Depth Data Improves Skin Lesion Segmentation

Authors:
Xiang Li;Ben Aldridge;Lucia Ballerini;Robert Fisher;Jonathan Rees
Affiliations:
School of Informatics, University of Edinburgh, UK;Dermatology, University of Edinburgh, UK;School of Informatics, University of Edinburgh, UK;School of Informatics, University of Edinburgh, UK;Dermatology, University of Edinburgh, UK
Venue:
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Year:
2009

Citing 7
Cited 1

Scale-Space and Edge Detection Using Anisotropic Diffusion

IEEE Transactions on Pattern Analysis and Machine Intelligence
The Design and Use of Steerable Filters

IEEE Transactions on Pattern Analysis and Machine Intelligence
A Review of Statistical Approaches to Level Set Segmentation: Integrating Color, Texture, Motion and Shape

International Journal of Computer Vision
A Variational Framework for Multiregion Pairwise-Similarity-Based Image Segmentation

IEEE Transactions on Pattern Analysis and Machine Intelligence
A multi-direction GVF snake for the segmentation of skin cancer images

Pattern Recognition
A narrow band graph partitioning method for skin lesion segmentation

Pattern Recognition
Active contours without edges

IEEE Transactions on Image Processing

Content-based image retrieval of skin lesions by evolutionary feature synthesis

EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper shows that adding 3D depth information to RGB colour images improves segmentation of pigmented and non-pigmented skin lesion. A region-based active contour segmentation approach using a statistical model based on the level-set framework is presented. We consider what kinds of properties (e.g., colour, depth, texture) are most discriminative. The experiments show that our proposed method integrating chromatic and geometric information produces segmentation results for pigmented lesions close to dermatologists and more consistent and accurate results for non-pigmented lesions.