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Massachusetts Institute of Technology

LesionAir : a low-cost tool for automated skin cancer diagnosis and mapping

Abstract

dc:description.abstract

Skin cancer is the most common form of cancer in the United States; one out of every five Americans develops skin cancer at some point in their lifetime. Diagnosing cancerous lesions early is critical as it significantly increases the chance of survival. However, current techniques for diagnosing skin cancer lack specificity and sensitivity, resulting in many unnecessary biopsies and missed diagnoses. Although some researchers have increased diagnostic efficacy by quantitatively diagnosing skin cancer in an automated fashion, these methods require extremely bulky, expensive, and complicated equipment. This thesis presents the design and testing of LesionAir, a small, low-cost skin cancer diagnostic tool that measures the full-field compliance of the skin - which is well known to correlate strongly with skin cancer - by applying a vacuum force to the tissue and measuring precise deflection using structured light 3D reconstruction. Image processing algorithms determine additional morphological information about the potentially cancerous lesion. A pilot study of ten patients with suspect lesions validated LesionAir's effectiveness. After biopsy and analysis, a dermatopathologist confirmed the diagnosis of skin cancer in tissue that LesionAir identified as noticeably stiffer, and the regions of this stiffened tissue aligned nearly perfectly with the bounds established by the histological tests, which showed the method determines the precise coordinates that must be excised to safely remove all cancerous tissue. This technology can thus enable patients, primary care physicians, and dermatologists to rapidly identify and diagnose skin cancer with diagnostic quality not seen before from any equipment in this class.

Degree

thesis:*
Department dc:contributor.department
Massachusetts Institute of Technology. Department of Mechanical Engineering.
Grantor dc:publisher
Massachusetts Institute of Technology
Year dc:date.issued
2016

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Wortman, Tyler David
Advisor dc:contributor.advisor
  • Alexander H. Slocum.

Subjects

dc:subject × 1

Rights

dc:rights
Statement dc:rights
  • M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
Language dc:language.iso
eng

Identifiers

dc:identifier.*
Handle dc:identifier.uri
http://hdl.handle.net/1721.1/104499
OAI identifier oai:identifier
oai:dspace.mit.edu:1721.1/104499

Chain of custody

source
Harvested from
MIT
Base URL
dspace.mit.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Wortman, Tyler David. LesionAir : a low-cost tool for automated skin cancer diagnosis and mapping. Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/104499