Active Galaxy (An Engineering Idea), Chennai
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AIM
 This work aims to propose a new method to segment and classify tissues present in chronic wounds
(granulation, necrosis and slough).
 Chronic wounds are ulcers with a difficult or nearly interrupted cicatrization process, like Pressure
Ulcers (PUs), Venous Leg Ulcers (VLUs) and Diabetic Foot Ulcers (DFUs) in humans.
 Chronic wounds increase the risk of complications in health and well-being of patients, like
amputation and infections, and affect an estimated 6.5 million people (2% of the population) in the
United State.
 Those wounds related to diabetes resulted in approximately 73,000 lower limb amputations in 2010.
 They costed $174 billions to the United States in 2007, where $116 billion were in direct costs and
$58.3 billion in indirect costs, such as loss of productivity, disability, and premature mortality.

Chronic wounds incidence tend to increase due to the aging population and more incidence of risk
factors such as diabetes and obesity.
MOTIVATION
 The main motivation for this work is its use in a smart system that will allow health
professionals to accurately compute the necrotic areas of an ulcer in order to
determine the optimal number of larvae needed to clean it.
 Our machine learning proposal should be able to:
 Segment wound area
 Classify wound tissues
 Calculate wound tissues areas in cm2
Objectives
 Proper application of Larval Therapy requires tissue area calculation in wound to
avoid waste of larvae or to use of less larvae than necessary.
 The visual exam of the wound is a simple and very used method, although highly
inaccurate.
 A somewhat more accurate alternative is the manual measurement of wounds height
and width, but with inconvenience of being invasive.
 As wounds usually cover irregular shapes and surfaces, this still does not have a
very good accuracy.
 Image processing and Computer Vision techniques can be a great aid to tissue area
calculation of wounds during Larval Therapy, with the potential of producing results
with better accuracies while using less health care professionals efforts and errors.
Comparing methods of
debridement
EXISITNG SYSTEM
 To implement wound bio-printing, an accurate measurement of wound surface
specifications is required.
 While contact measurement of wounds, e.g., ruler, wound tracing, and planimetry
methods, are still widely used, these methods are generally slow, inaccurate, and
often problematic.
 Thus, these limitations along with the need for high-speed methods for measuring
different soft materials such as human body tissues, including the skin, have led to
the emergence of noncontact fast measurement methods.
Proposed system
 The machine learning based Full-automatic image segmentation method gives
clinicians much higher control by providing more-efficient coordinates for
bioprinting.
 We used completely biocompatible materials in providing cell printing platforms.
 Alginate-gelatin hydrogel was synthesized by dissolving 16% (w/v) sodium-
alginate and 4% (w/v) gelatin in deionized water and, due to its desirable properties
such as biocompatibility, low cost and an easy gelation process, was used as the
biopolymer for cell encapsulation.
 The bioprinting results demonstrate 95.56% similarity between the bioprinted patch
dimensions and the desired wound geometries, which represents a good match and
overlap
Proposed system
REFERENCS
1) [1] K. Jung, S. Covington, C. Sen, M. Januszyk, R. Kirsner, G. Gurtner and N. Shah, “Rapid
identification of slow healing wounds”, Wound Rep and Reg, vol. 24, no. 1, pp. 181-188,
2016.
2) A. Dababneh and I. Ozbolat, “Bioprinting Technology: A Current Stateof-the-Art Review”, J.
of Manufacturing Science and Eng., vol. 136, no. 6, pp. 061016, 2014.
3) M. Bilgin and Ü. Güneş, “A Comparison of 3 Wound Measurement Techniques”, J. of Wound,
Ostomy and Continence Nursing, vol. 40, no. 6, pp. 590-593, 2013.
4) A. Shah, C. Wollak and J. Shah, “Wound Measurement Techniques: Comparing the Use of
Ruler Method, 2D Imaging and 3D Scanner”, J. of the American College of Clinical Wound
Specialists, vol. 5, no. 3, pp. 52-57, 2013.
5) J. Thatcher, J. Squiers, S. Kanick, D. King, Y. Lu, Y. Wang, R. Mohan, E. Sellke and J.
DiMaio, “Imaging Techniques for Clinical Burn Assessment with a Focus on Multispectral
Imaging”, Advances in Wound Care, 2016.