Unsupervised band selection of medical hyperspectral images guided by data gravitation and weak correlation

BACKGROUND AND OBJECTIVE

Medical hyperspectral images (MHSIs) are used for a contact-free examination of patients without harmful radiation. However, high-dimensionality images contain large amounts of data that are sparsely distributed in a high-dimensional space, which leads to the "curse of dimensionality" (called Hughes' phenomenon) and increases the complexity and cost of data processing and storage. Hence, there is a need for spectral dimensionality reduction before the clinical application of MHSIs. Some dimensionality-reducing strategies have been proposed; however, they distort the data within MHSIs.

METHODS

To compress dimensionality without destroying the original data structure, we propose a method that involves data gravitation and weak correlation-based ranking (DGWCR) for removing bands of noise from MHSIs while clustering signal-containing bands. Band clustering is done by using the connection centre evolution (CCE) algorithm and selecting the most representative bands in each cluster based on the composite force. The bands within the clusters are ranked using the new entropy-containing matrix, and a global ranking of bands is obtained by applying an S-shaped strategy. The source code is available at https://www.github.com/zhangchenglong1116/DGWCR.

RESULTS

Upon feeding the reduced-dimensional images into various classifiers, the experimental results demonstrated that the small number of bands selected by the proposed DGWCR consistently achieved higher classification accuracy than the original data. Unlike other reference methods (e.g. the latest deep-learning-based strategies), DGWCR chooses the spectral bands with the least redundancy and greatest discrimination.

CONCLUSION

In this study, we present a method for efficient band selection for MHSIs that alleviates the "curse of dimensionality". Experiments were validated with three MHSIs in the human brain, and they outperformed several other band selection methods, demonstrating the clinical potential of DGWCR.

Reconstruction based on adaptive group least angle regression for fluorescence molecular tomography

Fluorescence molecular tomography can combine two-dimensional fluorescence imaging with anatomical information to reconstruct three-dimensional images of tumors. Reconstruction based on traditional regularization with tumor sparsity priors does not take into account that tumor cells form clusters, so it performs poorly when multiple light sources are used. Here we describe reconstruction based on an "adaptive group least angle regression elastic net" (AGLEN) method, in which local spatial structure correlation and group sparsity are integrated with elastic net regularization, followed by least angle regression. The AGLEN method works iteratively using the residual vector and a median smoothing strategy in order to adaptively obtain a robust local optimum. The method was verified using numerical simulations as well as imaging of mice bearing liver or melanoma tumors. AGLEN reconstruction performed better than state-of-the-art methods with different sizes of light sources at different distances from the sample and in the presence of Gaussian noise at 5-25%. In addition, AGLEN-based reconstruction accurately imaged tumor expression of cell death ligand-1, which can guide immunotherapy.

In vivo optical molecular imaging of inflammation and immunity

Inflammation is the phenotypic form of various diseases. Recent development in molecular imaging provides new insights into the diagnostic and therapeutic evaluation of different inflammatory diseases as well as diseases involving inflammation such as cancer. While conventional imaging techniques used in the clinical setting provide only indirect measures of inflammation such as increased perfusion and altered endothelial permeability, optical imaging is able to report molecular information on diseased tissue and cells. Optical imaging is a quick, noninvasive, nonionizing, and easy-to-use diagnostic technology which has been successfully applied for preclinical research. Further development of optical imaging technology such as optoacoustic imaging overcomes the limitations of mere fluorescence imaging, thereby enabling pilot clinical applications in humans. By means of endogenous and exogenous contrast agents, sites of inflammation can be accurately visualized in vivo. This allows for early disease detection and specific disease characterization, enabling more rapid and targeted therapeutic interventions. In this review, we summarize currently available optical imaging techniques used to detect inflammation, including optical coherence tomography (OCT), bioluminescence, fluorescence, optoacoustics, and Raman spectroscopy. We discuss advantages and disadvantages of the different in vivo imaging applications with a special focus on targeting inflammation including immune cell tracking.