Human Skin Segmentation Improved by Texture Energy Under Superpixels

2015 ◽  
pp. 35-42 ◽  
Author(s):  
Anderson Santos ◽  
Helio Pedrini
Keyword(s):  
Human Skin ◽  
Skin Segmentation

Burnt Human Skin Segmentation and Depth Classification Using Deep Convolutional Neural Network (DCNN)

2020 ◽  
Vol 10 (10) ◽  
pp. 2421-2429
Author(s):  
Fakhri Alam Khan ◽  
Ateeq Ur Rehman Butt ◽  
Muhammad Asif ◽  
Hanan Aljuaid ◽  
Awais Adnan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Human Skin ◽  
Low Income ◽  
Deep Convolutional Neural Network ◽  
Image Feature ◽  
World Health ◽  
Skin Segmentation ◽  
Average Accuracy ◽  
Research Task

World Health Organization (WHO) manage health-related statistics all around the world by taking the necessary measures. What could be better for health and what may be the leading causes of deaths, all these statistics are well organized by WHO. Burn Injuries are mostly viewed in middle and low-income countries due to lack of resources, the result may come in the form of deaths by serious injuries caused by burning. Due to the non-accessibility of specialists and burn surgeons, simple and basic health care units situated at tribble areas as well as in small cities are facing the problem to diagnose the burn depths accurately. The primary goals and objectives of this research task are to segment the burnt region of skin from the normal skin and to diagnose the burn depths as per the level of burn. The dataset contains the 600 images of burnt patients and has been taken in a real-time environment from the Allied Burn and Reconstructive Surgery Unit (ABRSU) Faisalabad, Pakistan. Burnt human skin segmentation was carried by the use of Otsu's method and the image feature vector was obtained by using statistical calculations such as mean and median. A classifier Deep Convolutional Neural Network based on deep learning was used to classify the burnt human skin as per the level of burn into different depths. Almost 60 percent of images have been taken to train the classifier and the rest of the 40 percent burnt skin images were used to estimate the average accuracy of the classifier. The average accuracy of the DCNN classifier was noted as 83.4 percent and these are the best results yet. By the obtained results of this research task, young physicians and practitioners may be able to diagnose the burn depths and start the proper medication.

A Hybrid Cascade Approach for Human Skin Segmentation

2016 ◽  
Vol 17 (6) ◽  
pp. 1-14 ◽  
Author(s):  
Samy Bakheet ◽  
Ayoub Al-Hamadi
Keyword(s):  
Human Skin ◽  
Skin Segmentation

Using graph cuts in GPUs for color based human skin segmentation

2011 ◽  
Vol 18 (1) ◽  
pp. 41-59 ◽  
Author(s):  
Lucas Lattari ◽  
Anselmo Montenegro ◽  
Aura Conci ◽  
Esteban Clua ◽  
Virginia Mota ◽  
...  
Keyword(s):  
Human Skin ◽  
Graph Cuts ◽  
Skin Segmentation

In Vitro Human Skin Segmentation and Drug Concentration–Skin Depth Profiles

2011 ◽  
pp. 33-50 ◽  
Author(s):  
Ana Melero ◽  
Tsambika Hahn ◽  
Ulrich F. Schaefer ◽  
Marc Schneider
Keyword(s):  
Human Skin ◽  
Drug Concentration ◽  
Skin Depth ◽  
Skin Segmentation ◽  
Depth Profiles
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