Adam Brzeski

Depth images filtering in distributed streaming

Abstract In this paper, we propose a distributed system for point cloud processing and transferring them via computer network regarding to effectiveness-related requirements. We discuss the comparison of point cloud filters focusing on their usage for streaming optimization. For the filtering step of the stream pipeline processing we evaluate four filters: Voxel Grid, Radial Outliner Remover, Statistical Outlier Removal and Pass Through. For each of the filters we perform a series of tests for evaluating the impact on the point cloud size and transmitting frequency (analysed for various fps ratio). We present results of the optimization process used for point cloud consolidation in a distributed environment. We describe the processing of the point clouds before and after the transmission. Pre- and post-processing allow the user to send the cloud via network without any delays. The proposed pre-processing compression of the cloud and the post-processing reconstruction of it are focused on assuring that the end-user application obtains the cloud with a given precision.

Real-Time Gastrointestinal Tract Video Analysis on a Cluster Supercomputer

The article presents a novel approach to medical video data analysis and recognition. Emphasis has been put on adapting existing algorithms detecting lesions and bleedings for real time usage in a medical doctor’s office during an endoscopic examination. A system for diagnosis recommendation and disease detection has been designed taking into account the limited mobility of the endoscope and the doctor’s requirements. The main goal of the performed research was to establish the possibility of assuring the necessary performance to introduce the solution into real-life diagnostics.

Automatic Detection of Nerves in Confocal Corneal Images with Orientation-Based Edge Merging

The paper presents an algorithm for improving results of automatic nerve detections in confocal microscopy images of human corneal. The method is designed as a postprocessing step of regular detection. After the nerves are initially detected, the algorithms attempts to improve the results by filling undesired gaps between single nerves detections in order to correctly mark the entire nerve instead of only parts of it. This approach enables for reliable detection of long nerves, which can be used for more accurate elimination of short detections and therefore eliminating noise. The method evaluates candidate gaps by analysing the orientation of segments to be merged in the area near the gap. Segments with sufficiently high orientation compliance are merged to form a single nerve detection. Despite using only a simple technique for initial detection of nerves, the method enabled achieving a fairly low ratio of wrong nerve detections with a balanced level of overall accuracy.

Towards Healthcare Cloud Computing

In this paper we present construction of a software platform for supporting medical research teams, in the area of impedance cardiography, called IPMed. Using the platform, research tasks will be performed by the teams through computer-supported cooperative work. The platform enables secure medical data storing, access to the data for research group members, cooperative analysis of medical data and provide analysis supporting tools basing on intelligent information retrieval techniques (semantic text comparison, charts, images, video matching). On top of the platform, a dedicated web application is built for medical teams focused on the area of hemodynamics. The acquired data enables the researchers to develop new classification schemes for hemodynamic profiles, which will be used for construction of prototype application for supporting stroke treatment centers. In the paper we compare IPMed to other existing platforms which offer similar functionality and describe a concept of its migration to a cloud based system.