The brushstroke and materials of Amadeo de Souza-Cardoso combined in an authentication tool

Nowadays, authentication studies for paintings require a multidisciplinary approach, based on the contribution of visual features analysis but also on characterizations of materials and techniques. Moreover, it is important that the assessment of the authorship of a painting is supported by technical studies of a selected number of original artworks that cover the entire career of an artist. This dissertation is concerned about the work of modernist painter Amadeo de Souza-Cardoso. It is divided in three parts. In the first part, we propose a tool based on image processing that combines information obtained by brushstroke and materials analysis. The resulting tool provides qualitative and quantitative evaluation of the authorship of the paintings; the quantitative element is particularly relevant, as it could be crucial in solving authorship controversies, such as judicial disputes. The brushstroke analysis was performed by combining two algorithms for feature detection, namely Gabor filter and Scale Invariant Feature Transform. Thanks to this combination (and to the use of the Bag-of-Features model), the proposed method shows an accuracy higher than 90% in distinguishing between images of Amadeo’s paintings and images of artworks by other contemporary artists. For the molecular analysis, we implemented a semi-automatic system that uses hyperspectral imaging and elemental analysis. The system provides as output an image that depicts the mapping of the pigments present, together with the areas made using materials not coherent with Amadeo’s palette, if any. This visual output is a simple and effective way of assessing the results of the system. The tool proposed based on the combination of brushstroke and molecular information was tested in twelve paintings obtaining promising results. The second part of the thesis presents a systematic study of four selected paintings made by Amadeo in 1917. Although untitled, three of these paintings are commonly known as BRUT, Entrada and Coty; they are considered as his most successful and genuine works. The materials and techniques of these artworks have never been studied before. The paintings were studied with a multi-analytical approach using micro-Energy Dispersive X-ray Fluorescence spectroscopy, micro-Infrared and Raman Spectroscopy, micro-Spectrofluorimetry and Scanning Electron Microscopy. The characterization of Amadeo’s materials and techniques used on his last paintings, as well as the investigation of some of the conservation problems that affect these paintings, is essential to enrich the knowledge on this artist. Moreover, the study of the materials in the four paintings reveals commonalities between the paintings BRUT and Entrada. This observation is supported also by the analysis of the elements present in a photograph of a collage (conserved at the Art Library of the Calouste Gulbenkian Foundation), the only remaining evidence of a supposed maquete of these paintings. The final part of the thesis describes the application of the image processing tools developed in the first part of the thesis on a set of case studies; this experience demonstrates the potential of the tool to support painting analysis and authentication studies. The brushstroke analysis was used as additional analysis on the evaluation process of four paintings attributed to Amadeo, and the system based on hyperspectral analysis was applied on the painting dated 1917. The case studies therefore serve as a bridge between the first two parts of the dissertation.

Tomographic imaging with a scanning laser ophtalmoscop

Retinal imaging with a confocal scaning laser Ophthalmoscope (cSLO) involves scanning a small laser beam over the retina and constructing an image from the reflected light. By applying the confocal principle, tomographic images can be produced by measuring a sequence of slices at different depths. However, the thickness of such slices, when compared with the retinal thickness, is too large to give useful 3D retinal images, if no processing is done. In this work, a prototype cSLO was modified in terms hardware and software to give the ability of doing the tomographic measurements with the maximum theoretical axial resolution possible. A model eye was built to test the performance of the system. A novel algorithm has been developed which fits a double Gaussian curve to the axial intensity profiles generated from a stack of images slices. The underlying assumption is that the laser light has mainly been reflected by two structures in the retina, the internal limiting membrane and the retinal pigment epithelium. From the fitted curve topographic images and novel thickness images of the retina can be generated. Deconvolution algorithms have also been developed to improve the axial resolution of the system, using a theoretically predicted cSLO point spread function. The technique was evaluated using measurements made on a model eye, four normal eyes and seven eyes containing retinal pathology. The reproducibility, accuracy and physiological measurements obtained, were compared with available published data, and showed good agreement. The difference in the measurements when using a double rather than a single Gaussian model was also analysed.

Correction of spatial distortion in magnetic resonance imaging

Dissertation to Obtain the Degree of Master in Biomedical Engineering

Chromatin dynamics in Arbidopsis development: a live cell imaging approach

Dissertation presented to obtain the Ph.D degree in Biology

Technology governance in radiology: the example of magnetic resonance imaging

Based on the report for the unit “Project III” of the PhD programme on Technology Assessment in 2011. The unit was supervised by Prof. António B. Moniz (FCT-UNL).

