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Detection and quantification of intracerebral and intraventricular hemorrhage from computed tomography images with adaptive thresholding and case-based reasoning

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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 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 \(0.950\pm 0.015\). 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.

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Acknowledgments

This work has been supported by: National Program on Key Basic Research Project (No. 2013CB733800, 2013CB733803), Key Joint Program of National Natural Science Foundation and Guangdong Province (No. U1201257), National Natural Science Foundation of China (No. 61272328), Guangdong Natural Science Foundation (No. S2011010001820), and Shenzhen Key Basic Research Project (No. JC201005270370A). Authors would like to thank collaborating hospitals (Linyi People’s Hospital, Nanfang Hospital, and Tiantan Hospital) for providing the real clinical data.

Conflict of interest

There is no conflict of interest with any financial organization regarding the materials discussed or described in the paper.

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Correspondence to Qingmao Hu or Wenhua Huang.

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Zhang, Y., Chen, M., Hu, Q. et al. Detection and quantification of intracerebral and intraventricular hemorrhage from computed tomography images with adaptive thresholding and case-based reasoning. Int J CARS 8, 917–927 (2013). https://doi.org/10.1007/s11548-013-0830-x

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  • DOI: https://doi.org/10.1007/s11548-013-0830-x

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