A study of image processing approach for Brain Tumor Detection | Original Article

ABSTRACT:

The aim of this study is to It is now possible to derive precise anatomical features of the brain from clinical information utilising MRI or CT scans, which have revolutionised the medical imaging sector (CT). Beyond these procedures, PET makes use of noninvasive techniques to glean useful details regarding a patient's health and function. Additionally, it aids in the planning of surgeries, radiation treatments and the navigation of intraoperative procedures. The use of radiotherapy as an example illustrates this point, as it delivers a precise dose of radiation to the tumour while minimising collateral harm to healthy muscle. Imaging processes are designed to provide evidence regarding a specific location in the image. The tissue is penetrated by the X-ray CT by the application of photon attenuation. When using MRI, it is possible to figure out how dense water really is. The subject's anatomy can be seen clearly in MRI and CT scan images, and the varying density of tissues in different parts of the body can be identified in this way. Medical imaging poses a particularly complex set of issues when attempting to make sense of the data because of the large variety of individual variances. Another important factor when performing medical image processing is the presence of noise due to noise measurement, modality artefacts, and fluctuations in the intrinsic intensity of the region of interest. As a result, medical image processing employs a variety of techniques to eliminate noise and enhance images in the most effective way possible. The type of noise and its intensity are critical considerations because there are so many different imaging systems that rely on a wide range of measurement instruments. In addition, noise effects may vary greatly in different regions of interest, creating in further difficulties in their discernment, which often computes variance across intensity values rather than intensity values.