Computer-aided medical image analysis has been widely used in clinics to facilitate objective disease diagnosis. This facilitation, however, is often qualitative instead of quantitative due to the analysis challenges associated with medical images such as low signal-to-noise ratio, signal dropout, and large variations. Consequently, physicians have to rely on their personal experiences to make diagnostic decisions, which in turn is expertise-dependent and prone to individual bias.
Recently, low-rank modeling based approaches have achieved great success in natural image analysis. There is a trend that low-rank modeling will find its applications in medical image analysis. In this review paper, we like to review the recent progresses along this direction. Concretely, we will first explain the mathematical background of low-rank modeling, categorize existing low-rank modeling approaches and their applications in natural image analysis. After that, we will illustrate some application examples of using low-rank modeling in medical image analysis. Finally, we will discuss some possibilities of developing more robust analysis methods to better analyze cardiac images.