Registration of retinal angiograms using self organizing maps.

In this paper, an automatic method for registering multimodal retinal images is presented. The method consists of three steps: the vessel centerline detection and extraction of bifurcation points only in the reference image, the automatic correspondence of bifurcation points in the two images using a novel implementation of the Self Organized Maps (SOMs) and the extraction of the parameters of the affine transform using the previously obtained correspondences. The proposed registration algorithm was tested on 24 multimodal retinal pairs and the obtained results show an advantageous performance in terms of accuracy with respect to the manual registration.

A novel and efficient implementation of the marching cubes algorithm.

In this paper, a novel and efficient implementation of the marching cubes (MC) algorithm is presented for the reconstruction of anatomical structures from real three-dimensional medical data. The proposed approach is based on a generic rule, able to triangulate all 15 standard cube configurations used in the classical MC algorithm as well as additional cases presented in the literature. The proposed implementation of the MC algorithm can handle the Type A 'hole problem' which occurs when at least one cube face has an intersection point in each of its four edges. Theoretical and experimental results demonstrate the ability of the new implementation to reproduce standard MC results, resolving Type A 'hole problem'. Finally, the proposed implementation was applied to real medical date to reconstruct anatomical structures. The output of the proposed technique is in WWW compliant format.

Modeling tumor growth and irradiation response in vitro--a combination of high-performance computing and web-based technologies including VRML visualization.

A simplified three-dimensional Monte Carlo simulation model of in vitro tumor growth and response to fractionated radiotherapeutic schemes is presented in this paper. The paper aims at both the optimization of radiotherapy and the provision of insight into the biological mechanisms involved in tumor development. The basics of the modeling philosophy of Duechting have been adopted and substantially extended. The main processes taken into account by the model are the transitions between the cell cycle phases, the diffusion of oxygen and glucose, and the cell survival probabilities following irradiation. Specific algorithms satisfactorily describing tumor expansion and shrinkage have been applied, whereas a novel approach to the modeling of the tumor response to irradiation has been proposed and implemented. High-performance computing systems in conjunction with Web technologies have coped with the particularly high computer memory and processing demands. A visualization system based on the MATLAB software package and the virtual-reality modeling language has been employed. Its utilization has led to a spectacular representation of both the external surface and the internal structure of the developing tumor. The simulation model has been applied to the special case of small cell lung carcinoma in vitro irradiated according to both the standard and accelerated fractionation schemes. A good qualitative agreement with laboratory experience has been observed in all cases. Accordingly, the hypothesis that advanced simulation models for the in silico testing of tumor irradiation schemes could substantially enhance the radiotherapy optimization process is further strengthened. Currently, our group is investigating extensions of the presented algorithms so that efficient descriptions of the corresponding clinical (in vivo) cases are achieved.

Automatic retinal image registration scheme using global optimization techniques.

Retinal image registration is commonly required in order to combine the complementary information in different retinal modalities. In this paper, a new automatic scheme to register retinal images is presented and is currently tested in a clinical environment. The scheme considers the suitability and efficiency of different image transformation models and function optimization techniques, following an initial preprocessing stage. Three different transformation models--affine, bilinear and projective--as well as three optimization techniques--downhill simplex method, simulated annealing and genetic algorithms--are investigated and compared in terms of accuracy and efficiency. The registration of 26 pairs of Fluoroscein Angiography and Indocyanine Green Chorioangiography images with the corresponding Red-Free retinal images, showed the superiority of combining genetic algorithms with the affine and bilinear transformation models. A comparative study of the proposed automatic registration scheme against the manual method, commonly used in the clinical practice, is finally presented showing the advantage of the proposed automatic scheme in terms of accuracy and consistency.