Automatic region of interest segmentation for breast thermogram classification

被引：25

作者：

Sanchez-Ruiz, Daniel ^{[1
]}

Olmos-Pineda, Ivan ^{[1
]}

Arturo Olvera-Lopez, J. ^{[1
]}

机构：

[1] BUAP, Fac Comp Sci, 14 Sur & Av San Claudio, Puebla 72592, Pue, Mexico

来源：

PATTERN RECOGNITION LETTERS | 2020年 / 135卷

关键词：

CANCER; FEATURES; IMAGES;

D O I：

10.1016/j.patrec.2020.03.025

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. © 2020 Elsevier B.V.

引用

页码：72 / 81

页数：10

共 50 条

[1] Automatic segmentation of region of interest for breast thermographic image classification
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