Skin Lesion Correspondence Localization in Total Body Photography

Longitudinal tracking of skin lesions - finding correspondence, changes in morphology, and texture - is beneficial to the early detection of melanoma. However, it has not been well investigated in the context of full-body imaging. We propose a novel framework combining geometric and texture information to localize skin lesion correspondence from a source scan to a target scan in total body photography (TBP). Body landmarks or sparse correspondence are first created on the source and target 3D textured meshes. Every vertex on each of the meshes is then mapped to a feature vector characterizing the geodesic distances to the landmarks on that mesh. Then, for each lesion of interest (LOI) on the source, its corresponding location on the target is first coarsely estimated using the geometric information encoded in the feature vectors and then refined using the texture information. We evaluated the framework quantitatively on both a public and a private dataset, for which our success rates (at 10 mm criterion) are comparable to the only reported longitudinal study. As full-body 3D capture becomes more prevalent and has higher quality, we expect the proposed method to constitute a valuable step in the longitudinal tracking of skin lesions.Comment: MICCAI-202

Accurate segmentation and registration of skin lesion images to evaluate lesion change

Skin cancer is a major health problem. There are several techniques to help diagnose skin lesions from a captured image. Computer-aided diagnosis (CAD) systems operate on single images of skin lesions, extracting lesion features to further classify them and help the specialists. Accurate feature extraction, which later on depends on precise lesion segmentation, is key for the performance of these systems. In this paper, we present a skin lesion segmentation algorithm based on a novel adaptation of superpixels techniques and achieve the best reported results for the ISIC 2017 challenge dataset. Additionally, CAD systems have paid little attention to a critical criterion in skin lesion diagnosis: the lesion's evolution. This requires operating on two or more images of the same lesion, captured at different times but with a comparable scale, orientation, and point of view; in other words, an image registration process should first be performed. We also propose in this work, an image registration approach that outperforms top image registration techniques. Combined with the proposed lesion segmentation algorithm, this allows for the accurate extraction of features to assess the evolution of the lesion. We present a case study with the lesion-size feature, paving the way for the development of automatic systems to easily evaluate skin lesion evolutionThis work was supported in part by the Spanish Government (HAVideo, TEC2014-53176-R) and in part by the TEC department (Universidad Autonoma de Madrid

Computer aided diagnosis system using dermatoscopical image

Computer Aided Diagnosis (CAD) systems for melanoma detection aim to mirror the expert dermatologist decision when watching a dermoscopic or clinical image. Computer Vision techniques, which can be based on expert knowledge or not, are used to characterize the lesion image. This information is delivered to a machine learning algorithm, which gives a diagnosis suggestion as an output. This research is included into this field, and addresses the objective of implementing a complete CAD system using ‘state of the art’ descriptors and dermoscopy images as input. Some of them are based on expert knowledge and others are typical in a wide variety of problems. Images are initially transformed into oRGB, a perceptual color space, looking for both enhancing the information that images provide and giving human perception to machine algorithms. Feature selection is also performed to find features that really contribute to discriminate between benign and malignant pigmented skin lesions (PSL). The problem of robust model fitting versus statistically significant system evaluation is critical when working with small datasets, which is indeed the case. This topic is not generally considered in works related to PSLs. Consequently, a method that optimizes the compromise between these two goals is proposed, giving non-overfitted models and statistically significant measures of performance. In this manner, different systems can be compared in a fairer way. A database which enjoys wide international acceptance among dermatologists is used for the experiments.Ingeniería de Sistemas Audiovisuale

