Abstract
Cross-sectional imaging approaches play a key role in assessing bleeding brain injuries. Doctors commonly determine bleeding size and severity in CT and MRI. Separating and identifying artifacts is extremely important in processing medical images. Image and signal processing are used to classify tissues within images closely linked to edges. In CT images, a subjective process takes a stroke ‘s manual contour with less precision. This chapter presents the application of both image and signal processing techniques in the characterization of Brain Stroke field. This chapter also summarizes how to characterize the brain stroke using different image processing algorithms such as ROI based segmentation and watershed methods.
Highlights
A medical condition called a stroke is cell death associated with weak blood flow into the brain
Because there are treatments that can reduce the severity of stroke symptoms if given early enough, many people prefer the more urgent wording “stroke” or “acute ischemic stroke (AIS)” over the less urgent “brain attack” and “acute ischemic stroke” [12–19]
These two types of strokes, Ischemic and Hemorrhagic, have different properties. They are caused by a stroke, and the damage that stroke brings to brain
Summary
A medical condition called a stroke is cell death associated with weak blood flow into the brain. It is an ischemic stroke caused by insanity caused by blood flow, and it is a bleeding that in two different forms causes hemorrhagic stroke. Both have helped to prevent the proper functioning of brain sections [1–7]. One of the most common types of stroke is ischemia which causes a loss of brain functions because of severe lack of blood supply [8–11]. Biomedical Signal and Image Processing and blood tests [34–37]. In the 1990s, medical image processing technologies were used to control heart rates during coronary artery bypass grafts [17, 24]
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