Abstract
Accurate and early interpretation of CT scan images in TBI patients reduces the critical
time for diagnosis and management. As mentioned in other studies, automated CT interpretation
using the feature extraction method is a rapid and accurate tool. Despite several
studies on the machine and deep learning employing algorithms for automated CT interpretations,
it has its challenges. This study presents a concept note and proposes a feature-based
computer-aided diagnostic method to perform automated CT interpretation in TBI. The
method consists of preprocessing, segmentation, and extraction. We have described
a simple way of classifying the CT scan head into five circumferential zones in this
method. The zones are identified quickly based on the anatomic characteristics and
specific pathologies that affect each zone. Then, we have provided an overview of
different pathologies affecting each of these zones. Utilizing these zones for automated
CT interpretation will also be a helpful resource for concerned physicians during
the odd and rush hours.
Keywords
automated image analysis - segmentation - CT scan brain - traumatic brain injury
