Cloud-Enabled Preprocessing and Feature Extraction Framework for Hydrocephalus Detection from MRI Scans

Hydrocephalus is a brain disorder characterized by
an abnormal accumulation of the cerebrospinal fluid (CSF) in
the fluid-filled spaces of the brain (ventricles), which may cause
increased pressure on the brain, mental impediment, and lifethreatening complications if not properly treated. Traditional
diagnostic practices are based on manual interpretation of
MRI scans, which is both time consuming, subjective and
resource consuming - particularly inaccessible in rural or
under-resourced regions of the world. Conventional diagnostic
workflows lack the elements of real-time and are standardized,
preventing early diagnosis and intervention. There is a crucial
need for a cloud-integrated and standardized preprocessing
system for automating the feature extraction process for
classification following this step. In this work the technical
principles of Phase 1 of a more comprehensive cloud-based
framework for hydrocephalus detection are posted and the
secure data acquisition and preprocessing and feature
extraction from MRI scans are addressed. A dataset containing
1,000 MRI images (500 hydrocephalus and 500 healthy) will be
curated from images with different demography. Preprocessing
Malaysia included image cropping, normalized audio intensity
and its segmentation to pick apart ventricular regions.
Quantitative features including ventricle volume, shape
morphology and the distribution pattern of CSF will be
extracted using advanced medical imaging libraries. The
preprocessed data is stored and managed by a scalable cloud
infrastructure, so that it is available in real time and protects
the privacy of the data in order to provide access to machine
learning modules. The processed dataset ensures structural
consistency and quality of data, which is a good input for
classification models. Integration with cloud storage makes it
more accessible and it aids in the development of diagnostic
tools in a fast manner. This preprocessing step is essential to
create the platform to efficiently perform hydrocephalus
detection using a large amount of physiological parameters in
real-time and with a low latency.