The success of in-vitro fertilization can be predicted by a correct quantitative and qualitative assessment of ovarian follicles. Several ovarian follicle detection and recognition algorithms have been published. Their effectiveness is inferior to human follicle annotations due to various kinds of noise, degradations, and artefacts in ultrasonic images. This paper deals with an approach to recognize antral follicles from 2 mm in diameter in 3D ultrasound data. Its detection phase looks for candidate follicular regions, while the recognition phase assesses the likelihood of a region to correspond to a follicle. Three innovative definitions underpin the detection: Laplacian-of-Gaussian-based directional 3D wavelet transform, adaptive multiscale search based on Gaussian mixtures, and recursive convexity-based region splitting. A likelihood index is also introduced to support follicle recognition. The proposed approach was tested on 30 ultrasound ovarian volumes generated by different sonographic machines in stimulated and non-stimulated examination cycles. The obtained follicle recognition rates exceed those of the best 3D approaches known by about 10 percent, while qualitative assessments yield comparable values.

Various applications of cellular neural networks (CNNs) on complex image processing tasks raise questions about an appropriate selection of template elements that determine the CNN's behavior. In previous research we utilized multiple time variant template elements cellular neural networks for segmentation, which has many advantages compared to conventional search space approaches. In this paper a novel approach which utilizes the formalism of support vector machines (SVMs) that utilizes only single time invariant set of CNN template elements, is introduced. The main advantage of this approach is reduction of the number of CNNs templates and confirms to the conventional applications of CNN.

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%.

Simulated annealing is no doubt one of the most popular optimisation methods. Like similar methods it is based on natural phenomena. In the paper two examples of how to use this method in medical applications are presented. In the first example the successfulness of simulated annealing is compared to a genetic algorithm on the problem of the optimal coefficients determination for cellular neural networks, when used for segmentation of ovarian ultrasound images. In this field the latter method produced better results. The second example discusses the usage of simulated annealing for optimisation of signal classification.

The paper deals with the problem of processing and interpretation of clinically recorded ultrasound images for the reason of following the growth of dominant ovarian follicles in a day-to-day manner. A part of the XUltra project achievements is presented. We propose three different automatic computer-based follicle identification algorithms. The first one is based on cellular neural networks. The second one is based on region growing segmentation method, while the third one processes entire image sequence with a predictor-corrector recognition scheme. The recognition rate of follicles with these algorithms goes up to 78%, while the misidentification rate is around 15%.