Texture descriptors applied to digital mammography

Breast cancer is the second cause of death among women cancers. Computer Aided Detection has been demon- strated an useful tool for early diagnosis, a crucial as- pect for a high survival rate. In this context, several re- search works have incorporated texture features in mam- mographic image segmentation and description such as Gray-Level co-occurrence matrices, Local Binary Pat- terns, and many others. This paper presents an approach for breast density classi¯cation based on segmentation and texture feature extraction techniques in order to clas- sify digital mammograms according to their internal tis- sue. The aim of this work is to compare di®erent texture descriptors on the same framework (same algorithms for segmentation and classi¯cation, as well as same images). Extensive results prove the feasibility of the proposed ap- proach.Postprint (published version

Experiencias en un plan piloto de adaptación al EEES: Hoja de ruta para coordinadores

En este artículo se presenta una guía en formato “hoja de ruta para coordinadores de titulación” que relata la experiencia del equipo de dirección del ámbito informático, sobre la puesta en marcha del ECTS en las titulaciones de ITIG e ITIS. Durante el curso 2004-05 en la Universidad de Girona se inicia un plan piloto de adaptación de titulaciones al EEES. Desde entonces hemos desarrollado un conjunto de actividades que van desde la información, la formación, la coordinación de cursos, la puesta en marcha de herramientas y métodos, la definición de competencias, hasta la evaluación tanto a nivel de asignatura como de titulación. Los destinatarios de estas actividades han sido principalmente el colectivo de profesores y los estudiantes de la titulación, pero también se ha involucrado al PAS y profesores de otras titulaciones. En este artículo se resumen las acciones que hemos llevado a cabo en estos tres cursos, y se invita a reflexionar sobre los éxitos y los problemas que nos hemos encontrado

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

International audienceProstate cancer is the second most diagnosed cancer of men all over the world. In the last decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed improving diagnosis.In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systemshave been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field ofresearch for the last ten years. This survey aims to provide a comprehensive review of the state of the art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aidedsystem. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to theresearch community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey

Aplicación web para la gestión de una base de datos pública de mamografía digital: MamoDB

Cada vez son más los hospitales que disponen de sistemas computarizados de adquisición y visualización de imágenes digitales , con las ventaj as que ello supone cu anto a acceso a la información , capacidad de diagnóstico y aprendizaje . Sin embargo, el volumen ingente de datos requiere de nuevas herramientas para su alm acenaje, gestión y recuperación . En este trabajo se propone un modelo de estructura basado en tecnol ogía web como herramienta de ayuda al diagnóstico de Cáncer de Mama. La estructura propuesta se basa en la administración de imágenes y estudios mamográfico s con el objetivo de ser un referente en la comunidad científica. Su arquitectura, metodología y aplicación en formato web se presentan en es te trabajo así como conclusiones y trabajos futurosPostprint (published version

Normalization of T2W-MRI Prostate Images using Rician a priori

International audienceProstate cancer is reported to be the second most frequently diagnosed cancer of men in the world. In practise, diagnosis can be affected by multiple factors which reduces the chance to detect the potential lesions. In the last decades, new imaging techniques mainly based on MRI are developed in conjunction with Computer-Aided Diagnosis (CAD) systems to help radiologists for such diagnosis. CAD systems are usually designed as a sequential process consisting of four stages: pre-processing, segmentation, registration and classification. As a pre-processing, image normalization is a critical and important step of the chain in order to design a robust classifier and overcome the inter-patients intensity variations. However, little attention has been dedicated to the normalization of T2W-Magnetic Resonance Imaging (MRI) prostate images. In this paper, we propose two methods to normalize T2W-MRI prostate images: (i) based on a Rician a priori and (ii) based on a Square-Root Slope Function (SRSF) representation which does not make any assumption regarding the Probability Density Function (PDF) of the data. A comparison with the state-of-the-art methods is also provided. The normalization of the data is assessed by comparing the alignment of the patient PDFs in both qualitative and quantitative manners. In both evaluation, the normalization using Rician a priori outperforms the other state-of-the-art methods