Color constancy in dermatoscopy with smartphone

The recent spread of cheap dermatoscopes for smartphones can empower patients to acquire images of skin lesions on their own and send them to dermatologists. Since images are acquired by different smartphone cameras under unique illumination conditions, the variability in colors is expected. Therefore, the mobile dermatoscopic systems should be calibrated in order to ensure the color constancy in skin images. In this study, we have tested a dermatoscope DermLite DL1 basic, attached to Samsung Galaxy S4 smartphone. Under the controlled conditions, jpeg images of standard color patches were acquired and a model between an unknown device-dependent RGB and a device independent Lab color space has been built. Results showed that median and the best color error was 7.77 and 3.94, respectively. Results are in the range of a human eye detection capability (color error ≈ 4) and video and printing industry standards (color error is expected to be between 5 and 6). It can be concluded that a calibrated smartphone dermatoscope can provide sufficient color constancy and can serve as an interesting opportunity to bring dermatologists closer to the patients.EU_MSCA_IF_DogSpec_74539

Poor optical stability of molecular dyes when used as absorbers in water-based tissue-simulating phantoms

Biomedical optical systems and models can be easily validated by the use of tissue-simulating phantoms. They can consist of water-based turbid media which often include inks (India ink and molecular dyes) as absorbers. Optical stability of commonly exploited inks under the influence of light, pH changes and the addition of TiO2 and surfactant, was studied. We found that the exposure to ultraviolet and visible light can crucially affect the absorption properties of molecular dyes. On average, absorption peaks decreased by 47.3% in 150 exposure hours. Furthermore, dilution can affect ink’s pH and by that, its decay rate under light exposure. When TiO2 was added to the phantoms, all molecular dyes decayed rapidly. Photocatalytic nature of TiO2 can be partially avoided by selecting TiO2 with surface and crystal structure modification. Surfactant, normally present in the phantoms with polystyrene spheres, can cause absorption peak shifts up to 20 nm and amplitude changes of 29.6%. Therefore, it is crucial to test the optical stability of inks in the presented manner before their exploitation in water-based phantoms.EU_MSCA_IF_DogSpec_74539

MULTI-FEATURE MUTUAL INFORMATION IMAGE REGISTRATION

Nowadays, information-theoretic similarity measures, especially the mutual information and its derivatives, are one of the most frequently used measures of global intensity feature correspondence in image registration. Because the traditional mutual information similarity measure ignores the dependency of intensity values of neighboring image elements, registration based on mutual information is not robust in cases of low global intensity correspondence. Robustness can be improved by adding spatial information in the form of local intensity changes to the global intensity correspondence. This paper presents a novel method, by which intensities, together with spatial information, i.e., relations between neighboring image elements in the form of intensity gradients, are included in information-theoretic similarity measures. In contrast to a number of heuristic methods that include additional features into the generic mutual information measure, the proposed method strictly follows information theory under certain assumptions on feature probability distribution. The novel approach solves the problem of efficient estimation of multifeature mutual information from sparse high-dimensional feature space. The proposed measure was tested on magnetic resonance (MR) and computed tomography (CT) images. In addition, the measure was tested on positron emission tomography (PET) and MR images from the widely used Retrospective Image Registration Evaluation project image database. The results indicate that multi-feature mutual information, which combines image intensities and intensity gradients, is more robust than the standard single-feature intensity based mutual information, especially in cases of low global intensity correspondences, such as in PET/MR images or significant intensity inhomogeneity

SEGMENTATION OF ANATOMICAL STRUCTURES BY CONNECTED STATISTICAL MODELS

This paper presents a framework for the segmentation of anatomical structures in medical imagery by connected statistical models. The framework is based on three types of models: first, generic models which operate directly on image intensities, second, connecting models that impose restrictions on the spatial relationship of generic models, and third, a supervising model that represents an arbitrary number of generic and connecting models. In this paper, the statistical model of appearance is used as the generic model, whiles the statistical model of topology, obtained by applying principal component analysis (PCA) on aligned pose and shape parameters of the generic model, is used as the connecting model. The performance of such connected statistical model is demonstrated on anterior-posterior (AP) X-ray images of the hips and pelvis and compared to the modelling by one and six unconnected generic models. The most accurate and robust results were obtained by two-level hierarchical modelling, wherein connected statistical models were used first, followed by unconnected statistical models

Reflectance calibration of multimode optical fiber probes by probe-to-target distance reflectance profile modeling

Reflectance acquired with a multimode optical fiber probe can be related to optical properties of an investigated turbid medium by utilizing a light propagation model. During this step, a calibration of the light propagation model is required, as the modeled reflectance is normalized to the light energy of the source fiber, while the experimentally acquired reflectance is normalized to a reflective standard. Since currently established calibration methods based on liquid and solid turbid phantoms suffer from drawbacks such as low stability and dependence on other characterization methods, we propose a new method for reflectance calibration that is based on modeling and acquisition of probe-to-target distance reflectance profiles from first surface mirrors. We show that the spectrally resolved calibration factors can be estimated with a repeatability of 2% and agree within 10% with the reference values obtained by using turbid phantoms based on aqueous suspensions of polystyrene microspheres

New Robot for Power Line Inspection

Abstract-Power line inspection is of the utmost importance for the reliability and stability of electric power distribution. However, manual inspection is a hazardous, slow, expensive and unreliable task. Therefore, new highly specialized robots are required to improve the overall quality and safety of the power line inspection. The research conducted so far has been mainly focused on the development of climbing and flying robots. This paper first addresses the main achievements in the field of robotic power line inspection. The proposed solutions are critically assessed and the associated problems are outlined. Based on these findings, a new concept for robot-assisted power line inspection, combining both climbing and flying principles, is proposed in the second part of the paper. The proposed concept is critically assessed and related to the other established concepts so as to demonstrate its advantages and feasibility for a routine power line inspection

Deep shape features for predicting future intracranial aneurysm growth

Introduction: Intracranial aneurysms (IAs) are a common vascular pathology and are associated with a risk of rupture, which is often fatal. Aneurysm growth is considered a surrogate of rupture risktherefore, the study aimed to develop and evaluate prediction models of future artificial intelligence (AI) growth based on baseline aneurysm morphology as a computer-aided treatment decision support. Materials and methods: Follow-up CT angiography (CTA) and magnetic resonance angiography (MRA) angiograms of 39 patients with 44 IAs were classified by an expert as growing and stable (25/19). From the angiograms vascular surface meshes were extracted and the aneurysm shape was characterized by established morphologic features and novel deep shape features. The features corresponding to the baseline aneurysms were used to predict future aneurysm growth using univariate thresholding, multivariate random forest and multi-layer perceptron (MLP) learning, and deep shape learning based on the PointNet++ model. Results: The proposed deep shape feature learning method achieved an accuracy of 0.82 (sensitivity = 0.96, specificity = 0.63), while the multivariate learning and univariate thresholding methods were inferior with an accuracy of up to 0.68 and 0.63, respectively. Conclusion: High-performing classification of future growing IAs renders the proposed deep shape features learning approach as the key enabling tool to manage rupture risk in the “no treatment” paradigm of patient follow-up imaging