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PURPOSE This paper proposes to characterize the continuum of colorectal cancer (CRC) using multiple texture features extracted from multispectral optical microscopy images. Three types of pathological tissues (PT) are considered: benign hyperplasia, intraepithelial neoplasia and carcinoma. MATERIALS AND METHODS In the proposed approach, the region of(More)
OBJECTIVE Predicting the survival outcome of patients with glioblastoma multiforme (GBM) is of key importance to clinicians for selecting the optimal course of treatment. The goal of this study was to evaluate the usefulness of geometric shape features, extracted from MR images, as a potential non-invasive way to characterize GBM tumours and predict the(More)
In this paper we address the problem of robust unknown input observer design for continuous nonlinear timedelay systems. To investigate the robustness of the studied system, we compare between two approaches both dependent on the delay, the first one is based on a standard H∞ method and the second on Sobolev norms. New sufficient synthesis conditions are(More)
Emerging evidence suggests the presence of neuroanatomical abnormalities in subjects with autism spectrum disorder (ASD). Identifying anatomical correlates could thus prove useful for the automated diagnosis of ASD. Radiomic analyses based on MRI texture features have shown a great potential for characterizing differences occurring from tissue(More)
This paper proposes to use texture features extracted from multispectral microscopic images to detect histopathological abnormalities related to colorectal cancer (CRC): stroma (ST), benign hyperplasia (BH), intraepithelial neoplasia (IN) and carcinoma (Ca). Texture features, based on gray-level co-occurrence matrices (GLCM) and discrete wavelets (DW), are(More)
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