Breast cancer only detected by thermography

Yokoe, Takao; Iino, Yuichi; Tokura, Y.; Aoyagi, Hidetada; Sugamata, Noritaka; Koyama, Takayuki; Ohwada, Susumu; Morishita, Yasuo

doi:10.4993/acrt1992.4.89

Search citation statements

Order By: Relevance

Paper Sections

Select...

Introduction1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2015

Publication Types

Select...

Book1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…non-palpable lesions, that cannot be detected by physical examination or with a mammography [3]. Adding a thermography study to mammography provides a 95 % of accuracy in the diagnosis, whereas mammography by itself has only an 80 % of accuracy in the early stages of cancer [4].…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Regions of Interest in Breast Thermographic Images

Villalobos-Montiel¹,

Chacón-Murguía²,

Calderon-Contreras³

et al. 2015

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Breast thermography is a promising technique allowing breast cancer detection with the aid of infrared technology. However, the automatic segmentation of the regions of interest (ROI) to be analyzed is a difficult task and, thus, it is not commonly performed. In this paper we propose an automated technique for ROI extraction. The algorithm uses Canny edge detection with automatic threshold and symmetry inspection in order to correct tilt errors and to define a symmetry axis in the image. Furthermore, the Hough transform is used to provide circles giving a favorable approximation to the ROI. A region-based active contours technique and an edge smoothing technique are also used to improve the previous segmentation, taking in consideration the energies of the images. Experimental results indicate that the ROI were extracted more accurately and with higher precision than a state-of-the-art method.

show abstract

Section: Introductionmentioning

confidence: 99%