文献类型：期刊文献

英文题名：Adaptive Slices in Brain Haemorrhage Segmentation Based on the SLIC Algorithm

作者：Dawod, Ahmad Yahya[1];Phaphuangwittayakul, Aniwat[1];Ying, Fangli[2];Angkurawaranon, Salita[3]

机构：[1]Chiang Mai Univ, Int Coll Digital Innovat, Chiang Mai 50200, Thailand;[2]East China Univ Sci & Technol, Dept Comp Sci & Engn, Shanghai 200237, Peoples R China;[3]Chiang Mai Univ, Fac Med, Dept Radiol, Chiang Mai 50200, Thailand

年份：2021

卷号：29

期号：2

起止页码：795

外文期刊名：ENGINEERING LETTERS

收录：EI(收录号：20212210446575);WOS:【ESCI(收录号:WOS:000652486600050)】；

基金：This work was fully supported in part by International College of Digital Innovation (ICDI), Chiang Mai University, and Faculty of Medicine, Chiang Mai University, grant no. 143-2562.

语种：英文

外文关键词：SLIC algorithm; hybrid method; thresholding; region merging; segmentation

摘要：Traffic accidents have a significant impact on daily life, causing head injuries like skull fractures, brain damage, and so on. Many people fail to follow the safety regulations, such as riding a motorcycle without a helmet. The use of machine learning in brain haemorrhage research is extremely challenging since it involves the collection of patient data from computed tomography (CT) scan images. This study proposes a novel region-based segmentation approach for improving the accuracy and efficiency of CT automated 3D image processing in the analysis of brain injuries. It is quite challenging to create a highly efficient superpixel method which maintains a strategic distance from the segmentation and limited clusters of the pixels in respect to the intensity boundaries. The approach reduces computational costs, and the model achieves 97.79% accuracy in segmenting brain haemorrhage images. This study also guides the direction of future research in this domain.