow regime map is generated based on the identified two‐phase flow patterns and is validated with a flow map for conventional channels available in the literature.

Efficient evaluation of integrals with kernel 1/rχ for quadrilateral elements with irregular shape

In this paper, integrals with kernel 1/rχ are concerned with the following three aspects: a). the near singularity caused by distorted element shape; b). the near singularity derived from the angular direction; c). the singularity/near singularity in the radial direction. A conformal polar coordinate transformation (CPCT) is proposed to eliminate the shape effect of elements, which can keep the shape characteristic of distorted elements, and an improved sigmoidal transformation is introduced to alleviate the near singularity in the angular direction. By combination of the two strategies with existing methods, such as singularity subtraction method and distance transformation method utilized in this paper, an efficient and robust numerical integration approach can be obtained for various orders of singular/nearly singular integrals, and a distorted curved quadrilateral element extracted from a cylinder surface is provided to demonstrate the efficiency and robustness of the proposed method.

Hand tracking by extending distance transform and hand model in real-time

Tracking a human's hand is not a trivial task. This paper contributes a new approach for hand tracking based on distance transform (DT) and edge points in real-time. In the beginning, we create a hand model geometrically in three dimensions. It is done by utilizing shortened quadrics. After that, the degrees of freedom, shortly called as DOF, for every joint angle correspond to each DOF to use in the later process. The edge likelihood is used for the feature extraction. A Bayesian classifier is utilized adaptively and accurately for the silhouette likelihood. For this reason, it is to cope greatly with any environmental changes visibly. By using these techniques, this method can be performed in real-time. Experimental results are provided.

Euclidean Distance Transform (EDT) Algorithm Applied to Binary Image for Finding Breast Cancer

Euclidean Distance Transform (EDT) Algorithm Applied to Binary Image for Finding Breast Cancer

A Similarity-Based Learning Algorithm Using Distance Transformation

Numerous theories and algorithms have been developed to solve vectorial data learning problems by searching for the hypothesis that best fits the observed training sample. However, many real-world applications involve samples that are not described as feature vectors, but as (dis)similarity data. Converting vectorial data into (dis)similarity data is more easily performed than converting (dis)similarity data into vectorial data. This study proposes a stochastic iterative distance transformation model for similarity-based learning. The proposed model can be used to identify a clear class boundary in data by modifying the (dis)similarities between examples. The experimental results indicate that the performance of the proposed method is comparable with those of various vector-based and proximity-based learning algorithms.

SU‐E‐T‐349: Effective Dose‐Volume‐Histogram Prediction Method Using Euclidean Distance Volume Histogram for Volumetric Modulated Arc Therapy to Treat Prostate Cancer

Purpose:Manually checking the plan dose‐volume histograms (DVH) quality is time consuming, thereby limiting the patient throughput and leading to inconsistent plan quality across institutions. We propose to use Euclidean distance volume histogram (EDVH) to automate this process in volumetric‐modulated arch therapy (VMAT) to treat prostate cancer patients by predicting DVH of a new patient based on prior expert patients.Methods:52 prostate cancer patients treated by VMAT at our institution were randomly selected. 3D distance transformation was performed on the simulation CTs to get Euclidean distance for each voxel inside every organ‐at‐risk with respect to the tumor. EDVH was then constructed, analogous to computing a DVH. In order to evaluate our proposed method, we utilize the leave‐one‐patient‐out cross validation. For each test patient, we select 5 patients with the closest EDVHs from the remaining 51 patients and determine whether the DVH of this patient is within the band bounded by these 5 patients’ DVHs with a ratio defined as AREA5/AREA6, where AREA5 is the area‐between‐curves (ABC) bounded by the DVHs of the 5 selected patients and AREA6 is the ABC bounded by the DVHs of the 5 selected patients plus the test patient. The higher the ratio is, the better prediction performance is.Results:From the process of leave‐one‐patient‐out cross validation, we obtained a prediction ratio (0.94±0.11) for each patient. 48 out of 52 patients (92.3%) for bladder and 46 out of 52 patients (88.5%) for rectum had prediction ratios above 0.80. 41 out of 52 patients (78.9%) for bladder and 38 out of 52 patients (73.1%) for rectum had prediction ratios above 0.90.Conclusion:EDVH can effectively capture the overall geometric relationship between the tumor and organ‐at‐risks, which can be used to confidently predict the DVH based on prior expert patients for VMAT to treat prostate cancer.NIH/NCI K25CA168984, Eagles Cancer Research Career Development, The Lawrence W. and Marilyn W. Matteson Fund for Cancer Research, Mayo ASU Seed Grant, and The Kemper Marley Foundation.