Combining local features and region segmentation: methods and applications

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Tecnología Electrónica y de las Comunicaciones. Fecha de lectura: 23-01-2020Esta tesis tiene embargado el acceso al texto completo hasta el 23-07-2021Muchas y muy diferentes son las propuestas que se han desarrollado en el área de la visión artificial para la extracción de información de las imágenes y su posterior uso. Entra las más destacadas se encuentran las conocidas como características locales, del inglés local features, que detectan puntos o áreas de la imagen con ciertas características de interés, y las describen usando información de su entorno (local). También destacan las regiones en este área, y en especial este trabajo se ha centrado en los segmentadores en regiones, cuyo objetivo es agrupar la información de la imagen atendiendo a diversos criterios. Pese al enorme potencial de estas técnicas, y su probado éxito en diversas aplicaciones, su definición lleva implícita una serie de limitaciones funcionales que les han impedido exportar sus capacidades a otras áreas de aplicación. Se pretende impulsar el uso de estas herramientas en dichas aplicaciones, y por tanto mejorar los resultados del estado del arte, mediante la propuesta de un marco de desarrollo de nuevas soluciones. En concreto, la hipótesis principal del proyecto es que las capacidades de las características locales y los segmentadores en regiones son complementarias, y que su combinación, realizada de la forma adecuada, las maximiza a la vez que minimiza sus limitaciones. El principal objetivo, y por tanto la principal contribución del proyecto, es validar dicha hipótesis mediante la propuesta de un marco de desarrollo de nuevas soluciones combinando características locales y segmentadores para técnicas con capacidades mejoradas. Al tratarse de un marco de combinación de dos técnicas, el proceso de validación se ha llevado a cabo en dos pasos. En primer lugar se ha planteado el caso del uso de segmentadores en regiones para mejorar las características locales. Para verificar la viabilidad y el éxito de esta combinación se ha desarrollado una propuesta específica, SP-SIFT, que se ha validado tanto a nivel experimental como a nivel de aplicación real, en concreto como técnica principal de algoritmos de seguimiento de objetos. En segundo lugar, se ha planteado el caso de uso de características locales para mejorar los segmentadores en regiones. Para verificar la viabilidad y el éxito de esta combinación se ha desarrollado una propuesta específica, LF-SLIC, que se ha validado tanto a nivel experimental como a nivel de aplicación real, en concreto como técnica principal de un algoritmo de segmentación de lesiones pigmentadas de la piel. Los resultados conceptuales han probado que las técnicas mejoran a nivel de capacidades. Los resultados aplicados han probado que estas mejoras permiten el uso de estas técnicas en aplicaciones donde antes no tenían éxito. Con ello, se ha considerado la hipótesis validada, y por tanto exitosa la definición de un marco para el desarrollo de nuevas técnicas específicas con capacidades mejoradas. En conclusión, la principal aportación de la tesis es el marco de combinación de técnicas, plasmada en sus dos propuestas específicas: características locales mejoradas con segmentadores y segmentadores mejorados con características locales, y en el éxito conseguido en sus aplicaciones.A huge number of proposals have been developed in the area of computer vision for information extraction from images, and its further use. One of the most prevalent solutions are those known as local features. They detect points or areas of the image with certain characteristics of interest, and describe them using information from their (local) environment. The regions also stand out in the area, and especially this work has focused on the region segmentation algorithms, whose objective is to group the information of the image according to di erent criteria. Despite the enormous potential of these techniques, and their proven success in a number of applications, their de nition implies a series of functional limitations that have prevented them from exporting their capabilities to other application areas. In this thesis, it is intended to promote the use of these tools in these applications, and therefore improve the results of the state of the art, by proposing a framework for developing new solutions. Speci cally, the main hypothesis of the project is that the capacities of the local features and the region segmentation algorithms are complementary, and thus their combination, carried out in the right way, maximizes them while minimizing their limitations. The main objective, and therefore the main contribution of the thesis, is to validate this hypothesis by proposing a framework for developing new solutions combining local features and region segmentation algorithms, obtaining solutions with improved capabilities. As the hypothesis is proposing to combine two techniques, the validation process has been carried out in two steps. First, the use case of region segmentation algorithms enhancing local features. In order to verify the viability and success of this combination, a speci c proposal, SP-SIFT, was been developed. This proposal was validated both experimentally and in a real application scenario, speci cally as the main technique of object tracking algorithms. Second, the use case of enhancing region segmentation algorithm with local features. In order to verify the viability and success of this combination, a speci c proposal, LF-SLIC, was developed. The proposal was validated both experimentally and in a real application scenario, speci cally as the main technique of a pigmented skin lesions segmentation algorithm. The conceptual results proved that the techniques improve at the capabilities level. The application results proved that these improvements allow the use of this techniques in applications where they were previously unsuccessful. Thus, the hypothesis can be considered validated, and therefore the de nition of a framework for the development of new techniques with improved capabilities can be considered successful. In conclusion, the main contribution of the thesis is the framework for the combination of techniques, embodied in the two speci c proposals: enhanced local features with region segmentation algorithms, and region segmentation algorithms enhanced with local features; and in the success achieved in their applications.The work described in this Thesis was carried out within the Video Processing and Understanding Lab at the Department of Tecnología Electrónica y de las Comunicaciones, Escuela Politécnica Superior, Universidad Autónoma de Madrid (from 2014 to 2019). It was partially supported by the Spanish Government (TEC2014-53176-R, HAVideo)

