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Automated detection of parenchymal changes of ischemic stroke in non-contrast computer tomography: a fuzzy approach

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10.1016/j.bspc.2018.05.037
 
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hdl:2117/132813

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Davis, Alberto
Gordillo Castillo, Nelly
Montseny Masip, EduardMés informacióMés informació
Aymerich Martínez, Francisco JavierMés informacióMés informacióMés informació
López Córdova, M.
Mejía Muñoz, J.
Ortega, L.
Mederos, B.
Document typeArticle
Defense date2018-08-01
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
The detection of ischemic changes is a primary task in the interpretation of brain Computer Tomography (CT) of patients suffering from neurological disorders. Although CT can easily show these lesions, their interpretation may be difficult when the lesion is not easily recognizable. The gold standard for the detection of acute stroke is highly variable and depends on the experience of physicians. This research proposes a new method of automatic detection of parenchymal changes of ischemic stroke in Non-Contrast CT. The method identifies non-pathological cases (94 cases, 40 training, 54 test) based on the analysis of cerebral symmetry. Parenchymal changes in cases with abnormalities (20 cases) are detected by means of a contralateral analysis of brain regions. In order to facilitate the evaluation of abnormal regions, non-pathological tissues in Hounsfield Units were characterized using fuzzy logic techniques. Cases of non-pathological and stroke patients were used to discard/confirm abnormality with a sensitivity (TPR) of 91% and specificity (SPC) of 100%. Abnormal regions were evaluated and the presence of parenchymal changes was detected with a TPR of 96% and SPC of 100%. The presence of parenchymal changes of ischemic stroke was detected by the identification of tissues using fuzzy logic techniques. Because of abnormal regions are identified, the expert can prioritize the examination to a previously delimited region, decreasing the diagnostic time. The identification of tissues allows a better visualization of the region to be evaluated, helping to discard or confirm a stroke.
CitationDavis-Ortiz, A. [et al.]. Automated detection of parenchymal changes of ischemic stroke in non-contrast computer tomography: a fuzzy approach. "Biomedical signal processing and control", 1 Agost 2018, vol. 45, p. 117-127. 
URIhttp://hdl.handle.net/2117/132813
DOI10.1016/j.bspc.2018.05.037
ISSN1746-8094
Publisher versionhttps://www.sciencedirect.com/science/article/pii/S1746809418301459
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