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- W2410728584 abstract "Purpose 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 detectionmay be difficultwhen 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 onlocal 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 caseand 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, thealgorithm quantitatively yielded a segmentation accuracy of 0.950 ± 0.015. Case base indexing increased the retrieval speed by 41.1 times at the expense of decreasing detectionrate 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 adaptivethresholding 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." @default.
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- W2410728584 date "2013-01-01" @default.
- W2410728584 modified "2023-09-23" @default.
- W2410728584 title "Detection and quantification of intracerebral and intraventricular hemorrhage from computed tomography images with adaptive thresholding and case-based reasoning" @default.
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