Int J Comput Assist Radiol Surg - Detection and quantification of intracerebral and intraventricular hemorrhage from computed tomography images with adaptive thresholding and case-based reasoning.

Tópicos

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Resumo

RPOSE: ???Hemorrhage within the brain space (HWBS) involves the brain parenchyma and ventricle systems, and is associated with high morbidity and mortality. Computed tomography (CT) head scans are the recommended modality for diagnosis and treatment for HWBS. However, HWBS detection may be difficult when the hemorrhage is inconspicuous, while quantification is hard as hemorrhage can have very variable intensity that overlaps with normal brain tissue. An algorithm is proposed to detect and quantify HWBS.METHODS: Adaptive thresholding and case-based reasoning (CBR) were applied to HWBS in four steps: preprocessing to extract the brain, adaptive thresholding based on local contrast with varied window sizes to derive candidate HWBS regions, case representation to represent each candidate HWBS region by parameters on context as well as intensity and geometrical characteristics, and classification of HWBS by taking each candidate HWBS region as a case and applying CBR. Additionally, case base indexing and weights optimization were used to increase retrieval speed and improve performances. Refinement of each recognized HWBS was performed for quantifying HWBS.RESULTS: Validation on 426 clinical CT data indicates that the proposed algorithm achieved a detection rate of 94.4?% and recall of 79.2?% for detecting HWBS regions. Visually, the HWBS regions calculated from adaptive thresholding plus refinement agreed well with expert delineation. For 10 representative data with small to large hemorrhage, the algorithm quantitatively yielded a segmentation accuracy of [Formula: see text]. Case base indexing increased the retrieval speed by 41.1 times at the expense of decreasing detection rate of 0.5?% and recall of 2.6?%. Genetic algorithm optimization enhanced the detection rate and recall to, respectively, 94.9 and 83.5?%.CONCLUSIONS: We developed and tested an algorithm that combined adaptive thresholding and CBR for detecting and quantifying HWBS. Experiments showed that adaptive thresholding could provide suitable candidates, while CBR was able to identify HWBS regions. The proposed method has potential as a new tool for accurately detecting and quantifying HWBS.

Resumo Limpo

rpose hemorrhag within brain space hwbs involv brain parenchyma ventricl system associ high morbid mortal comput tomographi ct head scan recommend modal diagnosi treatment hwbs howev hwbs detect may difficult hemorrhag inconspicu quantif hard hemorrhag can variabl intens overlap normal brain tissu algorithm propos detect quantifi hwbsmethod adapt threshold casebas reason cbr appli hwbs four step preprocess extract brain adapt threshold base local contrast vari window size deriv candid hwbs region case represent repres candid hwbs region paramet context well intens geometr characterist classif hwbs take candid hwbs region case appli cbr addit case base index weight optim use increas retriev speed improv perform refin recogn hwbs perform quantifi hwbsresult valid clinic ct data indic propos algorithm achiev detect rate recal detect hwbs region visual hwbs region calcul adapt threshold plus refin agre well expert delin repres data small larg hemorrhag algorithm quantit yield segment accuraci formula see text case base index increas retriev speed time expens decreas detect rate recal genet algorithm optim enhanc detect rate recal respect conclus develop test algorithm combin adapt threshold cbr detect quantifi hwbs experi show adapt threshold provid suitabl candid cbr abl identifi hwbs region propos method potenti new tool accur detect quantifi hwbs

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