Geospatial assessment of pest-induced forest damage through the use of UAV-bases NIR imaging and Gi-technology

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Microwave imaging of the axilla to aid breast cancer diagnosis

Breast cancer is the most common type of cancer among women all over the world. An important issue that is not commonly addressed in breast cancer imaging literature is the importance of imaging the underarm region—where up to 80% of breast cancer cells can metastasise to. The first axillary lymph nodes to receive drainage from the primary tumour in the breast are called Sentinel Node. If cancer cells are found in the Sentinel Node, there is an increased risk of metastatic breast cancer which makes this evaluation crucial to decide what follow-up exams and therapy to follow. However, non-invasive detection of cancer cells in the lymph nodes is often inconclusive, leading to the surgical removal of too many nodes which causes adverse side-effects for patients. Microwave Imaging is one of the most promising non-invasive imaging modalities for breast cancer early screening and monitoring. This novel study tests the feasibility of imaging the axilla region by means of the simulation of an Ultra-Wideband Microwave Imaging system. Simulations of such system are completed in several 2D underarm models that mimic the axilla. Initial imaging results are obtained by means of processing the simulated backscattered signals by eliminating artefacts caused by the skin and beamforming the processed signals in order to time-align all the signals recorded at each antenna. In this dissertation several image formation algorithms are implemented and compared by visual inspection of the resulting images and through a range of performance metrics, such as Signal-to-Clutter Ratio and FullWidth Half Maximum calculations. The results in this study showed that Microwave Imaging is a promising technique that might allow to identify the presence and location of metastasised cancer cells in axillary lymph nodes, enabling the non-invasive evaluation of breast cancer staging.

Diffusional kurtosis imaging using a fast heuristic constrained linear least squares algorithm: a plugin for OsiriX

Diffusion Kurtosis Imaging (DKI) is a fairly new magnetic resonance imag-ing (MRI) technique that tackles the non-gaussian motion of water in biological tissues by taking into account the restrictions imposed by tissue microstructure, which are not considered in Diffusion Tensor Imaging (DTI), where the water diffusion is considered purely gaussian. As a result DKI provides more accurate information on biological structures and is able to detect important abnormalities which are not visible in standard DTI analysis. This work regards the development of a tool for DKI computation to be implemented as an OsiriX plugin. Thus, as OsiriX runs under Mac OS X, the pro-gram is written in Objective-C and also makes use of Apple’s Cocoa framework. The whole program is developed in the Xcode integrated development environ-ment (IDE). The plugin implements a fast heuristic constrained linear least squares al-gorithm (CLLS-H) for estimating the diffusion and kurtosis tensors, and offers the user the possibility to choose which maps are to be generated for not only standard DTI quantities such as Mean Diffusion (MD), Radial Diffusion (RD), Axial Diffusion (AD) and Fractional Anisotropy (FA), but also DKI metrics, Mean Kurtosis (MK), Radial Kurtosis (RK) and Axial Kurtosis (AK).The plugin was subjected to both a qualitative and a semi-quantitative analysis which yielded convincing results. A more accurate validation pro-cess is still being developed, after which, and with some few minor adjust-ments the plugin shall become a valid option for DKI computation

Structural connectivity based on diffusion Kurtosis imaging

Structural connectivity models based on Diffusion Tensor Imaging (DTI) are strongly affected by the technique’s inability to resolve crossing fibres, either intra- or inter-hemispherical connections. Several models have been proposed to address this issue, including an algorithm aiming to resolve crossing fibres which is based on Diffusion Kurtosis Imaging (DKI). This technique is clinically feasible, even when multi-band acquisitions are not available, and compatible with multi-shell acquisition schemes. DKI is an extension of DTI enabling the estimation of diffusion tensor and diffusion kurtosis metrics. In this study we compare the performance of DKI and DTI in performing structural brain connectivity. Six healthy subjects were recruited, aged between 25 and 35 (three females). The MRI experiments were performed using a 3T Siemens Trio with a 32-channel head coil. The scans included a T1-weighted sequence (1mm3), and a DWI with b-values 0, 1000 and 2000 s:mm