  Sparse learning has been shown to be effective in solving many real-world
problems. Finding sparse representations is a fundamentally important topic in
many fields of science including signal processing, computer vision, genome
study and medical imaging. One important issue in applying sparse
representation is to find the basis to represent the data,especially in
computer vision and medical imaging where the data is not necessary incoherent.
In medical imaging, clinicians often grade the severity or measure the risk
score of a disease based on images. This process is referred to as medical
image grading. Manual grading of the disease severity or risk score is often
used. However, it is tedious, subjective and expensive. Sparse learning has
been used for automatic grading of medical images for different diseases. In
the grading, we usually begin with one step to find a sparse representation of
the testing image using a set of reference images or atoms from the dictionary.
Then in the second step, the selected atoms are used as references to compute
the grades of the testing images. Since the two steps are conducted
sequentially, the objective function in the first step is not necessarily
optimized for the second step. In this paper, we propose a novel sparse
range-constrained learning(SRCL)algorithm for medical image grading.Different
from most of existing sparse learning algorithms, SRCL integrates the objective
of finding a sparse representation and that of grading the image into one
function. It aims to find a sparse representation of the testing image based on
atoms that are most similar in both the data or feature representation and the
medical grading scores. We apply the new proposed SRCL to CDR computation and
cataract grading. Experimental results show that the proposed method is able to
improve the accuracy in cup-to-disc ratio computation and cataract grading.
