scholarly journals Region Space Guided Transfer Function Design for Nonlinear Neural Network Augmented Image Visualization

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Fei Yang ◽  
Xiangxu Meng ◽  
JiYing Lang ◽  
Weigang Lu ◽  
Lei Liu
Keyword(s):  
Neural Network ◽  
Transfer Function ◽  
Ray Casting ◽  
Volume Data ◽  
Field Quality ◽  
Transfer Function Design ◽  
3D Volume ◽  
Rendering Pipeline ◽  
2D Histogram

Visualization provides an interactive investigation of details of interest and improves understanding the implicit information. There is a strong need today for the acquisition of high quality visualization result for various fields, such as biomedical or other scientific field. Quality of biomedical volume data is often impacted by partial effect, noisy, and bias seriously due to the CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) devices, which may give rise to an extremely difficult task of specifying transfer function and thus generate poor visualized image. In this paper, firstly a nonlinear neural network based denoising in the preprocessing stage is provided to improve the quality of 3D volume data. Based on the improved data, a novel region space with depth based 2D histogram construction method is then proposed to identify boundaries between materials, which is helpful for designing the proper semiautomated transfer function. Finally, the volume rendering pipeline with ray-casting algorithm is implemented to visualize several biomedical datasets. The noise in the volume data is suppressed effectively and the boundary between materials can be differentiated clearly by the transfer function designed via the modified 2D histogram.

CONTROL OF OBJECT VISIBILITY IN VOLUME RENDERING — A DISTANCE-BASED APPROACH

2005 ◽  
Vol 05 (04) ◽  
pp. 699-714 ◽  
Author(s):  
JIANLONG ZHOU ◽  
ANDREAS DÖRING ◽  
KLAUS D. TÖNNIES
Keyword(s):  
Transfer Function ◽  
Volume Rendering ◽  
Transfer Functions ◽  
Redundant Information ◽  
Volume Data ◽  
Data Value ◽  
Rendering Pipeline

Volume data often have redundant information for clinical uses. The essence of volume rendering can be regarded as a mechanism to determine visibility of redundant information and structures of interest using different approaches. Controlling the visibility of these structures in volume rendering depends on the following factors in existing rendering algorithms: The data value of current voxel and its derivatives (used in transfer function based approaches), and the voxel position (used in volume clipping). This paper introduces the distance which is defined by the user into volume rendering pipeline to control the visibility of structures. The distance based approach, which is named as distance transfer function, has the flexibility of transfer functions for depicting data information and the advantages of volume clippings for visualizing inner structures. The results show that the distance based approach is a powerful tool for volume data information depiction.

scholarly journals 3Dive: An Immersive Environment for Interactive Volume Data Exploration

2001 ◽  
Vol 5 (1) ◽  
pp. 38-51 ◽  
Author(s):  
Michael Boyles ◽  
Shiaofen Fang
Keyword(s):  
Data Visualization ◽  
Data Exploration ◽  
Volume Data ◽  
View Sharing ◽  
Level Data ◽  
Immersive Environment ◽  
Transfer Function Design ◽  
Volumetric Objects ◽  
Public Domain Software ◽  
Rendering Pipeline

This paper describes an immersive system, called 3DIVE, for interactive volume data visualization and exploration inside the CAVE virtual environment. Combining interactive volume rendering and virtual reality provides a natural immersive environment for volumetric data visualization. More advanced data exploration operations, such as object level data manipulation, simulation and analysis, are supported in 3DIVE by several new techniques: volume primitives and texture regions are used for the rendering, manipulation, and collision detection of volumetric objects; the region based rendering pipeline is integrated with 3D image filters to provide an image-based mechanism for interactive transfer function design; a collaborative visualization module allows remote sites to collaborate over common datasets with passive or active view sharing. The system has been recently released as public domain software for CAVE/ImmersaDesk users, and is currently being actively used by a 3D microscopy visualization project.

