A knowledge discovery approach to diagnosing intracranial hematomas on brain CT: recognition, measurement and classification

Authors:
Chun-Chih Liao;Furen Xiao;Jau-Min Wong;I-Jen Chiang
Affiliations:
Graduate Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan and Department of Neurosurgery, Taipei Hospital, Department of Health, Taipei, Taiwan;Graduate Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan;Graduate Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan;Graduate Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan and Graduate Institute of Medical Informatics, Taipei Medical University, Taipei, Taiwan
Venue:
ICMB'08 Proceedings of the 1st international conference on Medical biometrics
Year:
2008

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Abstract

Computed tomography (CT) of the brain is preferred study on neurological emergencies. Physicians use CT to diagnose various types of intracranial hematomas, including epidural, subdural and intracerebral hematomas according to their locations and shapes. We propose a novel method that can automatically diagnose intracranial hematomas by combining machine vision and knowledge discovery techniques. The skull on the CT slice is located and the depth of each intracranial pixel is labeled. After normalization of the pixel intensities by their depth, the hyperdense area of intracranial hematoma is segmented with multi-resolution thresholding and region-growing. We then apply C4.5 algorithm to construct a decision tree using the features of the segmented hematoma and the diagnoses made by physicians. The algorithm was evaluated on 48 pathological images treated in a single institute. The two discovered rules closely resemble those used by human experts, and are able to make correct diagnoses in all cases.