Image enhancement techniques for highlighting brain tumors in magnetic resonance imaging
DOI:
https://doi.org/10.24933/rep.v8i1.359Keywords:
histogram equalization, contrast improvement, CLAHE, BCETAbstract
The technology integration across many fields of human knowledge, particularly the intersection of medicine and computer science, made possible the implementation and contribution to procedures related to medical imaging acquisition and medical treatments. This has resulted in the enhancement of medical practices and, consequently, in the overall health condition of patients. However, obtaining images, such as magnetic resonance imaging, unveils several challenges, including noise, distortions, and others images characteristics that can influence the medical assessment. In order to relieve these issues, this paper presents an application that receives magnetic resonance images containing patients with and without brain tumors. Subsequently, contrast enhancement methods such as CLAHE (Contrast Limited Adaptive Histogram Equalization), BCET (Balance contrast enhancement technique), and histogram equalization are applied. The advantages and disadvantages of each method are discussed, and the evaluation is explored through the interpretation of histograms from the resulting algorithm-generated images. In conclusion, it is found that CLAHE outperformed the other techniques, effectively highlighting tumors in the images and emphasizing other crucial regions for patient diagnosis.
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