Multi-Dimensional Transfer Functions Design

2011 ◽  
pp. 223-240
Author(s):  
Hai Lin
Keyword(s):  
Transfer Function ◽  
Volume Rendering ◽  
Large Scale ◽  
Spatial Information ◽  
Transfer Functions ◽  
Design Method ◽  
Data Layout ◽  
Volume Data ◽  
Function Design ◽  
Transfer Function Design

Transfer function design is one of the most important procedures in volume rendering. Transfer function maps, which is a function mapping relationship, data values to display attributes, such as color and opacity. This chapter introduces region growing- based multi-dimensional transfer function design method, which can improve the effect of the multi-dimensional transfer function design, and help the users save the time used in the interactive design and decrease the difficult. In order to use the spatial information as independent variable, we combine spatial information to generate multi-dimensional transfer function. This chapter discusses the GPU-based transfer function lookup method and illumination parameter setting problems. In the last part of this chapter, we discuss the data layout of large scale volume data set and its volume rendering methods.

Convolutional Neural Network for Customer’s Opinion on Amazon Products

2019 ◽  
Vol 8 (3) ◽  
pp. 6634-6643 ◽  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Sentiment Analysis ◽  
Latent Dirichlet Allocation ◽  
Opinion Mining ◽  
Text Documents ◽  
Customer Churn ◽  
Learning Classifier ◽  
Review Spam

Opinion mining and sentiment analysis are valuable to extract the useful subjective information out of text documents. Predicting the customer’s opinion on amazon products has several benefits like reducing customer churn, agent monitoring, handling multiple customers, tracking overall customer satisfaction, quick escalations, and upselling opportunities. However, performing sentiment analysis is a challenging task for the researchers in order to find the users sentiments from the large datasets, because of its unstructured nature, slangs, misspells and abbreviations. To address this problem, a new proposed system is developed in this research study. Here, the proposed system comprises of four major phases; data collection, pre-processing, key word extraction, and classification. Initially, the input data were collected from the dataset: amazon customer review. After collecting the data, preprocessing was carried-out for enhancing the quality of collected data. The pre-processing phase comprises of three systems; lemmatization, review spam detection, and removal of stop-words and URLs. Then, an effective topic modelling approach Latent Dirichlet Allocation (LDA) along with modified Possibilistic Fuzzy C-Means (PFCM) was applied to extract the keywords and also helps in identifying the concerned topics. The extracted keywords were classified into three forms (positive, negative and neutral) by applying an effective machine learning classifier: Convolutional Neural Network (CNN). The experimental outcome showed that the proposed system enhanced the accuracy in sentiment analysis up to 6-20% related to the existing systems.

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Hybrid Reference Current Generation Theory for Solar Fed UPFC System

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1527
Author(s):  
R. Senthil Kumar ◽  
K. Mohana Sundaram ◽  
K. S. Tamilselvan
Keyword(s):  
Neural Network ◽  
Power Factor ◽  
Reactive Power ◽  
Voltage Source ◽  
Unity Power Factor ◽  
Current Generation ◽  
Load Voltage ◽  
Reference Current Generation ◽  
Source Current

The extensive usage of power electronic components creates harmonics in the voltage and current, because of which, the quality of delivered power gets affected. Therefore, it is essential to improve the quality of power, as we reveal in this paper. The problems of load voltage, source current, and power factors are mitigated by utilizing the unified power flow controller (UPFC), in which a combination of series and shunt converters are combined through a DC-link capacitor. To retain the link voltage and to maximize the delivered power, a PV module is introduced with a high gain converter, named the switched clamped diode boost (SCDB) converter, in which the grey wolf optimization (GWO) algorithm is instigated for tracking the maximum power. To retain the link-voltage of the capacitor, the artificial neural network (ANN) is implemented. A proper control of UPFC is highly essential, which is achieved by the reference current generation with the aid of a hybrid algorithm. A genetic algorithm, hybridized with the radial basis function neural network (RBFNN), is utilized for the generation of a switching sequence, and the generated pulse has been given to both the series and shunt converters through the PWM generator. Thus, the source current and load voltage harmonics are mitigated with reactive power compensation, which results in attaining a unity power factor. The projected methodology is simulated by MATLAB and it is perceived that the total harmonic distortion (THD) of 0.84% is attained, with almost a unity power factor, and this is validated with FPGA Spartan 6E hardware.

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