The main goal of this paper was to determine the biomechanical parameters of abdominal aortic aneurysm using the Bubble Inflated method. In other words, the main task of this paper was to develop a system which would be able to increase the pressure of physiological saline which affects blood vessel tissue and causes its deformation. This deformation is recorded using a camera and providing data at each moment about pressure values which affect tissue and were detected by using a pressure sensor. [Projekat Ministarstva nauke Republike Srbije, br. III41007 i br. OI-175008]

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Image segmentation is one of the most common procedures in medical imaging applications. It is also a very important task in breast cancer detection. Breast cancer detection procedure based on mammography can be divided into several stages. The first stage is the extraction of the region of interest from a breast image, followed by the identification of suspicious mass regions, their classification, and comparison with the existing image database. It is often the case that already existing image databases have large sets of data whose processing requires a lot of time, and thus the acceleration of each of the processing stages in breast cancer detection is a very important issue. In this paper, the implementation of the already existing algorithm for region-of-interest based image segmentation for mammogram images on High-Performance Reconfigurable Dataflow Computers (HPRDCs) is proposed. As a dataflow engine (DFE) of such HPRDC, Maxeler's acceleration card is used. The experiments for examining the acceleration of that algorithm on the Reconfigurable Dataflow Computers (RDCs) are performed with two types of mammogram images with different resolutions. There were, also, several DFE configurations and each of them gave a different acceleration value of algorithm execution. Those acceleration values are presented and experimental results showed good acceleration.

In this paper, we present a method for determining the mobility of the spinal column using a network of sensors. The sensors consist of accelerometers and gyroscopes, and mutual communication is accomplished using a I2C bus. The main sensor node collects data from all the sensors and sends them to a computer using Bluetooth communication. The collected data is then filtered and converted to the values of the angles that are of interest to quantify the movement. The experimental part of this work method is applied to determine the range of motion of patients in the Clinical Center in Kragujevac

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