429. Optimizing Intratumoral Oncolytic Virus Delivery Using Hemodynamic Manipulations

Oncolytic virotherapies like vesicular stomatitis virus (VSV) are experimental cancer therapies wherein naturally evolved or engineered viruses selectively infect tumor cells, inducing tumor cell death (oncolysis), immunotherapeutic responses, and tumor regression. Our mathematical model predicts increasing delivery, distribution, and size of intratumoral infectious centers will increase the probability of achieving tumor cure from direct oncolysis. However, currently intratumoral infection is heterogeneously distributed, resulting in intratumoral infection voids. These voids are predicted to be a result of barriers to intratumoral virus that have the potential to be targeted in order to optimize current oncolytic regimens consistent with mathematical predictions.Optimization requires the ability to accurately visualize and analyze intratumoral infection noninvasively in animals. Here we have used dynamic radiohistology in which noninvasive SPECT/CT imaging after the administration of recombinant virus expressing the sodium-iodide symporter (NIS) reporter gene is used to visualize radiotracer uptake at centers of active NIS expression correlating with intratumoral infectious centers. The SPECT/CT data was analyzed using distributive distance transformation algorithms that quantitatively assess changes in intratumoral distribution and infection voids, allowing for testing of the mathematical model predictions. This technique has proven to be valuable in observing location, size, and evolution of intratumoral infectious centers in three dimensions, and quantitative analysis of infection distribution using SPECT/CT data has the potential to increase understanding of barriers to therapeutic success.Hemodynamic changes are thought to alter tumoral perfusion pressure, a barrier to intratumoral virus delivery. Indeed, we show that increasing intratumoral perfusion pressure through an increase in systemic blood pressure results in an increase in survival matched with an increase in delivery shown using the above analytical methods. Conversely, intratumoral delivery decreases with a decrease in blood pressure. This confirms the ability to use a mathematically guided approach to inform and analyze increases in oncolytic virus therapeutic efficacy and highlights the importance of hemodynamic regulation during therapeutic administration.

View-independent action recognition: a hybrid approach

In this paper, we propose a new framework for view independent action recognition, which uses a combination of a view-dependent representation and a view-independent representation. The view-dependent representation reduces the number of possible action's labels prior to the view-independent representation. We used the entropy of silhouette's distance transformation as view-dependent representation and the self-similarity matrix of the trajectory of uniformly distributed feature points over the human body as view-independent representation. The experiment results show that the proposed method outperforms recent action recognition approaches despite its low computational cost.

Oriented Changing Light Facial Image Recognition Based on Sub Pattern Texture Analysis

Face recognition (FR) system can automatically identify or check face image from a digital camera or image generation equipment, in order to do this, to extract facial features from images obtained, and compared with face data in the database. At present, almost all of the FR face barriers associated with facial Angle, including the lack of light and the low resolution, these problems has greatly reduce the recognition rate. In order to solve this problem, this paper proposes a face recognition framework based on the sub pattern under the condition of illumination change, first of all, the framework using minimize the total variation image of discrete cosine transform (DTV) and the Gabor filter, and combined the sub-mode analysis (SMP) and distinguish the accumulative feature transformation (DAFT), can effectively solve the face recognition problem of light conditions big change, secondly by extracting the texture characteristics of local model is not sensitive to illumination changes, using Distance transformation measures (Distance Conversion Metrics, DCM) and k-means (K-Mean) algorithm, the recognition rate of face recognition is improved effectively. The effectiveness of the method verified respectively on the two face library: ATR - Jaffe and Yale, the experimental results show that compared with other state-of-the-art methods, the proposed method in dealing with the face recognition problem under the condition of the unrestricting obtained better recognition effect.

Calculating the medial axis of a CAD model by multi-CPU based parallel computation

Computational efficiency is still a great challenge for the generation of the Medial Axis (MA) for complicated CAD models. Current research mainly focuses on CPU-based MA generation methods. However, most of the methods emphasize using a single CPU. The highly-efficient methods based on parallel computing are still missing. In this study, a parallel method based on multi-CPU is proposed for the efficient MA generation of CAD models using distance dilation. By dividing the whole model into several parts for which MAs are calculated in parallel and then combined, computational efficiency can be greatly improved in theory and the computation time can be reduced nearly K times if K CPUs are used. Firstly, an adaptive division method is proposed to divide the voxelized model into blocks which have nearly the same number of voxels to balance the computational burden. Secondly, the local Euclidean Distance Transform (EDT) is calculated for each block based on the existing distance dilation method. Thirdly, the complete inter-dilation method is proposed to compute the influence between different blocks to get a global EDT for each block. Finally, each block generates a sub-MA separately and then all the generated MAs are combined to obtain the final MA. The last three processes can be efficiently conducted in parallel by using multiple CPUs. Several groups of experiments are conducted which demonstrate the good performance of the proposed methods in terms of efficiency.

