title: Microscopic image analysis for quantitative characterization of muscle fiber type composition.
abstract: Skeletal muscles consist of muscle fibers that are responsible for contracting and generating force. Skeletal muscle fibers are categorized into distinct subtypes based on several characteristics such as contraction time, force production and resistance to fatigue. The composition of distinct muscle fibers in terms of their number and cross-sectional areas is characterized by a histological examination. However, manual delineation of individual muscle fibers from digitized muscle histology tissue sections is extremely time-consuming. In this study, we propose an automated image analysis system for quantitative characterization of muscle fiber type composition. The proposed system operates on digitized histological muscle tissue slides and consists of the following steps: segmentation of muscle fibers, registration of successive slides with distinct stains, and classification of muscle fibers into distinct subtypes. The performance of the proposed approach was tested on a dataset consisting of 25 image pairs of successive muscle histological cross-sections with different ATPase stain. Experimental results demonstrate a promising overall segmentation and classification accuracy of 89.1% in identifying muscle fibers of distinct subtypes.
authors.affiliation: Dept. of Biomedical Informatics, The Ohio State Univ., Columbus, OH, USA. osertel@bmi.osu.edu
authors.forename: Olcay
authors.initials: O
authors.lastname: Sertel
authors.forename: Belma
authors.initials: B
authors.lastname: Dogdas
authors.forename: Chi Sung
authors.initials: CS
authors.lastname: Chiu
authors.forename: Metin N
authors.initials: MN
authors.lastname: Gurcan
mesh_terms: D000465:Algorithms; D003952:Diagnostic Imaging; D006653:Histology; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D018656:Muscle Fibers, Fast-Twitch / Q000502:physiology*; D018657:Muscle Fibers, Slow-Twitch / Q000502:physiology*; D013194:Staining and Labeling