X-Noiseguard Intelligent Noise Reduction for High-Fidelity X-Ray Imaging

Author:

Harish G 1,Dr. H. Jayamangala 1

Affiliation:

1. Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamil Nadu

Abstract

The presence of noise in images produced by medical imaging equipment is common and unavoidable. Image noise can obscure and stimulate pathology, even sometimes to the extent of making them diagnostically unusable. To minimize noise in medical images, it is essential to comprehend the sources of noise and how they occur. In this project, we have reviewed different sources of noise that are present in images produced in radiography and tomography imaging techniques, the causes, effects and the various ways that are employed in their reduction. In order to completely eliminate noise in radiological imaging systems, we recommend that detectors that are free from noise should be designed and incorporated into future imaging systems. For segmentation and classification if we need a better result we need to target a preprocessing technique which involves removal of noise. For better PSNR we will be using Gaussian filtering technique for noise removal

Publisher

Naksh Solutions

Reference15 articles.

1. [1] SarvestanSoltani A, Safavi A A, Parandeh M N and Salehi M , “Predicting Breast Tumor Survivability using Data Mining Techniques”, IEEE 2019.

2. [2] Software Technology and Engineering (ICSTE), 2nd International Conference, Vol.2, pages 227-231,2019.

3. [3] Werner J C and Fogarty T C, “Genetic Programming Applied to Severe Diseases Diagnosis”, In Proceedings Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP), 2019.

4. [4] Iranpour M, Almassi S and Analoui M, “Breast Tumor Detection from fna using SVM and RBF Classifier”, In 1st Joint Congress on Fuzzy and Intelligent Systems, 2019.

5. [5] Joachims T, Scholkopf B, Burges C and Smola A, “Making large-scale SVM Learning Practical, Advances in Kernel Methods-Support Vector Learning”, Cambridge, MA, USA, 2019.

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