title: Medical image classification using spatial adjacent histogram based on adaptive local binary patterns.
abstract: Medical image recognition is an important task in both computer vision and computational biology. In the field of medical image classification, representing an image based on local binary patterns (LBP) descriptor has become popular. However, most existing LBP-based methods encode the binary patterns in a fixed neighborhood radius and ignore the spatial relationships among local patterns. The ignoring of the spatial relationships in the LBP will cause a poor performance in the process of capturing discriminative features for complex samples, such as medical images obtained by microscope. To address this problem, in this paper we propose a novel method to improve local binary patterns by assigning an adaptive neighborhood radius for each pixel. Based on these adaptive local binary patterns, we further propose a spatial adjacent histogram strategy to encode the micro-structures for image representation. An extensive set of evaluations are performed on four medical datasets which show that the proposed method significantly improves standard LBP and compares favorably with several other prevailing approaches.
authors.affiliation: Jilin University, College of Computer Science and Technology, Changchun 130012, China; Jilin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Liu
authors.affiliation: Jilin University, College of Computer Science and Technology, Changchun 130012, China; Jilin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China. Electronic address: wss@jlu.edu.cn.
authors.forename: Shengsheng
authors.initials: S
authors.lastname: Wang
authors.affiliation: Jilin University, College of Computer Science and Technology, Changchun 130012, China; Jilin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China.
authors.forename: Dezhi
authors.initials: D
authors.lastname: Huang
authors.affiliation: Renmin Hospital of Wuhan University, Department of Neurosurgery, Wuhan 430060, China.
authors.forename: Gang
authors.initials: G
authors.lastname: Deng
authors.affiliation: Jilin University, College of Computer Science and Technology, Changchun 130012, China; Jilin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of the Ministry of Education, Changchun 130012, China.
authors.forename: Fantao
authors.initials: F
authors.lastname: Zeng
authors.affiliation: College of Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China.
authors.forename: Huiling
authors.initials: H
authors.lastname: Chen
mesh_terms: D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008962:Models, Theoretical
keywords: Feature extraction; Image classification; Local binary patterns; Medical images; Microscope images