Purdue University Graduate School
Masters_Thesis_DanielaChanci_Final.pdf (8.45 MB)


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posted on 2021-07-20, 19:27 authored by Daniela Chanci ArrublaDaniela Chanci Arrubla

Accurate assessment of burn injuries is critical for the correct management of such wounds. Depending on the total body surface area affected by the burn, and the severity of the injury, the optimal treatment and the surgical requirements are selected. However, such assessment is considered a clinical challenge. In this thesis, to address this challenge, an automatic framework to segment the burn using RGB images, and classify the injury based on the severity using ultrasound images is proposed and implemented. With the use this framework, the conventional assessment approach, which relies exclusively on a physical and visual examination of the injury performed by medical practitioners, could be complemented and supported, yielding accurate results. The ultrasound data enables the assessment of internal structures of the body, which can provide complementary and useful information. It is a noninvasive imaging modality that provides access to internal body structures that are not visible during the typical physical examination of the burn. The semantic segmentation module of the proposed approach was evaluated through one experiment. Similarly, the classification module was evaluated through two experiments. The second experiment assessed the effects of incorporating texture features as extra features for the classification task. Experimental results and evaluation metrics demonstrated the satisfactory results obtained with the proposed framework for the segmentation and classification problem. Therefore, this work acts as a first step towards the creation of a Computer-Aided Diagnosis and Detection system for burn injury assessment.


Degree Type

  • Master of Science


  • Industrial Engineering

Campus location

  • West Lafayette

Advisor/Supervisor/Committee Chair

Juan Wachs

Additional Committee Member 2

Ramses Martinez

Additional Committee Member 3

Chandan Sen