Computer simulation of batik printing patterns with cracks

Generating batik printing patterns is an ancient and unique dye-resistant printing technique. Due to the dip dyeing of the cloth with wax in the dyes, the wax as a reserve dyeing agent cracks naturally, and the dyes permeate into the cloth along the cracks. It forms a natural texture and generates a unique and beautiful artwork that is difficult to produce manually. According to the characteristics of the batik printing patterns with cracks, we developed a specific method for simulation of the batik printing patterns with cracks that are relevant or not relevant to the batik patterns (BPPCNR or BPPCR). For the simulation of the process of the extension of the crack, we adapted the method of solving two-dimensional crack fracture based on the extended finite element method (EFEM) in the field of fracture mechanics. We achieved the results of cracks with different shades of colors for batik printing patterns with cracks that are not relevant to the batik patterns (BPPCNR). For the batik printing patterns with cracks that are relevant to the batik patterns (BPPCR), we proposed the method combining the distance transformation algorithm and EFEM, which can quickly define the initial cracks by the distance transformation image of the input printing patterns, and simulated the process of the extension of the crack by the EFEM. Moreover, we proposed the definition of a local axis parallel extension area that can greatly reduce the computation overhead. Experiments show that our method is effective in that it can realize various satisfying vivid batik printing patterns with cracks.

Constrained distance transforms for spatial atlas registration.

BackgroundSpatial frameworks are used to capture organ or whole organism image data in biomedical research. The registration of large biomedical volumetric images is a complex and challenging task, but one that is required for spatially mapped biomedical atlas systems. In most biomedical applications the transforms required are non-rigid and may involve significant deformation relating to variation in pose, natural variation and mutation. Here we develop a new technique to establish such transformations for mapping data that cannot be achieved by existing approaches and that can be used interactively for expert editorial review.ResultsThis paper presents the Constrained Distance Transform (CDT), a novel method for interactive image registration. The CDT uses radial basis function transforms with distances constrained to geodesics within the domains of the objects being registered. A geodesic distance algorithm is discussed and evaluated. Examples of registration using the CDT are presented.ConclusionThe CDT method is shown to be capable of simultaneous registration and foreground segmentation even when very large deformations are required.

Depth based Sign Language Recognition System Using SVM

In this paper, we propose a sign language recognition system using an SVM (Support Vector Machine) and a depth camera. In particular, we focus on the Korean sign language. For the sign language system, we suggest two methods, one for the hand feature extraction stage and the other for the recognition stage. Hand features consist of the number of fingers, finger length, palm radius, and hand direction. To extract hand features, we use Distance Transform and a hand skeleton. This method is more accurate and faster than a traditional method that uses contours. To recognize hand posture, we develop a decision tree with hand features. For more accuracy, we use SVM to determine the threshold value in the decision tree. In the experiment results, we show that the suggested method is more accurate and faster when extracting hand features and recognizing hand postures.

Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation.

BackgroundDNA-binding proteins play a pivotal role in various intra- and extra-cellular activities ranging from DNA replication to gene expression control. Identification of DNA-binding proteins is one of the major challenges in the field of genome annotation. There have been several computational methods proposed in the literature to deal with the DNA-binding protein identification. However, most of them can't provide an invaluable knowledge base for our understanding of DNA-protein interactions.ResultsWe firstly presented a new protein sequence encoding method called PSSM Distance Transformation, and then constructed a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM). First, the PSSM profiles are generated by using the PSI-BLAST program to search the non-redundant (NR) database. Next, the PSSM profiles are transformed into uniform numeric representations appropriately by distance transformation scheme. Lastly, the resulting uniform numeric representations are inputted into a SVM classifier for prediction. Thus whether a sequence can bind to DNA or not can be determined. In benchmark test on 525 DNA-binding and 550 non DNA-binding proteins using jackknife validation, the present model achieved an ACC of 79.96%, MCC of 0.622 and AUC of 86.50%. This performance is considerably better than most of the existing state-of-the-art predictive methods. When tested on a recently constructed independent dataset PDB186, SVM-PSSM-DT also achieved the best performance with ACC of 80.00%, MCC of 0.647 and AUC of 87.40%, and outperformed some existing state-of-the-art methods.ConclusionsThe experiment results demonstrate that PSSM Distance Transformation is an available protein sequence encoding method and SVM-PSSM-DT is a useful tool for identifying the DNA-binding proteins. A user-friendly web-server of SVM-PSSM-DT was constructed, which is freely accessible to the public at the web-site on http://bioinformatics.hitsz.edu.cn/PSSM-DT/.

