Computer-supported diagnosis for endotension cases in endovascular aortic aneurysm repair evolution.
An abdominal aortic aneurysm (AAA) is a localized abnormal enlargement of the abdominal aorta with fatal consequences if not treated on time. The endovascular aneurysm repair (EVAR) is a minimal invasive therapy that reduces recovery times and improves survival rates in AAA cases. Nevertheless, post-operation difficulties can appear influencing the evolution of treatment. The objective of this work is to develop a pilot computer-supported diagnosis system for an automated characterization of EVAR progression from CTA images. The system is based on the extraction of texture features from post-EVAR thrombus aneurysm samples and on posterior classification. Three conventional texture-analysis methods, namely the gray level co-occurrence matrix (GLCM), the gray level run length matrix (GLRLM), the gray level difference method (GLDM), and a new method proposed by the authors, the run length matrix of local co-occurrence matrices (RLMLCM), were applied to each sample. Several classification schemes were experimentally evaluated. The ensembles of a k-nearest neighbor (k-NN), a multilayer perceptron neural network (MLP-NN), and a support vector machine (SVM) classifier fed with a reduced version of texture features resulted in a better performance (Az=94.35±0.30), as compared to the classification performance of the other alternatives.
Systems Engineering and Automatic Control Department - EUP, University of the Basque Country (UPV/EHU), Escuela Universitaria Politécnica, Plaza Europa 1, 20018 San Sebastián, Spain. Electronic address: g.garcia@ehu.es.
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Systems Engineering and Automatic Control Department - EUP, University of the Basque Country (UPV/EHU), Escuela Universitaria Politécnica, Plaza Europa 1, 20018 San Sebastián, Spain. Electronic address: arantxa.tapia@ehu.es.
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Tapia
Interventional Radiology Department, Donostia Hospital, Paseo Doctor José Beguiristain s/n, 20014 Donostia-San Sebastián, Spain. Electronic address: mariano.deblasbravo@osakidetza.net.
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De Blas
D000465:Algorithms; D000792:Angiography / Q000379:methods; D017544:Aortic Aneurysm, Abdominal / Q000000981:diagnostic imaging / Q000601:surgery*; D002309:Cardiology / Q000379:methods; D013504:Cardiovascular Surgical Procedures / Q000379:methods*; D003287:Contrast Media; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016014:Linear Models; D016571:Neural Networks, Computer; D012984:Software; D060388:Support Vector Machine
D003287:Contrast Media
Aneurysm; EVAR; Endotension; Ensembles of classifiers; Texture features