Design of Microwave-based Brain Tumor Detection Framework with the Development of Sparse and Low-Rank Compressive Sensing Image Reconstruction

Cancer is one of the leading causes of death, and the brain is one of the body’s cancer-prone organs.  The early detection of brain tumors can reduce cancer risk, which is practically assisted and conducted using scanners such as computed tomography (CT) and magnetic resonance imaging (MRI). However, those modalities are high-cost and large-sized, and they have a side effect risk to health. Alternatively, microwave imaging offers a novel cancer scanning method for early detection with low cost, small size and low health risk. Consequently, this research designs and creates a framework with a novel microwave image reconstruction algorithm inside. The framework is a component of the controller and image reconstructor for a portable microwave-based brain tumor detector that is open source and multi-platform. For the novel algorithm, this research proposes a CS-based imaging algorithm by exploiting the data‘s sparse and low-rank properties. The experiment shows that the proposed algorithm can give better qualitative and quantitative reconstruction results compared to a full-sampling-based as well as CS-based algorithm

A Nuclear Norm Based Matrix Regression Based Projections Method for Feature Extraction

© 2013 IEEE. In the traditional graph embedding framework, the graph is usually built by k-NN or r-ball. Since it is difficult to manually set the parameters k and r in the high-dimensional space, sparse representation-based methods are usually introduced to automatically build the graphs. In recent years, nuclear norm-based matrix regression (NMR) has been proposed for face recognition using the low rank structural information (i.e., the image matrix-based error model). Inspired by NMR, we give a NMR-based projections (NMRP) method for feature extraction and recognition. The experiments on FERET and extended Yale B face databases show that NMR can be used to build the graph while NMRP is an effective feature extraction method