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Improving Medical Image Preprocessing Using Denoising Technique
Image denoising is a main issue found in medical images and computer vision issues. There are different existing techniques in denoising image but the significant property of a decent image denoising model is that eliminate noise beyond what many would consider possible just as protect edges. Digital images accept a fundamental part both in step-by-step medical image applications, for instance, satellite TV, figured tomography. This method implemented for removing the noise from the lung cancer medical images with securing, transmission and gathering and capacity and recovery measures. This paper presents a preprocessing calculation which is named as Preprocessing Profuse Clustering Technique (PPCT) in light of the super pixel clustering. K-Means clustering, Simple Linear Iterative Clustering, Fusing Optimization algorithms are engaged with this proposed Preprocessing Profuse Clustering Technique and is additionally utilized for denoising the Lung Cancer images to get the more exact outcome in the dynamic interaction.
Preprocessing, K-Means, Preprocessing, Medical Image, Profuse Clustering Technique, Denoising
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