Medical Image Retrieval Based on An Improved Non-negative Matrix Factorization Algorithm
J. Wang, S.Y. Lai, M.D. Li, D. H. Lies
Journal of Digital Information Management, Vol. 13, No. 6, 2015.
DOI: 
Link: http://dline.info/fpaper/jdim/v13i6/v13i6_3.pdf
Abstract:
A gradient-projected relevance feedback algorithm
based on non-negative matrix factorization (NMF)
is proposed in this study to improve the performance of
retrieval algorithm in the medical image processing field.
Relevance feedback has been an important method in
image retrieval technology in recent years because it allows
users to participate. Thus, it can compensate for
the shortcomings of using low-level features to describe
the semantic contents of an image to some degree. Given
that NMF can partly sketch the distribution of relevant
images in the space represented by the base matrix, finding
more related images from image repositories is possible.
This condition can be achieved by conducting an
NMF operation of the query image, using the gradient projection
iterative rules to update variables, and selecting
the appropriate iteration stop conditions to optimize the
time complexity of the algorithm. Compared with the commonly
used and multiplicative updating NMF approaches,
the proposed method improved the speed of the feedback
on the premise of guaranteeing precision and recall
rates, and significantly optimized the retrieval accuracy.
Experiments were conducted on the base of 586 cerebral
hemorrhage images and 634 spine and cervical-spine
mixed images. Results show that the proposed approach
is feasible in medical image retrieval.