Hardware-Accelerated Volume Rendering for Real-Time Medical Data Visualization

To provide on-patient medical data visualization, a medical augmented reality environment must support volume rendering, accurate tracking, real-time performance and high visual quality in the final rendering. Another interesting feature is markerless registration, to solve the intrusiveness introduced by the use of fiducial markers for tracking. In this paper we address the problem of on-patient medical data visualization in a real-time high-quality markerless augmented reality environment. The medical data consists of a volume reconstructed from 3D computed tomography image data. Markerless registration is done by generating a 3D reference model of the region of interest in the patient and tracking it from the depth stream of an RGB-D sensor. From the estimated camera pose, the volumetric medical data and the reference model are combined allowing a visualization of the patient as well as part of his anatomy. To improve the visual perception of the scene, focus+context visualization is used in the augmented reality scene to dynamically define which parts of the medical volume will be visualized in the context of the patient’s image. Moreover, context-preserving volume rendering is employed to dynamically control which parts of the volume will be rendered. The results obtained show that the markerless environment runs in real-time and the techniques applied greatly improve the visual quality of the final rendering.

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OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽

10.3390/data6010001 ◽

2020 ◽

Vol 6 (1) ◽

pp. 1

Author(s):

Ahmed Elmogy ◽

Hamada Rizk ◽

Amany M. Sarhan

Keyword(s):

Data Mining ◽

Image Processing ◽

Intrusion Detection ◽

Real Time ◽

Outlier Detection ◽

Real World ◽

Medical Data ◽

Experimental Results ◽

Real Time Applications ◽

Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

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