title: Computerized segmentation and measurement of chronic wound images.
abstract: An estimated 6.5 million patients in the United States are affected by chronic wounds, with more than US$25 billion and countless hours spent annually for all aspects of chronic wound care. There is a need for an intelligent software tool to analyze wound images, characterize wound tissue composition, measure wound size, and monitor changes in wound in between visits. Performed manually, this process is very time-consuming and subject to intra- and inter-reader variability. In this work, our objective is to develop methods to segment, measure and characterize clinically presented chronic wounds from photographic images. The first step of our method is to generate a Red-Yellow-Black-White (RYKW) probability map, which then guides the segmentation process using either optimal thresholding or region growing. The red, yellow and black probability maps are designed to handle the granulation, slough and eschar tissues, respectively; while the white probability map is to detect the white label card for measurement calibration purposes. The innovative aspects of this work include defining a four-dimensional probability map specific to wound characteristics, a computationally efficient method to segment wound images utilizing the probability map, and auto-calibration of wound measurements using the content of the image. These methods were applied to 80 wound images, captured in a clinical setting at the Ohio State University Comprehensive Wound Center, with the ground truth independently generated by the consensus of at least two clinicians. While the mean inter-reader agreement between the readers varied between 67.4% and 84.3%, the computer achieved an average accuracy of 75.1%.
authors.affiliation: Faculty of Engineering, Multimedia University, Cyberjaya, Selangor, Malaysia. Electronic address: faizal1@mmu.edu.my.
authors.forename: Mohammad Faizal
authors.initials: MF
authors.lastname: Ahmad Fauzi
authors.affiliation: Department of Plastic Surgery, The Ohio State University, Wexner Medical Center, Columbus, OH, USA.
authors.forename: Ibrahim
authors.initials: I
authors.lastname: Khansa
authors.affiliation: Department of Internal Medicine, The Ohio State University, Wexner Medical Center, Columbus, OH, USA.
authors.forename: Karen
authors.initials: K
authors.lastname: Catignani
authors.affiliation: Department of Plastic Surgery, The Ohio State University, Wexner Medical Center, Columbus, OH, USA; OSU Comprehensive Wound Center, Center for Regenerative Medicine and Cell-Based Therapies, The Ohio State University, Wexner Medical Center, Columbus, OH, USA.
authors.forename: Gayle
authors.initials: G
authors.lastname: Gordillo
authors.affiliation: Department of Surgery, The Ohio State University, Wexner Medical Center, Columbus, OH, USA; OSU Comprehensive Wound Center, Center for Regenerative Medicine and Cell-Based Therapies, The Ohio State University, Wexner Medical Center, Columbus, OH, USA.
authors.forename: Chandan K
authors.initials: CK
authors.lastname: Sen
authors.affiliation: Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
authors.forename: Metin N
authors.initials: MN
authors.lastname: Gurcan
mesh_terms: D000465:Algorithms; D003201:Computers; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008490:Medical Informatics / Q000379:methods; D015233:Models, Statistical; D015588:Observer Variation; D010781:Photography; D011336:Probability; D015203:Reproducibility of Results; D012984:Software; D014947:Wounds and Injuries / Q000473:pathology*
keywords: Chronic wound; Probability map; Wound analysis; Wound measurement; Wound segmentation