D
Dimitris K. Iakovidis
Researcher at University of Thessaly
Publications - 151
Citations - 3763
Dimitris K. Iakovidis is an academic researcher from University of Thessaly. The author has contributed to research in topics: Computer science & Feature extraction. The author has an hindex of 31, co-authored 129 publications receiving 3097 citations. Previous affiliations of Dimitris K. Iakovidis include Research Academic Computer Technology Institute & National and Kapodistrian University of Athens.
Papers
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Journal ArticleDOI
Computer-aided tumor detection in endoscopic video using color wavelet features
TL;DR: An approach to the detection of tumors in colonoscopic video based on a new color feature extraction scheme to represent the different regions in the frame sequence based on the wavelet decomposition, reaching 97% specificity and 90% sensitivity.
Journal ArticleDOI
Software for enhanced video capsule endoscopy: challenges for essential progress
TL;DR: An in-depth critical analysis is presented that aims to inspire and align the agendas of the two scientific groups in the field of small bowel diseases.
Journal ArticleDOI
Intuitionistic Fuzzy Cognitive Maps for Medical Decision Making
TL;DR: A novel approach based on cognitive maps and intuitionistic fuzzy logic is proposed, which extends the existing fuzzy cognitive map (FCM) by considering the expert's hesitancy in the determination of the causal relations between the concepts of a domain.
Book ChapterDOI
Fuzzy Local Binary Patterns for Ultrasound Texture Characterization
TL;DR: The proposed Fuzzy Local Binary Pattern approach was experimentally evaluated for supervised classification of nodular and normal samples from thyroid ultrasound images and the results validate its effectiveness over LBP and other common feature extraction methods.
Journal ArticleDOI
Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification
Dimitris K. Iakovidis,Spiros V. Georgakopoulos,Michael Vasilakakis,Anastasios Koulaouzidis,Vassilis P. Plagianakos +4 more
TL;DR: A novel methodology for automatic detection and localization of gastrointestinal (GI) anomalies in endoscopic video frame sequences, performed with weakly annotated images, which makes it a cost-effective approach for the analysis of large videoendoscopy repositories.