赵文达   

Associate Professor
Supervisor of Master's Candidates

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Language:English

Paper Publications

Title of Paper:Medical Image Fusion and Denoising with Alternating Sequential Filter and Adaptive Fractional Order Total Variation

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First Author:Zhao, Wenda

Correspondence Author:Zhao, WD (reprint author), Dalian Univ Technol, Sch Informat & Commun Engn, Dalian 116024, Peoples R China.

Co-authors:Lu, Huchuan

Date of Publication:2017-09-01

Journal:IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT

Included Journals:SCIE、EI、Scopus

Volume:66

Issue:9

Page Number:2283-2294

ISSN No.:0018-9456

Key Words:Adaptive fractional order total variation (AFOTV); medical image fusion and denoising; multiscale alternating sequential filter; variation model

Abstract:Medical image fusion aims at integrating information from multimodality medical images to obtain a more complete and accurate description of the same object, which provides an easy access for image-guided medical diagnostic and treatment. Unfortunately, medical images are often corrupted by noise in acquisition or transmission, and the noise signal is easily mistaken for a useful characterization of the image, making the fusion effect drop significantly. Thus, the existence of noise presents a great challenge for most of traditional image fusion methods. To address this problem, an effective variation model for multimodality medical image fusion and denoising is proposed. First, a multiscale alternating sequential filter is exploited to extract the useful characterizations (e. g., details and edges) from noisy input medical images. Then, a recursive filtering-based weight map is constructed to guide the fusion of main features of input images. Additionally, total variation (TV) constraint is developed by constructing an adaptive fractional order p based on the local contrast of fused image, further effectively suppressing noise while avoiding the staircase effect of the TV. The experimental results indicate that the proposed method performs well with both noisy and normal medical images, outperforming conventional methods in terms of fusion quality and noise reduction.

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