Automatic region of interest segmentation for breast thermogram image classification

•An automatic region of interest segmentation method•A set of rules as a metric to measure the region of interest segmentation results•Analysis of thermal matrices as new data source for feature extraction•Design of experiments in a more robust manner taking into account most of the images in the da...

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Bibliographic Details
Published in:Pattern recognition letters 2020-07, Vol.135, p.72-81
Main Authors: Sánchez-Ruiz, Daniel, Olmos-Pineda, Ivan, Olvera-López, J. Arturo
Format: Article
Language:English
Subjects:
Abnormalities
Automatic segmentation
Breast cancer
Classification
Computer vision
Image classification
Image processing
Image segmentation
Interpolation
Pattern recognition
Thermography
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Summary:•An automatic region of interest segmentation method•A set of rules as a metric to measure the region of interest segmentation results•Analysis of thermal matrices as new data source for feature extraction•Design of experiments in a more robust manner taking into account most of the images in the database•Genetic algorithm for hyperparameter tunning for the Artificial Neural Network Breast thermography images are a new type of data that has been analyzed in recent years in order to detect abnormalities, which can lead to a future breast cancer. This paper proposes a methodology for breast thermal image classification, which is useful in Computer-Aided Detection Systems. The main contribution is an automatic method to segment the region of interest (ROI) based on local operations, local analysis, interpolation and statistical operators. For our experiments, we used an image database that is widely used in this research area, obtaining accuracy results between 90.17% and 98.33%, which are competitive with respect to related works.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2020.03.025