Adaptive medical image denoising using support vector regression

Authors:
Dinh Hoan Trinh;Marie Luong;Jean-Marie Rocchisani;Canh Duong Pham;Françoise Dibos
Affiliations:
LAGA, Université Paris, Villetaneuse, France;L2TI, Université Paris, Villetaneuse, France;Hôpital Avicenne, Bobigny - UFR SMBH, Université Paris, Villetaneuse, France;CIID, Vietnam Academy of Science and Technology, Hanoi, Vietnam;LAGA, Université Paris, Villetaneuse, France
Venue:
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part I
Year:
2011

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Abstract

Medical images are often corrupted by random noise due to various acquisitions, transmission, storage and display devices. Noise can seriously affect the quality of disease diagnosis or treatment. Image denosing is then a required task to ensure the quality of medical image analysis. In this paper, we propose a novel method for reducing some types of common noises in medical images by using a set of given standard images and a well-known machine learning technique namely the Support Vector Regression (SVR). Experimental results are carried out to demonstrate that our method can effectively denoise while preserving small details. A comparison is also performed to demonstrate the outperformance of the proposed technique in terms of both objective and subjective evaluations.