Cerebral aneurysms (CA) are pathological dilations of a vessel wall which if not treated at proper time are liable to rupture causing spontaneous intracranial bleeding. Because of the critical location of aneurysms, such bleeding causes subarachnoid haemorrhage and may result in death or severe morbidity to the individual. Therefore, proper detection of CA has got a crucial clinical significance that might be useful in its prognosis and treatment trials. Till date Digital Substraction Angiography(DSA), considered as a gold standard for the diagnosis of CA. In this book two novel methods have been implemented for successful detection and classification of CA from DSA. Firstly, Thresholding Spatial Filtering Thresholding (TSFT) which is a lightweight algorithm applicable for real-time processing of CA images. Secondly, Modified Hough Circle Transform & Peak Trekking (MHCT-PT) which is a heavyweight algorithm requiring off-line processing of CA images to detect and classify aneurysms with higher accuracy.