Approximating Major Cerebrospinal Fluid Space in a Distance Transformation Based Bayesian Framework from Clinical Non-Enhanced Computed Tomography Images

Automatically detecting the abnormality within cerebrospinal fluid space (CSF) from clinical non-enhanced computed tomography (NCT) images is significant since it can help diagnosis of many neurological diseases such as hydrocephalus and subarachnoid hemorrhage (SAH). However, extracting CSF space from NCT images is not easy, due to such factors as small size of CSF, partial volume effect due to large slice spacing, varied grayscale of CSF especially when hemorrhage appears in CSF space. In this paper a method is proposed to approximate major CSF space for detecting hemorrhage. The tissues with good contrast in the brain are extracted as anatomical landmarks, followed by extraction of features using distance transformation with respect to the landmarks. By combining kernel density estimation (KDE) and mutual information (MI), discriminative features are selected for Bayesian decision based classification. Experiments show that the proposed method can locate the major CSF space.