A Review of Denoising Medical Images Using Machine Learning Approaches

Title:A Review of Denoising Medical Images Using Machine Learning Approaches

Volume: 14

Author(s): Prabhpreet Kaur*, Gurvinder Singh and Parminder Kaur

Affiliation:

• Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar,India

Keywords: Image denoising, ultrasound, filtering techniques, classifiers, wavelets, curvelets, data mining methods.

Abstract: Background: This paper attempts to identify suitable Machine Learning (ML) approach for image denoising of radiology based medical application. The Identification of ML approach is based on (i) Review of ML approach for denoising (ii) Review of suitable Medical Denoising approach.

Discussion: The review focuses on six application of radiology: Medical Ultrasound (US) for fetus development, US Computer Aided Diagnosis (CAD) and detection for breast, skin lesions, brain tumor MRI diagnosis, X-Ray for chest analysis, Breast cancer using MRI imaging. This survey identifies the ML approach with better accuracy for medical diagnosis by radiologists. The image denoising approaches further includes basic filtering techniques, wavelet medical denoising, curvelet and optimization techniques. In most of the applications, the machine learning performance is better than the conventional image denoising techniques. For fast and computational results the radiologists are using the machine learning methods on MRI, US, X-Ray and Skin lesion images. The characteristics and contributions of different ML approaches are considered in this paper.

Conclusion: The problem faced by the researchers during image denoising techniques and machine learning applications for clinical settings have also been discussed.

Cite this article as:

Kaur Prabhpreet *, Singh Gurvinder and Kaur Parminder, A Review of Denoising Medical Images Using Machine Learning Approaches, Current Medical Imaging 2018; 14 (5) . https://dx.doi.org/10.2174/1573405613666170428154156