Una metodología para la segmentación automática en CADs de imágenes térmicas
(An approach for automatic segmentation of thermal imaging in Computer Aided Diagnosis)
Rafael Souza Marques (email@example.com)1, Aura Conci (firstname.lastname@example.org)2, María G. Pérez (email@example.com)3, Víctor H. Andaluz (firstname.lastname@example.org)4, Tatiana M. Mejía (email@example.com)5
1Visual Lab, IC/UFF2Universidade Federal Fluminense -UFF3Escuela Politécnica Nacional4Universidad de las Fuerzas Armadas ESPE5Universidad Técnica de Ambato
This paper appears in: Revista IEEE América Latina
Publication Date: April 2016
Volume: 14, Issue: 4
Breast cancer is major cause of the high mortality rates among young women in developing countries. In Latin America, it is a great health problem, as well. For example in Brazil and Ecuador, this is the leading cause of cancer among women around 35 years old. Early detection is important to improve the chance of cure. Thermal imaging has the ability to show regions where there is any potential cancer by indicating areas of the body where there is an abnormal temperature variation. Moreover, thermography can detect suspicious regions in patients of any age, even in cases of dense breasts, where the detection of an abnormality can not be achieved by mammography. An essential step in the use of thermal imaging systems is the development of computer-aided diagnosis (CAD). However, any development towards a CAD system or even an examination guided by computer should consider adequate extraction of the region of interest (ROI). This paper proposes a methodology for automatic segmentation of thermal imaging breast and validation of the results by generating a Ground Truth (GT). The automatic method proposed in this paper consists of several image-processing techniques such as thresholding, clustering, edge detection and refinement, among others. For the evaluation of the results the developed GT are presently available on the Internet, in order to allow proper verification of the results. Finally, the results obtained by the proposed methodology for the 328 images used in this study showed average values of accuracy and sensitivity around 96% and 97%, respectively.
Image processing, early detection, automatic segmentation of imaging, region of interest (ROI), thermal imaging, clustering, morphological operations, region growing, curve fitting, refinements, interpolation, Hausdorff distance, computer-aided diagn
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