Paper Info
Title
Entropy-based feature extraction and decision tree induction for breast cancer diagnosis with standardized thermograph images.
Abstract
In this study, a computer-assisted entropy-based feature extraction and decision tree induction protocol for breast cancer diagnosis using thermograph images was proposed. First, Beier-Neely field morphing and linear affine transformation were applied in geometric standardization for whole body and partial region respectively. Gray levels of pixel population at the same anatomical position were statistically analyzed for abnormal region classification. Morphological closing and opening operations were used to identify unified abnormal regions. Three types of 25 feature parameters (i.e. 10 geometric, 7 topological and 8 thermal) were extracted for parametric factor analysis. Positive and negative abnormal regions were further reclassified by decision trees to induce the case-based diagnostic rules. Finally, anatomical organ matching was utilized to identify the corresponding organ with the positive abnormal regions. To verify the validity of the proposed case-based diagnostic protocol, 71 and 131 female patients with and without breast cancer were analyzed. Experimental results indicated that 1750 abnormal regions (703 positive and 1047 negative) were detected and 822 branches were broken down into the decision space. Fourteen branches were found to have more than 4 positive abnormal regions. These critical diagnostic paths with less than 10% of positive abnormal regions (61/703=8.6%) can effectively classify more than half of the cancer patients (42/71=59.2%) in the abovementioned 14 branches.
Year
DOI
Venue
2010
10.1016/j.cmpb.2010.04.014
Computer Methods and Programs in Biomedicine
Keywords
Field
DocType
positive abnormal region,entropy-based feature extraction,negative abnormal region,breast cancer diagnosis,breast cancer,cancer patient,abnormal region classification,standardized thermograph image,abnormal region,decision tree induction,decision space,critical diagnostic path,case-based diagnostic rule,affine transformation,parametric analysis,decision tree,factor analysis,feature extraction
Affine transformation,Decision tree,Population,Breast cancer,Pattern recognition,Closing (morphology),Feature extraction,Pixel,Artificial intelligence,Standard anatomical position,Mathematics
Journal
Volume
Issue
ISSN
100
3
1872-7565
Citations 
PageRank 
References 
10
0.73
6
Authors
2
Name
Order
Citations
PageRank
Ming-Yih Lee15614.49
Chi-Shih Yang2100.73