Classification of Medical Image using Sparse Representation Based on Fisher Discrimination

In today’s world, there is a dire need for the appropriate use of technology to diagnose and treat patients by analyzing medical data, which is usually in the form of images. This need calls for an in-depth research in the field of data. Medical images play a crucial role in treating patients and the use of image processing tools can enhance the detection accuracy. Sparse representation-based methods have performed well in machine vision and image processing. In this paper, an improved method to classify medical images is discussed. The classification scheme associated with the Fisher discrimination dictionary learning (FDDL) model is consequently presented by exploiting the discriminative information in both the representation residual and the representation coefficients. This paper also uses image pre-processing techniques to select the best representative features to classify an image and to avoid the curse of dimensionality. The main challenge in designing a proper classifier to detect medical images is modeling the data subspace and classification based on the presented model. These experiments were evaluated on leukemia and BRATS dataset. Experimental results showed the classification performance obtained ۹۷.۸% and ۹۸.۸۹%.

Keywords:

Sparse Representation , automatic diagnosis , medical image classification , Fisher Discrimination