Computer Vision Techniques for the Diagnosis of Skin Cancer, 1st Edition

  • Published By:
  • ISBN-10: 3642396089
  • ISBN-13: 9783642396083
  • DDC: 616.99477
  • Grade Level Range: College Freshman - College Senior
  • 282 Pages | eBook
  • Original Copyright 2014 | Published/Released May 2014
  • This publication's content originally published in print form: 2014

  • Price:  Sign in for price



The goal of this volume is to summarize the state-of-the-art in the utilization of computer vision techniques in the diagnosis of skin cancer. Malignant melanoma is one of the most rapidly increasing cancers in the world. Early diagnosis is particularly important since melanoma can be cured with a simple excision if detected early. In recent years, dermoscopy has proved valuable in visualizing the morphological structures in pigmented lesions. However, it has also been shown that dermoscopy is difficult to learn and subjective. Newer technologies such as infrared imaging, multispectral imaging, and confocal microscopy, have recently come to the forefront in providing greater diagnostic accuracy. These imaging technologies presented in this book can serve as an adjunct to physicians and  provide automated skin cancer screening. Although computerized techniques cannot as yet provide a definitive diagnosis, they can be used to improve biopsy decision-making as well as early melanoma detection, especially for patients with multiple atypical nevi.

Table of Contents

Front Cover.
Other Frontmatter.
Title Page.
Copyright Page.
1: Pigment Network Detection and Analysis.
2: Pattern Analysis in Dermoscopic Images.
3: A Bag-of-Features Approach for the Classification of Melanomas in Dermoscopy Images: The Role of Color and Texture Descriptors.
4: Automatic Diagnosis of Melanoma Based on the 7-Point Checklist.
5: Dermoscopy Image Processing for Chinese.
6: Automated Detection of Melanoma in Dermoscopic Images.
7: Melanoma Decision Support Using Lighting-Corrected Intuitive Feature Models.
8: Texture Information in Melanocytic Skin Lesion Analysis Based on Standard Camera Images.
9: Recovering Skin Reflectance and Geometry for Diagnosis of Melanoma.
10: Melanoma Diagnosis with Multiple Decision Trees.