Automatic region of interest segmentation for breast thermogram image classification
| Autor: | J. Arturo Olvera-López, Daniel Sánchez-Ruiz, Ivan Olmos-Pineda |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Contextual image classification
Computer science business.industry ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Pattern recognition 02 engineering and technology medicine.disease 01 natural sciences ComputingMethodologies_PATTERNRECOGNITION Breast cancer Artificial Intelligence Region of interest 0103 physical sciences Signal Processing Pattern recognition (psychology) 0202 electrical engineering electronic engineering information engineering medicine 020201 artificial intelligence & image processing Segmentation Computer Vision and Pattern Recognition Artificial intelligence 010306 general physics business Software Interpolation |
| Zdroj: | Pattern Recognition Letters. 135:72-81 |
| ISSN: | 0167-8655 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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