Open AccessCT brain image advancement for ICH diagnosis
Author(s) -
Shaik Amir Nor Shahirah,
Kang Law Zhe,
Mukari Shahizon Azura,
Sahathevan Ramesh,
Chellappan Kalaivani
Publication year - 2020
Publication title -
healthcare technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.45
H-Index - 19
ISSN - 2053-3713
DOI - 10.1049/htl.2018.5003
Subject(s) - unsharp masking , artificial intelligence , computer science , wiener filter , pattern recognition (psychology) , computer vision , noise reduction , image quality , sharpening , mean squared error , peak signal to noise ratio , pixel , image processing , image (mathematics) , mathematics , statistics
A critical step in detection of primary intracerebral haemorrhage (ICH) is an accurate assessment of computed tomography (CT) brain images. The correct diagnosis relies on imaging modality and quality of acquired images. The authors present an enhancement algorithm which can improve the clarity of edges on CT images. About 40 samples of CT brain images with final diagnosis of primary ICH were obtained from the UKM Medical Centre in Digital Imaging and Communication in Medicine format. The images resized from 512 × 512 to 256 × 256 pixel resolution to reduce processing time. This Letter comprises of two main sections; the first is denoising using Wiener filter, non‐local means and wavelet; the second section focuses on image enhancement using a modified unsharp masking (UM) algorithm to improve the visualisation of ICH. The combined approach of Wiener filter and modified UM algorithm outperforms other combinations with average values of mean square error, peak signal‐to‐noise ratio, variance and structural similarity index of 2.89, 31.72, 0.12 and 0.98, respectively. The reliability of proposed algorithm was evaluated by three blinded assessors which achieved a median score of 65%. This approach provides reliable validation for the proposed algorithm which has potential in improving image analysis.