Algorithms for colour image processing based on neurological models

373 p. : il., gráf., fot., tablasColour image processing is nowadays mostly achieved through the extrapolation of algorithms developed for images in grey levels into three colour planes, either RGB or some transformed planes, such as HSI, CIELAB... These techniques provide reliable solutions only in simple situations. As colour is a perception and not a characteristic inherent to objects, this thesis has developed new bioinspired algorithms for colour image processing. The work of this thesis has joined elements in colour theory and processing undertaken in the human visual system. A new functional model of the retina has been developed where each cell type has been characterised according to its connections, distribution and size. A retina architecture has been created which provides detailed information about its cell elements and organisation. This has allowed the creation of a retina model that generates a set of parallel output channels as happens in the human retina. The level of detail provided in the model has allowed the characterisation of each of the pathways with a precision that is not present in existing models described in scientific publications. The development of a colour processing model requires the combination of a functional retina model with colour appearance models. This union has achieved a new algorithm for colour image processing that provides colour attributes, such as: hue, lightness, brightness, saturation, chroma, colourfulness as well as edge detection components both in chromatic as well as achromatic components. The results provided by this model have been compared with CIECAM02 model's ones and have obtained noticeably better results in the "ab" plane and in the attributes calculated on Munsell colour samples. The colour processing model is backed by its results and has allowed identifying output channels of the retina that make up the usual "a", "b" and "A" channels in colour appearance models. This model entails a step forward on colour processing techniques that shall be of great use for image segmentation, characterisation and object identification. Key Words Colour image processing, neuroinspired models, computational modelling, colour appearance models. Colour image processing is nowadays mostly achieved through the extrapolation of algorithms developed for images in grey levels into three colour planes, either RGB or some transformed planes, such as HSI, CIELAB... These techniques provide reliable solutions only in simple situations. As colour is a perception and not a characteristic inherent to objects, this thesis has developed new bioinspired algorithms for colour image processing. The work of this thesis has joined elements in colour theory and processing undertaken in the human visual system. A new functional model of the retina has been developed where each cell type has been characterised according to its connections, distribution and size. A retina architecture has been created which provides detailed information about its cell elements and organisation. This has allowed the creation of a retina model that generates a set of parallel output channels as happens in the human retina. The level of detail provided in the model has allowed the characterisation of each of the pathways with a precision that is not present in existing models described in scientific publications. The development of a colour processing model requires the combination of a functional retina model with colour appearance models. This union has achieved a new algorithm for colour image processing that provides colour attributes, such as: hue, lightness, brightness, saturation, chroma, colourfulness as well as edge detection components both in chromatic as well as achromatic components. The results provided by this model have been compared with CIECAM02 model's ones and have obtained noticeably better results in the "ab" plane and in the attributes calculated on Munsell colour samples. The colour processing model is backed by its results and has allowed identifying output channels of the retina that make up the usual "a", "b" and "A" channels in colour appearance models. This model entails a step forward on colour processing techniques that shall be of great use for image segmentation, characterisation and object identification. Key Words - Colour image processing, neuroinspired models, computational modelling, colour appearance models.El Gobierno Vasco ha proporcionado apoyo financiero a través del programa ETORTEK, para las estancias en el Instituto Técnico de Massachusetts (MIT) y en la Universidad de Cambridge

Melanoma and nevi subtype histopathological characterization with optical coherence tomography

Background: Melanoma incidence has continued to rise in the latest decades, and the forecast is not optimistic. Non-invasive diagnostic imaging techniques such as optical coherence tomography (OCT) are largely studied; however, there is still no agreement on its use for the diagnosis of melanoma. For dermatologists, the differentiation of non-invasive (junctional nevus, compound nevus, intradermal nevus, and melanoma in-situ) versus invasive (superficial spreading melanoma and nodular melanoma) lesions is the key issue in their daily routine. Methods: This work performs a comparative analysis of OCT images using haematoxylin-eosin (HE) and anatomopathological features identified by a pathologist. Then, optical and textural properties are extracted from OCT images with the aim to identify subtle features that could potentially maximize the usefulness of the imaging technique in the identification of the lesion?s potential invasiveness. Results: Preliminary features reveal differences discriminating melanoma in-situ from superficial spreading melanoma and also between melanoma and nevus subtypes that pose a promising baseline for further research. Conclusions: Answering the final goal of diagnosing non-invasive versus invasive lesions with OCT does not seem feasible in the short term, but the obtained results demonstrate a step forward to achieve this.This work has been funded by the Department of Economic Development, Sustainability and the Environment of the Basque Government (Spain) ELKARTEK projects ONKOTOOLS with grant numbers KK-2020/00069, the Spanish Ministry of Science and Education CERVERA project AI4ES with grant numbers CER-20211030, and by the ECSEL JU European project ASTONISH with the grant number 692470, UC Industrial Doctorate DI14

