Nikolaos A. Mouravliansky

Multimodal registration of retinal images 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 organizing maps 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.

In silico radiation oncology: combining novel simulation algorithms with current visualization techniques

The concept of in silica radiation oncology is clarified in this paper. A brief literature review points out the principal domains in which experimental, mathematical, and three-dimensional (3-D) computer simulation models of tumor growth and response to radiation therapy have been developed. Two paradigms of 3-D simulation models developed by our research group are concisely presented. The first one refers to the in vitro development and radiation response of a tumor spheroid whereas the second one refers to the fractionated radiation response of a clinical tumor in vivo based on the patients imaging data. In each case, a description of the salient points of the corresponding algorithms and the visualization techniques used takes place. Specific applications of the models to experimental and clinical cases are described and the behavior of the models is two- and three-dimensionally visualized by using virtual reality techniques. Good qualitative agreement with experimental and clinical observations strengthens the applicability of the models to real situations. A protocol for further testing and adaptation is outlined. Therefore, an advanced integrated patient specific decision support and spatio-temporal treatment planning system is expected to emerge after the completion of the necessary experimental tests and clinical evaluation.

A comparative study of surface‐ and volume‐based techniques for the automatic registration between CT and SPECT brain images

Image registration of multimodality images is an essential task in numerous applications in three-dimensional medical image processing. Medical diagnosis can benefit from the complementary information in different modality images. Surface-based registration techniques, while still widely used, were succeeded by volume-based registration algorithms that appear to be theoretically advantageous in terms of reliability and accuracy. Several applications of such algorithms for the registration of CT-MRI, CT-PET, MRI-PET, and SPECT-MRI images have emerged in the literature, using local optimization techniques for the matching of images. Our purpose in this work is the development of automatic techniques for the registration of real CT and SPECT images, based on either surface- or volume-based algorithms. Optimization is achieved using genetic algorithms that are known for their robustness. The two techniques are compared against a well-established method, the Iterative Closest Point-ICP. The correlation coefficient was employed as an independent measure of spatial match, to produce unbiased results. The repeated measures ANOVA indicates the significant impact of the choice of registration method on the magnitude of the correlation (F = 4.968, p = 0.0396). The volume-based method achieves an average correlation coefficient value of 0.454 with a standard deviation of 0.0395, as opposed to an average of 0.380 with a standard deviation of 0.0603 achieved by the surface-based method and an average of 0.396 with a standard deviation equal to 0.0353 achieved by ICP. The volume-based technique performs significantly better compared to both ICP (p<0.05, Neuman Keuls test) and the surface-based technique (p<0.05, Neuman-Keuls test). Surface-based registration and ICP do not differ significantly in performance.

Thoracic non-rigid registration combining self-organizing maps and radial basis functions

An automatic three-dimensional non-rigid registration scheme is proposed in this paper and applied to thoracic computed tomography (CT) data of patients with stage III non-small cell lung cancer (NSCLC). According to the registration scheme, initially anatomical set of points such as the vertebral spine, the ribs, and shoulder blades are automatically segmented slice by slice from the two CT scans of the same patient in order to serve as interpolant points. Based on these extracted features, a rigid-body transformation is then applied to provide a pre-registration of the data. To establish correspondence between the feature points, the novel application of the self-organizing maps (SOMs) is adopted. In particular, the automatic correspondence of the interpolant points is based on the initialization of the Kohonen neural network model capable to identify 500 corresponding pairs of points approximately in the two CT sets. Then, radial basis functions (RBFs) using the shifted log function is subsequently employed for elastic warping of the image volume, using the correspondence between the interpolant points, as obtained in the previous phase. Quantitative and qualitative results are also presented to validate the performance of the proposed elastic registration scheme resulting in an alignment error of 6 mm, on average, over 15 CT paired datasets. Finally, changes of the tumor volume in respect to each reference dataset are estimated for all patients, which indicate inspiration and/or movement of the patient during acquisition of the data. Thus, the practical implementation of this scheme could provide estimations of lung tumor volumes during radiotherapy treatment planning.

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 (1968, 1981, 1992, 1995) 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.

Detection of glaucomatous change based on vessel shape analysis

Glaucoma, a leading cause of blindness worldwide, is a progressive optic neuropathy with characteristic structural changes in the optic nerve head and concomitant visual field defects. Ocular hypertension (i.e. elevated intraocular pressure without glaucoma) is the most important risk factor to develop glaucoma. Even though a number of variables, including various optic disc and visual field parameters, have been used in order to identify early glaucomatous damage, there is a need for computer-based methods that can detect early glaucomatous progression so that treatment to prevent further progression can be initiated. This paper is focused on the description of a system based on image processing and classification techniques for the estimation of quantitative parameters to define vessel deformation and the classification of image data into two classes: patients with ocular hypertension who develop glaucomatous damage and patients with ocular hypertension who remain stable. The proposed system consists of the retinal image preprocessing module for vessel central axis segmentation, the automatic retinal image registration module based on a novel application of self organizing maps (SOMs) to define automatic point correspondence, the retinal vessel attributes calculation module to select the vessel shape attributes and the data classification module, using an artificial neural network classifier, to perform the necessary subject classification. Implementation of the system to optic disc data from 127 subjects obtained by a fundus camera at regular intervals provided a classification rate of 87.5%, underscoring the value of the proposed system to assist in the detection of early glaucomatous change.

Automatic retinal registration using global optimization techniques

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 two global optimization techniques, Simulated Annealing and Genetic Algorithms-are investigated and compared in terms of accuracy and efficiency. The registration of 26 pairs of Fluoroscein Angiography (FA) and Indocyanine Green Chorioangiography (ICG) images with the corresponding Red Free (RF) retinal images, showed the superiority of combining Genetic Algorithms with the Affine and Bilinear transformation models.

ESTERR-PRO: a setup verification software system using electronic portal imaging.

The purpose of the paper is to present and evaluate the performance of a new software-based registration system for patient setup verification, during radiotherapy, using electronic portal images. The estimation of setup errors, using the proposed system, can be accomplished by means of two alternate registration methods. (a) The portal image of the current fraction of the treatment is registered directly with the reference image (digitally reconstructed radiograph (DRR) or simulator image) using a modified manual technique. (b) The portal image of the current fraction of the treatment is registered with the portal image of the first fraction of the treatment (reference portal image) by applying a nearly automated technique based on self-organizing maps, whereas the reference portal has already been registered with a DRR or a simulator image. The proposed system was tested on phantom data and on data from six patients. The root mean square error (RMSE) of the setup estimates was 0.8 ± 0.3 (mean value ± standard deviation) for the phantom data and 0.3 ± 0.3 for the patient data, respectively, by applying the two methodologies. Furthermore, statistical analysis by means of the Wilcoxon nonparametric signed test showed that the results that were obtained by the two methods did not differ significantly (P value > 0.05).

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 new method for the elastic registration of CT and MRI head images

A new method for optimizing the performance of CT and MRI head registration techniques introducing elastic deformation is presented. The method uses a Self Organizing Map to define the node correspondence of the triangulated external surfaces of both modalities. The two sets of equivalent nodes are subsequently registered using Thin-Plate Splines deformation.