Binomial classification based on DLENE features in sparse representation: Application in kidney detection in 3D ultrasound

Mahdi Marsousi, Konstantinos N. Plataniotis

Abstract: Sparse representation-based classification (SRC) has been recently attracted a great interest among the signal processing society. SRC applies a discriminative representation using training samples to separate signals into their classes. In existing SRC methods, the dictionary size, which highly affects the performance, is manually set. Moreover, they are linear classifiers, and thus, they are not suitable for classifying nonlinear problems. In this paper, we propose a new classification method by cascading a dictionary learning and the neural network to take the advantages of both methods. We use dictionary learning with efficient number of elements (DLENE) to extract discriminative features. We also use the proposed binomial classifier to detect kidneys in 3D ultrasound images. A set of Caltech-101 images are used to compare the proposed method with the state-of-the-art. The proposed kidney detection is evaluated by a set of ultrasound volumes. The results confirm the superiority of our proposed method.