D
Dimitris Maroulis
Researcher at National and Kapodistrian University of Athens
Publications - 131
Citations - 2366
Dimitris Maroulis is an academic researcher from National and Kapodistrian University of Athens. The author has contributed to research in topics: Image segmentation & Active contour model. The author has an hindex of 25, co-authored 131 publications receiving 2173 citations. Previous affiliations of Dimitris Maroulis include Athens State University.
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Fuzzy binary patterns for uncertainty-aware texture representation
TL;DR: A generic, uncertainty-aware methodology for the derivation of Fuzzy BP (FBP) texture models is proposed that assumes that a local neighbourhood can be partially characterized by more than one binary patterns due to noise-originated uncertainty in the pixel values.
A Contourlet Transform Feature Extraction Scheme for Ultrasound Thyroid Texture Classification
TL;DR: Results provide evidence that CT based texture features can be successfully applied for the classification of different types of texture in ultrasound thyroid images and show that the proposed methodology is more efficient than previous thyroid ultrasound representation methods proposed in the literature.
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Unsupervised SVM-based gridding for DNA microarray images
TL;DR: This paper presents a novel method for unsupervised DNA microarray gridding based on support vector machines (SVMs) that is robustness in the presence of artifacts, noise and weakly expressed spots, as well as image rotation.
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Texture multichannel measurements for cancer precursors’ identification using support vector machines
TL;DR: A novel framework for the automated identification of colon cancer precursors based on the processing of color video frames acquired during endoscopy is proposed, which has proven to provide high discrimination of image regions corresponding to normal and abnormal tissue.
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Computer-Based Nodule Malignancy Risk Assessment in Thyroid Ultrasound Images
TL;DR: This paper presents a computer-based approach for detection, delineation, and malignancy risk assessment of thyroid nodules in ultrasound (US) images that contributes to the objectification of the diagnostic process by the utilization of explicit image features and could contribute to the reduction of false medical decisions.