1. Classification of anomalous region in human cerebrovascular structure using random forest
Authors : Pranati Rakshit, Mita Nasipuri, Nirmal Das, Subhadip Basu
Pages : 15-21
DOI : http://dx.doi.org/10.21172/1.93.03
Keywords : Aneurysm, hemodynamic analysis, cerebrovascular phantoms, wall shear stress, classification, feature, rupture risk. Abstract :To predict and assess the cerebrovascular diseases anomaly detection in cerebrovasculature is a crucial job. Classification of anomalous part in human cerebrovascular structure which will lead to several vascular disease, can help to detect or predict the same. Aneurysm is one of the vascular disease which causes a number of deaths worldwide and so it is very crucial for clinicians to predict or assess it. It is reasonable to assume that rupture risk assessment can be improved by incorporating hemodynamic analysis as it is commonly thought to play an important role in the mechanisms of development, progression, and rupture of aneurysm. Hemodynamic parameter like wall shear stress(wss), velocity, static pressure information are to be developed by digital flow based model. So the comparison of the hemodynamic parameters on several cerebrovascular structures with and without aneurysm and to find some features from that for pattern classification, carries a significant role in prediction of occurrence of aneurysm and subsequently rupture risk of the same.
Citing this Journal Article :Pranati Rakshit, Mita Nasipuri, Nirmal Das, Subhadip Basu, "Classification of anomalous region in human cerebrovascular structure using random forest ", Volume 9 Issue 3 - January 2018, 15-21
Click here to Submit Copyright Takedown Notice for this article.