Automatic Segmentation of the Right Ventricle by Active Shape Model and a Distance Transform

Automatic Segmentation of the Right Ventricle by Active Shape Model and a Distance Transform

Relationship between Right Ventricular Longitudinal Strain, Invasive Hemodynamics, and Functional Assessment in Pulmonary Arterial Hypertension.

Background and ObjectivesRight ventricular longitudinal strain (RVLS) is a new parameter of RV function. We evaluated the relationship of RVLS by speckle-tracking echocardiography with functional and invasive parameters in pulmonary arterial hypertension (PAH) patients.Subjects and MethodsThirty four patients with World Health Organization group 1 PAH (29 females, mean age 45±13 years old). RVLS were analyzed with velocity vector imaging.ResultsPatients with advanced symptoms {New York Heart Association (NYHA) functional class III/IV} had impaired RVLS in global RV (RVLSglobal, -17±5 vs. -12±3%, p<0.01) and RV free wall (RVLSFW, -19±5 vs. -14±4%, p<0.01 to NYHA class I/II). Baseline RVLSglobal and RVLSFW showed significant correlation with 6-minute walking distance (r=-0.54 and r=-0.57, p<0.01 respectively) and logarithmic transformation of brain natriuretic peptide concentration (r=0.65 and r=0.65, p<0.01, respectively). These revealed significant correlations with cardiac index (r=-0.50 and r=-0.47, p<0.01, respectively) and pulmonary vascular resistance (PVR, r=0.45 and r=0.45, p=0.01, respectively). During a median follow-up of 33 months, 25 patients (74%) had follow-up examinations. Mean pulmonary arterial pressure (mPAP, 54±13 to 46±16 mmHg, p=0.03) and PVR (11±5 to 6±2 wood units, p<0.01) were significantly decreased with pulmonary vasodilator treatment. RVLSglobal (-12±5 to -16±5%, p<0.01) and RVLSFW (-14±5 to -18±5%, p<0.01) were significantly improved. The decrease of mPAP was significantly correlated with improvement of RVLSglobal (r=0.45, p<0.01) and RVLSFW (r=0.43, p<0.01). The PVR change demonstrated significant correlation with improvement of RVLSglobal (r=0.40, p<0.01).ConclusionRVLS correlates with functional and invasive hemodynamic parameters in PAH patients. Decrease of mPAP and PVR as a result of treatment was associated with improvement of RVLS.

An Approach to Generate Spatial Voronoi Treemaps for Points, Lines, and Polygons

As a space-filling method, Voronoi Treemaps are used for showcasing hierarchies. Previously presented algorithms are limited to visualize nonspatial data. The approach of spatial Voronoi Treemaps is proposed in this paper to eliminate these problems by enabling the subdivisions for points, lines, and polygons with spatial coordinates and references. The digital distance transformation is recursively used to generate nested raster Voronoi polygons while the raster to vector conversion is used to create a vector-based Treemap visualization in a GIS (geographic information system) environment. The objective is to establish a spatial data model to better visualize and understand the hierarchies in the geographic field.

Global transcriptome analysis of alveolar macrophage from tuberculosis patients and non-infected controls

Global transcriptome analysis of alveolar macrophage from tuberculosis patients and non-infected controls

깊이 카메라와 SVM을 이용한 수화 인식 시스템

In this paper, we propose a sign language recognition system using SVM and depth camera. Especially, we focus on the Korean sign language. For the sign language system, we suggest two methods, one in hand feature extraction stage and the other in recognition stage. Hand features are consisted of the number of fingers, finger length, radius of palm, and direction of the hand. To extract hand features, we use Distance Transform and make hand skeleton. This method is more accurate than a traditional method which uses contours. To recognize hand posture, we develop the decision tree with the hand features. For ∙제1저자 : 김기상 ∙교신저자 : 최형일 ∙투고일 : 2014. 9. 19, 심사일 : 2014. 9. 29, 게재확정일 : 2014. 10 28 * 숭실대학교 미디어학과(School of Media, Soongsil University) ※ 본 연구는 서울시 산학연 협력사업(SS110013)과 2013년도 정부(미래창조과학부)의 재원으로 한국연구재단의 지원을 받아 수행된 기초연구사업임 (No.2013R1A1A2012012) 64 Journal of The Korea Society of Computer and Information November 2014 more accuracy, we use SVM to determine the threshold value in the decision tree. In the experimental results, we show that the suggested method is more accurate and faster when extracting hand features a recognizing hand postures. ▸