Texture–based graph model of the lungs for drug resistance detection, tuberculosis type classification, and severity scoring : participation in the ImageCLEF 2018 Tuberculosis Task

Dicente Cid, Yashin (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis) ; University of Geneva, Switzerland) ; Müller, Henning (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis); University of Geneva, Switzerland)

In 2018, ImageCLEF proposed a task using CT (Computed Tomography) scans of patients with tuberculosis (TB). The task was divided into three subtasks: multi{drug resistance detection, TB type classi _cation, and severity scoring. In this work we present a graph model of the lungs capable of characterizing TB patients with di_erent lung problems. The graph contains a _xed number of nodes with weighted edges based on dissimilarity measures between texture descriptors computed in the nodes. This model encodes the texture distribution along the lungs, making it suitable for describing patients with di_erent TB types. The results show the strength of the technique, leading to high results in the three subtasks.


Keywords:
Conference Type:
full paper
Faculty:
Economie et Services
School:
HEG-VS
Institute:
Institut Informatique de gestion
Subject(s):
Economie/gestion
Publisher:
Avignon, France, 10-14 September 2018
Date:
2018-09
Avignon, France
10-14 September 2018
Pagination:
9 p.
Published in:
Proceedings of the CLEF 2018 Working Notes
External resources:
Appears in Collection:



 Record created 2018-10-22, last modified 2019-06-11

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