title: Image analysis of chronic wounds for determining the surface area.
abstract: Progress in wound healing is primarily quantified by the rate of change of the wound's surface area. The most recent guidelines of the Wound Healing Society suggest that a reduction in wound size of <40% within 4 weeks necessitates a reevaluation of the treatment. However, accurate measurement of wound size is challenging due to the complexity of a chronic wound, the variable lighting conditions of examination rooms, and the time constraints of a busy clinical practice. In this paper, we present our methodology to quantify a wound boundary and measure the enclosed wound area reproducibly. The method derives from a combination of color-based image analysis algorithms, and our results are validated with wounds in animal models and human wounds of diverse patients. Images were taken by an inexpensive digital camera under variable lighting conditions. Approximately 100 patient images and 50 animal images were analyzed and a high overlap was achieved between the manual tracings and the calculated wound area by our method in both groups. The simplicity of our method combined with its robustness suggests that it can be a valuable tool in clinical wound evaluations. The basic challenge of our method is in deep wounds with very small surface areas where color-based detection can lead to erroneous results and which could be overcome by texture-based detection methods. The authors are willing to provide the developed MATLAB code for the work discussed in this paper.
authors.affiliation: School of Biomedical Engineering, Science and Health Systems, Drexel University, Philadelphia, Pennsylvania 19104, USA. esp25@drexel.edu
authors.forename: Elisabeth S
authors.initials: ES
authors.lastname: Papazoglou
authors.forename: Leonid
authors.initials: L
authors.lastname: Zubkov
authors.forename: Xiang
authors.initials: X
authors.lastname: Mao
authors.forename: Michael
authors.initials: M
authors.lastname: Neidrauer
authors.forename: Nicolas
authors.initials: N
authors.lastname: Rannou
authors.forename: Michael S
authors.initials: MS
authors.lastname: Weingarten
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D000818:Animals; D001830:Body Surface Area; D002908:Chronic Disease; D003116:Color; D003921:Diabetes Mellitus, Experimental / Q000473:pathology; D017719:Diabetic Foot / Q000473:pathology* / Q000628:therapy; D004195:Disease Models, Animal; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010781:Photography / Q000379:methods*; D051381:Rats; D017207:Rats, Sprague-Dawley; D012680:Sensitivity and Specificity; D013997:Time Factors; D016896:Treatment Outcome; D014945:Wound Healing