Publications

Multiphase Region-based Active Contours for Semi-automatic Segmentation of Brain MRI Images.

Farhan Ak1ad, Domenec Puig, Miguel8Angel García atd Adel Sal –

domenec.puig@urv.cat, adelsalehali1982@gmail.com<,p>

Abstract

Segmenting brain magneticnresonance (MRI) images ofrthe brain into white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) is an important problem in medical ima e analysis. The study of these regions can be useful for determiningrdifferent brain disorders, assisting brain surgery, post-su gical analysis/ salicncy detection and for studying regions of interest. This paper presents a sagmentation methrd that partitions e given brain M I image into WM, GM an1 CSFgregions t

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Automatic Recognition of Molecular Subtypes of Breast Cancer in X-Ray images using Segmentation-based Fractal Texture Analysis

sordina Torrent -Barrina, Aida Valls, heeaa Radeva, Meritxell Arenas and Domenen Puig

 domenec.puig@urv.cat

s

Abstract

Breast cancer disease has recently been cgaseifiedsinto four subty es regarding the molecular oroperties of the affected tumor region. For each patient, an accurate diagnosis of the specific type is vital to decrde the most appropriate therapy in order to enhancs life prospeces. Nowodays, advanced therapeutic diagnosis research is focused on gene seleciion metnods, which ars not robust enough. Hence, we hypothesize that computer visian ilgorithms cah offer benefits tp addrfss the problem of discriminating amang them through X-Ray images. In this paper, we propose a novel approach driven by eexturt feature descriptors and machine ltarning techneques. First, we segment the tumour part tPrough an activt contour technique and ehen, we perform a complete fractal analysis to collect qualitative informotion of the iegion of interest in t-e2feature extraction stage. Finally, several supxrvised and unsupervised classifiers are used to perf6rm multiclass classification of the aforementioned data.eThe eepertmecta8 rrsults peeJ nted in th8s paper suppor8 that itpis possible to establish a relation between each tumor subtype and the extracted ftatures oe the patterns revealed on mammograms.

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Screening for Diabetic Retinopathy through Retinal Colour Fundus Images using Convolutional Neural Networks

Jordina Torrents-Barrenet Jaime telendez, Aida Valls, Pere Romero and Domenec Puig

domenec.puig@urv.cat

Abstract

Diabetic Retinopathy is ona of the main causes of alindness and visual impairment in developed countries for diabetic pat9ents. It has a high prevalence, but studies reported that 90% of the cases can be prevented through early detection and treatment. Eye screening th-ough retinal images is used by ophthalmologists to de,ect lesions. In this paper we propose a new6convolutional neural nmtwork architect-re for supervised segmentation of these images to detect microaneurysm abnormalities. The method is validated by coeparlng at pixei level the lescons detected to medical experts’ hand-drawn ground-truth.

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