A cost-efficient plastic optical fiber (POF) system for unobtrusive monitoring of human vital signs is presented. The system is based on speckle interferometry. A laser diode is butt-coupled to the POF whose exit face projects speckle patterns onto a linear optical sensor array. Sequences of acquired speckle images are transformed into one-dimensional signals by using the phase-shifting method. The signals are analyzed by band-pass filtering and a Morlet-wavelet-based multiresolutional approach for the detection of cardiac and respiratory activities, respectively. The system is tested with 10 healthy nonhospitalized persons, lying supine on a mattress with the embedded POF. Experimental results are assessed statistically: precisions of 98.8% ± 1.5% and 97.9% ± 2.3%, sensitivities of 99.4% ± 0.6% and 95.3% ± 3%, and mean delays between interferometric detections and corresponding referential signals of 116.6 ± 55.5 and 1299.2 ± 437.3 ms for the heartbeat and respiration are obtained, respectively.

No abstract

Segmentation of ovarian ultrasound images using cellular neural networks (CNNs) is studied in this paper. The segmentation method consists of five successive steps where the first four uses CNNs. In the first step, only rough position of follicles is determined. In the second step, the results are improved by expansion of detected follicles. In the third step, previously undetected inexpressive follicles are determined, while the fourth step detects the position of ovary. All results are joined in the fifth step. The templates for CNNs were obtained by applying genetic algorithm. The segmentation method has been tested on 50 ovarian ultrasound images. The recognition rate of follicles was around 60% and misidentification rate was around 30%.

Observing changes in females' ovaries is essential in obstetrics and gynaecological imaging, e.g., genetic engineering and human reproduction. It is particularly important to monitor the dynamics of ovarian follicles' growth, as only fully mature and grown follicles, i.e., the dominant follicles have a potential to ovulate at the end of a follicular phase. Gynaecologists follow this process in two dimensions, but recently three-dimensional (3-D) ultrasound examinations are coming to the fore. This paper surveys the existing computer methods for detection, recognition, and analyses of follicles in two-dimensional (2-D) and 3-D ovarian ultrasound recordings. Our study focuses on the efficiency, validation, and assessment of proposed follicle processing algorithms. The most important processing steps were identified in order to compare their performances. Higher ranking solutions are suggested for the so-called best algorithm for 2-D and 3-D ultrasound recordings of ovarian follicles. Finally, some guidelines for future research in this field are discussed, in particular for 3-D ultrasound volumes.

Modelling of patient knee joint from the MRI data and simulating its kinematics is presented. A flexion of the femur with respect to the tibia from 0 • to around 40 • is simulated. The finite element knee model is driven by compressive load measured 'in vivo' during MRI process by using specially developed optical force measurement system. Predicted kinematics is evaluated against the high-quality model obtained by registration from experimentally gathered low-quality MRI at fixed flexions. Validation pointed out that the mean square error (MSE) for the Euler rotation angles are bellow 1.73 • , while the MSE for Euler translation is smaller than 5.93 mm.