A novel algorithm for the detection of cerebral aneurysm using sub-band morphological operation

Abstract

Junctions or portions of blood vessels may sometimes develop weak spots because of certain factors like high blood pressure, head trauma, and infection. With time, these weak spots result in bulging or ballooning out or filling with blood creating some sac-like structures known as aneurysm. These structures can burst by leaking blood into the nearby tissues as a result of artery spam known as the subarachnoid hemorrhage, which leads to potential stroke and even death. In this regard, patients’ outcome is treated before the ruptured detection during the setting up of the aneurysm process. It is considered as a much better approach than the treatment after the aneurysm process. Neurosurgeons prefer two treatment options that are clipping and coiling, which demand sufficient detection of cerebral aneurysm. To facilitate expert neurosurgeons, we propose a novel method to determine an automatic cerebral aneurysm in the domain of digital subtraction angiography. It is processed by using the sub-band morphological operation in the wavelet domain. Simulation results have demonstrated the proposed method in order to prove efficiency in terms of accurate detection with impartial location, definite size, and firm type of aneurysm.