Instrumentation & Measurement Magazine 24-2 - 45

Recent Advances in Sparse
Representation Based
Medical Image Fusion
Yu Liu, Xun Chen, Aiping Liu, Rabab K. Ward, and Z. Jane Wang

M

edical image fusion, which aims to combine
multi-source information captured by different imaging modalities, is of great significance
to medical professionals for precise diagnosis and treatment.
In the last decade, sparse representation (SR)-based approach
has emerged as a very active direction in the field of medical
image fusion, due to its powerful ability for image representation. In this paper, we mainly present an overview of the recent
advances achieved in SR-based medical image fusion, ranging
from the conventional local and single-component SR-based
methods to the latest global and multi-component SR-based
methods. In addition, several major challenges remained in
this direction are presented and some future prospects are
discussed.

Introduction to Medical Image Fusion
Medical imaging plays an increasingly important role in routine clinical diagnosis and treatment. It is known that medical
images from different modalities have different characteristics and capture different tissue information. For example,
computed tomography (CT) images can provide precise positioning for bones and implants, while magnetic resonance
(MR) images are good at revealing the structural information of soft tissue with high resolution. In nuclear medicine,
the functional imaging modalities like single-photon emission computed tomography (SPECT) and positron emission
tomography (PET) can depict the information about the metabolism of organisms, albeit with low spatial resolution. An
example of multimodality brain images from the same subject, including CT, T2-weighted MR and SPECT modalities
is shown in Fig. 1. In many application scenarios, the information provided by a single imaging modality may not be
sufficient, and physicians would typically require images from
different modalities for accurate decision making. However,

analyzing multiple images separately in a manual manner is
generally practice-inconvenient.
Medical image fusion, which aims to combine the complementary information preserved in multimodality medical
images by generating a composite image, provides an effective solution to the above problem. The composite image is
expected to be more suitable for physician observation or
machine perception [2]. Medical image fusion has been
demonstrated to be a promising technique in many clinical applications, e.g., MR and PET fusion for liver metastasis
detection [3], MR and SPECT fusion for brain functional abnormality localization [4], CT and MR fusion for virtual surgical
planning [5], multimodality MR fusion for brain tissue segmentation [6], etc. In recent years, medical image fusion has
emerged as a very active research topic in a broad range of
fields including Instrumentation and Measurement (I&M)
with a number of advanced methods being proposed [7]-[12].
Fig. 2 shows a typical schematic diagram of medical image
fusion methods [12]. It consists of three main stages: decomposition, fusion and reconstruction. The input multiple
source images are firstly converted into a transform domain
by employing an image decomposition approach. Then, the
decomposed coefficients are merged by a designed fusion

Fig. 1. An example of multimodality brain images (these images are from the
Whole Brain Atlas database [1]).

This research was supported by the National Natural Science Foundation of China (Grants 61701160, 61922075 and 61701158),
the Fundamental Research Funds for the Central Universities (Grant JZ2020HGPA0111) and the Key Research and
Development Program of Anhui Province (Grant 1804h08020244).
April 2021	

IEEE Instrumentation & Measurement Magazine	45
1094-6969/21/$25.00©2021IEEE



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