Detección de fibrilación ventricular mediante técnicas de aprendizaje profundo

Detección de fibrilación ventricular mediante técnicas de aprendizaje profundo La detección de arritmias ventriculares, en particular la fibrilación ventricular (FV), es parte fundamental de los algoritmos de clasificación de arritmias de los desfibriladores. Dichos algoritmos deciden si administrar la descarga de desfibrilación, para lo que clasifican los ritmos en desfibrilables (Sh) o no desfibrilables (NSh). Este trabajo propone un nuevo abordaje para la clasificación Sh/NSh de ritmos basado en un sistema de aprendizaje profundo. Para el trabajo se emplearon tres bases de datos públicas de la plataforma Physionet (CUDB, VFDB y AHADB), y se extrajeron segmentos de 4 y 8 segundos. Se anotaron los segmentos como Sh y NSh en base a las anotaciones de las bases de datos, que fueron auditadas por expertos. Los datos se dividieron por paciente en 80% para desarrollar los algoritmos y 20% para evaluación. El sistema de aprendizaje profundo emplea dos etapas convolucionales seguidas de, una red longshort- term-memory y una etapa final de clasificación basada en red neuronal. A modo de referencia se optimizó un clasificador SVM basado en las características de detección de arritmias ventriculares más eficientes publicadas en la literatura. Se calculó la sensibilidad (Se), ritmos desfibrilables, especificidad (Sp), ritmos no desfibrilables, y la precisión (Acc). El método de aprendizaje profundo proporcionó Se, Sp y Acc de 98.5%, 99.4% y 99.2% para segmentos de 4 segundos y 99.7%, 98.9%, 99.1% para segmentos de 8 segundos. El algoritmo permite detectar FV de forma fiable con segmentos de 4 segundos, corrigiendo un 30% de los errores del método basado en SVM.Este trabajo ha sido financiado por el Ministerio de Economía y Competitividad mediante el proyecto TEC2015-64678R junto con el Fondo Europeo de Desarrollo Regional (FEDER), así como por la UPVEHU mediante el proyecto EHU16/18

Novel Pixelwise Co-Registered Hematoxylin-Eosin and Multiphoton Microscopy Image Dataset for Human Colon Lesion Diagnosis

Colorectal cancer presents one of the most elevated incidences of cancer worldwide. Colonoscopy relies on histopathology analysis of hematoxylin-eosin (H&E) images of the removed tissue. Novel techniques such as multi-photon microscopy (MPM) show promising results for performing real-time optical biopsies. However, clinicians are not used to this imaging modality and correlation between MPM and H&E information is not clear. The objective of this paper is to describe and make publicly available an extensive dataset of fully co-registered H&E and MPM images that allows the research community to analyze the relationship between MPM and H&E histopathological images and the effect of the semantic gap that prevents clinicians from correctly diagnosing MPM images. The dataset provides a fully scanned tissue images at 10x optical resolution (0.5 m/px) from 50 samples of lesions obtained by colonoscopies and colectomies. Diagnostics capabilities of TPF and H&E images were compared. Additionally, TPF tiles were virtually stained into H&E images by means of a deep-learning model. A panel of 5 expert pathologists evaluated the different modalities into three classes (healthy, adenoma/hyperplastic, and adenocarcinoma). Results showed that the performance of the pathologists over MPM images was 65% of the H&E performance while the virtual staining method achieved 90%. MPM imaging can provide appropriate information for diagnosing colorectal cancer without the need for H&E staining. However, the existing semantic gap among modalities needs to be corrected.This work was supported by the PICCOLO project. This project has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement No. 732111. The sole re- sponsibility of this publication lies with the authors. The European Union is not responsible for any use that may be made of the information contained therei