Assessment of hydrocephalus in children based on digital image processing and analysis

Author:

Fabijańska Anna1,Węgliński Tomasz1,Zakrzewski Krzysztof2,Nowosławska Emilia2

Affiliation:

1. Institute of Applied Computer Science Łódź University of Technology, Stefanowskiego 18/22, 90-924 Łódź, Poland

2. Department of Neurosurgery, Polish Mother’s Memorial Hospital, Research Institute in Łódź, Rzgowska 281/289, 93-338 Łódź, Poland

Abstract

Abstract Hydrocephalus is a pathological condition of the central nervous system which often affects neonates and young children. It manifests itself as an abnormal accumulation of cerebrospinal fluid within the ventricular system of the brain with its subsequent progression. One of the most important diagnostic methods of identifying hydrocephalus is Computer Tomography (CT). The enlarged ventricular system is clearly visible on CT scans. However, the assessment of the disease progress usually relies on the radiologist’s judgment and manual measurements, which are subjective, cumbersome and have limited accuracy. Therefore, this paper regards the problem of semi-automatic assessment of hydrocephalus using image processing and analysis algorithms. In particular, automated determination of popular indices of the disease progress is considered. Algorithms for the detection, semi-automatic segmentation and numerical description of the lesion are proposed. Specifically, the disease progress is determined using shape analysis algorithms. Numerical results provided by the introduced methods are presented and compared with those calculated manually by a radiologist and a trained operator. The comparison proves the correctness of the introduced approach.

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,Engineering (miscellaneous),Computer Science (miscellaneous)

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Detecting Hydrocephalus Using CT Scans and Machine Leaning Techniques;2023 2nd International Engineering Conference on Electrical, Energy, and Artificial Intelligence (EICEEAI);2023-12-27

2. Automatic determination of ventricular indices in hydrocephalic pediatric brain CT scan;Interdisciplinary Neurosurgery;2023-03

3. Modified distance regularized level set evolution for brain ventricles segmentation;Visual Computing for Industry, Biomedicine, and Art;2020-12

4. Implementation and evaluation of medical imaging techniques based on conformal geometric algebra;International Journal of Applied Mathematics and Computer Science;2020

5. Automated Ventricular System Segmentation in Paediatric Patients Treated for Hydrocephalus Using Deep Learning Methods;BioMed Research International;2019-07-07

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