Methods based on B-splines for model representation, numerical analysis and image registration

The thesis consists of inter-connected parts for modeling and analysis using newly developed isogeometric methods. The main parts are reproducing kernel triangular B-splines, extended isogeometric analysis for solving weakly discontinuous problems, collocation methods using superconvergent points, and B-spline basis in image registration applications. Each topic is oriented towards application of isogeometric analysis basis functions to ease the process of integrating the modeling and analysis phases of simulation. First, we develop reproducing a kernel triangular B-spline-based FEM for solving PDEs. We review the triangular B-splines and their properties. By definition, the triangular basis function is very flexible in modeling complicated domains. However, instability results when it is applied for analysis. We modify the triangular B-spline by a reproducing kernel technique, calculating a correction term for the triangular kernel function from the chosen surrounding basis. The improved triangular basis is capable to obtain the results with higher accuracy and almost optimal convergence rates. Second, we propose an extended isogeometric analysis for dealing with weakly discontinuous problems such as material interfaces. The original IGA is combined with XFEM-like enrichments which are continuous functions themselves but with discontinuous derivatives. Consequently, the resulting solution space can approximate solutions with weak discontinuities. The method is also applied to curved material interfaces, where the inverse mapping and the curved triangular elements are considered. Third, we develop an IGA collocation method using superconvergent points. The collocation methods are efficient because no numerical integration is needed. In particular when higher polynomial basis applied, the method has a lower computational cost than Galerkin methods. However, the positions of the collocation points are crucial for the accuracy of the method, as they affect the convergent rate significantly. The proposed IGA collocation method uses superconvergent points instead of the traditional Greville abscissae points. The numerical results show the proposed method can have better accuracy and optimal convergence rates, while the traditional IGA collocation has optimal convergence only for even polynomial degrees. Lastly, we propose a novel dynamic multilevel technique for handling image registration. It is application of the B-spline functions in image processing. The procedure considered aims to align a target image from a reference image by a spatial transformation. The method starts with an energy function which is the same as a FEM-based image registration. However, we simplify the solving procedure, working on the energy function directly. We dynamically solve for control points which are coefficients of B-spline basis functions. The new approach is more simple and fast. Moreover, it is also enhanced by a multilevel technique in order to prevent instabilities. The numerical testing consists of two artificial images, four real bio-medical MRI brain and CT heart images, and they show our registration method is accurate, fast and efficient, especially for large deformation problems

Elastic image registration using parametric deformation models

The main topic of this thesis is elastic image registration for biomedical applications. We start with an overview and classification of existing registration techniques. We revisit the landmark interpolation which appears in the landmark-based registration techniques and add some generalizations. We develop a general elastic image registration algorithm. It uses a grid of uniform B-splines to describe the deformation. It also uses B-splines for image interpolation. Multiresolution in both image and deformation model spaces yields robustness and speed. First we describe a version of this algorithm targeted at finding unidirectional deformation in EPI magnetic resonance images. Then we present the enhanced and generalized version of this algorithm which is significantly faster and capable of treating multidimensional deformations. We apply this algorithm to the registration of SPECT data and to the motion estimation in ultrasound image sequences. A semi-automatic version of the registration algorithm is capable of accepting expert hints in the form of soft landmark constraints. Much fewer landmarks are needed and the results are far superior compared to pure landmark registration. In the second part of this thesis, we deal with the problem of generalized sampling and variational reconstruction. We explain how to reconstruct an object starting from several measurements using arbitrary linear operators. This comprises the case of traditional as well as generalized sampling. Among all possible reconstructions, we choose the one minimizing an a priori given quadratic variational criterion. We give an overview of the method and present several examples of applications. We also provide the mathematical details of the theory and discuss the choice of the variational criterion to be used