title: Dual discriminative local coding for tissue aging analysis.
abstract: In aging research, morphological age of tissue helps to characterize the effects of aging on different individuals. While currently manual evaluations are used to estimate morphological ages under microscopy, such operation is difficult and subjective due to the complex visual characteristics of tissue images. In this paper, we propose an automated method to quantify morphological ages of tissues from microscopy images. We design a new sparse representation method, namely dual discriminative local coding (DDLC), that classifies the tissue images into different chronological ages. DDLC in- corporates discriminative distance learning and dual-level local coding into the basis model of locality-constrained linear coding thus achieves higher discriminative capability. The morphological age is then computed based on the classification scores. We conducted our study using the publicly avail- able terminal bulb aging database that has been commonly used in existing microscopy imaging research. To represent these images, we also design a highly descriptive descriptor that combines several complementary texture features extracted at two scales. Experimental results show that our method achieves significant improvement in age classification when compared to the existing approaches and other popular classifiers. We also present promising results in quantification of morphological ages.
authors.affiliation: School of Information Technologies, University of Sydney, Australia. Electronic address: yson1723@uni.sydney.edu.au.
authors.forename: Yang
authors.initials: Y
authors.lastname: Song
authors.affiliation: School of Information Technologies, University of Sydney, Australia.
authors.forename: Qing
authors.initials: Q
authors.lastname: Li
authors.affiliation: School of Information Technologies, University of Sydney, Australia.
authors.forename: Fan
authors.initials: F
authors.lastname: Zhang
authors.affiliation: Department of Computer Science and Engineering, University of Texas at Arlington, USA.
authors.forename: Heng
authors.initials: H
authors.lastname: Huang
authors.affiliation: School of Information Technologies, University of Sydney, Australia; Med-X Research Institute, Shanghai Jiaotong University, China.
authors.forename: Dagan
authors.initials: D
authors.lastname: Feng
authors.affiliation: Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, USA.
authors.forename: Yue
authors.initials: Y
authors.lastname: Wang
authors.affiliation: Computer Engineering Department, University of Albany State University of New York, USA; Robotics Institute, Carnegie Mellon University, USA.
authors.forename: Mei
authors.initials: M
authors.lastname: Chen
authors.affiliation: School of Information Technologies, University of Sydney, Australia.
authors.forename: Weidong
authors.initials: W
authors.lastname: Cai
mesh_terms: D000375:Aging; D000465:Algorithms; D000818:Animals; D017173:Caenorhabditis elegans / Q000033:anatomy & histology / Q000502:physiology; D016208:Databases, Factual; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*
keywords: Microscopy image; Sparse representation; Tissue age