Automatic segmentation of dermoscopy images using self-generating neural networks seeded by genetic algorithm

Authors:
Fengying Xie;Alan C. Bovik
Affiliations:
School of Aeronautics and Astronautics, Beihang University, Beijing 100191, China;Department of Electrical and Computer Engineering, The University of Texas at Austin, TX 78712, USA
Venue:
Pattern Recognition
Year:
2013

Citing 8
Cited 0

Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Comparing Images Using the Hausdorff Distance

IEEE Transactions on Pattern Analysis and Machine Intelligence
Quality Scheme Assessment in the Clustering Process

PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Exploiting color and topological features for region segmentation with recursive fuzzy C-means

Machine Graphics & Vision International Journal - Special issue on latest results in colour image processing and applications
Statistical Region Merging

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting clusters of different geometrical shapes in microarray gene expression data

Bioinformatics
A Cluster Separation Measure

IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficiency of self-generating neural networks applied to pattern recognition

Mathematical and Computer Modelling: An International Journal

Quantified Score

Hi-index	0.01

Visualization

Abstract

A novel dermoscopy image segmentation algorithm is proposed using a combination of a self-generating neural network (SGNN) and the genetic algorithm (GA). Optimal samples are selected as seeds using GA; taking these seeds as initial neuron trees, a self-generating neural forest (SGNF) is generated by training the rest of the samples using SGNN. Next the number of clusters is determined by optimizing the SD index of cluster validity, and clustering is completed by treating each neuron tree as a cluster. Since SGNN often delivers inconsistent cluster partitions owing to sensitivity relative to the input order of the training samples, GA is combined with SGNN to optimize and stabilize the clustering result. In the post-processing phase, the clusters are merged into lesion and background skin, yielding the segmented dermoscopy image. A series of experiments on the proposed model and the other automatic segmentation methods (including Otsu's thresholding method, k-means, fuzzy c-means (FCM) and statistical region merging (SRM)) reveals that the optimized model delivers better accuracy and segmentation results.