Detection of breast abnormalities of thermograms based on a new segmentation method

Ali, MAS; Sayed, GI; Gaber, T; Hassanien, AE; Snasel, V; Silva, LF

Abstract

Breast cancer is one from various diseases that has
got great attention in the last decades. This due to the number
of women who died because of this disease. Segmentation is
always an important step in developing a CAD system. This paper
proposed an automatic segmentation method for the Region of
Interest (ROI) from breast thermograms. This method is based
on the data acquisition protocol parameter (the distance from
the patient to the camera) and the image statistics of DMR-IR
database. To evaluated the results of this method, an approach for
the detection of breast abnormalities of thermograms was also
proposed. Statistical and texture features from the segmented
ROI were extracted and the SVM with its kernel function
was used to detect the normal and abnormal breasts based
on these features. The experimental results, using the benchmark
database, DMR-IR, shown that the classification accuracy
reached (100%). Also, using the measurements of the recall and
the precision, the classification results reached 100%. This means
that the proposed segmentation method is a promising technique
for extracting the ROI of breast thermograms.

Citation

Ali, M., Sayed, G., Gaber, T., Hassanien, A., Snasel, V., & Silva, L. Detection of breast abnormalities of thermograms based on a new segmentation method. Presented at Federated Conference on Computer Science and Information System

• Presentation Conference Type: Other
• Conference Name: Federated Conference on Computer Science and Information System
• Publication Date: Jan 1, 2015
• Deposit Date: Sep 11, 2019
• DOI: https://doi.org/10.15439/2015F318
• Publisher URL: http://dx.doi.org/10.15439/2015F318
• Related Public URLs: https://annals-csis.org/
• Additional Information: Event Type : Conference; Funders : European Social Fund;The state budget of the Czech Republic; Projects : New creative teams in priorities of scientific research",; Grant Number: CZ.1.07/2.3.00/30.0055,