Document
Generically Dependent Continuant
RDF description of Extended Blind End-Member and Abundance Extraction for Biomedical Imaging Applications - http://orbis.uaslp.mx/vivo/individual/scopus6077
Article
Information Content Entity
In some applications of biomedical imaging, a linear mixture model can represent the constitutive elements (end-members) and their contributions (abundances) per pixel of the image. In this work, the extended blind end-member and abundance extraction (EBEAE) methodology is mathematically formulated to address the blind linear unmixing (BLU) problem subject to positivity constraints in optical measurements. The EBEAE algorithm is based on a constrained quadratic optimization and an alternated least-squares strategy to jointly estimate end-members and their abundances. In our proposal, a local approach is used to estimate the abundances of each end-member by maximizing their entropy, and a global technique is adopted to iteratively identify the end-members by reducing the similarity among them. All the cost functions are normalized, and four initialization approaches are suggested for the end-members matrix. Synthetic datasets are used first for the EBEAE validation at different noise types and levels, and its performance is compared to state-of-the-art algorithms in BLU. In a second stage, three experimental biomedical imaging applications are addressed with EBEAE: M-FLIM for chemometric analysis in oral cavity samples, OCT for macrophages identification in post-mortem artery samples, and hyper-spectral images for in-vivo brain tissue classification and tumor identification. In our evaluations, EBEAE was able to provide a quantitative analysis of the samples with none or minimal a priori information. © 2019 IEEE.
Extended Blind End-Member and Abundance Extraction for Biomedical Imaging Applications
Blind linear unmixing; constrained optimization; fluorescence lifetime imaging microscopy; hyperspectral imaging; optical coherence tomography Blind source separation; Constrained optimization; Cost functions; Extraction; Fluorescence imaging; Image segmentation; Iterative methods; Medical imaging; Optical data processing; Optical tomography; Quadratic programming; Spectroscopy; Biomedical imaging applications; Brain tissue classifications; Constrained quadratic optimization; Fluorescence lifetime imaging microscopy; Hyper-spectral images; Linear mixture modeling; Linear unmixing; State-of-the-art algorithms; Hyperspectral imaging
Consejo Nacional de Ciencia y Tecnología, CONACYT: 254637
Agencia Canaria de Investigación, Innovación y Sociedad de la Información, ACIISI: ProID2017010164