Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Wound Segmentation using Image Segmentation and Artificial Neural Network


Affiliations
1 Department of Information Science & Engineering, JSS Academy of Technical Education, Bangalore, Karnataka, India
2 Department of Information Science & Engineering, JSS Academy of Technical Education, Bangalore, Karnataka, India
     

   Subscribe/Renew Journal


Chronic wounds are worldwide, present health challenge burdens a huge number of individuals. The effectual diagnoses and treatment of the wound mainly depend on clear-cut wound identification and wounded tissue measurement. but right now wound evaluation and the clinical process is completely done manually which is not accurate. From the standpoint of improving the final result in wound management and care, which delivers inefficient and economically viable medical treatment/practice, an automated computerized system for speedy and precise wound segmentation and wound identification is needed. The author created a system that leverages patient-provided wound images and performs automated picture segmentation and wound identification. Multilayer perceptron and Radial basis function are the two strategies employed.

Keywords

Artificial Neural Networks, Image Processing, Image Segmentation, Traditional Method.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Changhan Wang1 , Xinchen Yan , Max Smith , Kanika Kochhar , Marcie Rubin Stephen M. Warren , James Wrobel , and Honglak Lee1Darshan Tank,Akshai Aggarwal, Nirbhay Chaubey “A Unified Framework for Automatic Wound Segmentation and Analysis with Deep Convolutional Neural Networks” Feb 2019
  • Ihor Farmaha, Ukraine Marian Banaś , Poland Vasyl Savchyn, Bohdan Lukashchuk “Wound image segmentation using clustering based algorithms “- Aug 2019
  • Bo Song “An Automated Wound Identification System Based on Image Segmentation and Artificial Neural Networks “- June 2012
  • Fangzhao Li , Changjian Wang, Xiaohui Liu, Yuxing Peng , and Shiyao Jin, “A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks.”- May 2018
  • Prathima Guruprasad, “Overview of different thresholding methods in image processing” -June 2020
  • Muthukrishnan.R and M.Radha, “Edge detection techniques for image segmentation”-Dec 2011
  • Basheer and M. Hajmeer, "Artificial neural networks: fundamentals, computing, design, and application," Journal of microbiological methods, vol. 43, pp. 3-31, 2000.
  • K. Sen, G. M. Gordillo, S. Roy, R. Kirsner, L. Lambert, T. K. Hunt, F. Gottrup, G. C. Gurtner, and M. T. Longaker, "Human skin wounds: a major and snowballing threat to public health and the economy," Wound Repair Regen, vol. 17, pp. 763-71, NovDec 2009.

Abstract Views: 151

PDF Views: 0




  • Wound Segmentation using Image Segmentation and Artificial Neural Network

Abstract Views: 151  |  PDF Views: 0

Authors

S. Nagashree
Department of Information Science & Engineering, JSS Academy of Technical Education, Bangalore, Karnataka, India
M. Ayesha
Department of Information Science & Engineering, JSS Academy of Technical Education, Bangalore, Karnataka, India
D. Abhishek Kumar
Department of Information Science & Engineering, JSS Academy of Technical Education, Bangalore, Karnataka, India
D. Mohd Sameer
Department of Information Science & Engineering, JSS Academy of Technical Education, Bangalore, Karnataka, India
Syed Nasir Abbas
Department of Information Science & Engineering, JSS Academy of Technical Education, Bangalore, Karnataka, India

Abstract


Chronic wounds are worldwide, present health challenge burdens a huge number of individuals. The effectual diagnoses and treatment of the wound mainly depend on clear-cut wound identification and wounded tissue measurement. but right now wound evaluation and the clinical process is completely done manually which is not accurate. From the standpoint of improving the final result in wound management and care, which delivers inefficient and economically viable medical treatment/practice, an automated computerized system for speedy and precise wound segmentation and wound identification is needed. The author created a system that leverages patient-provided wound images and performs automated picture segmentation and wound identification. Multilayer perceptron and Radial basis function are the two strategies employed.

Keywords


Artificial Neural Networks, Image Processing, Image Segmentation, Traditional Method.

References