TY - GEN
T1 - Automatic recognition of microcalcifications in mammography images through fractal texture analysis
AU - Cardona, Hernán Darío Vargas
AU - Orozco, Álvaro
AU - Álvarez, Mauricio A.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2014.
PY - 2014
Y1 - 2014
N2 - Mammography images are widely used for detection of microcalcifications (MCs), which constitute the early stage of breast cancer. Moreover, these images allow the medical specialist to perform a timely diagnosis and to prevent complications in patients. Automatic identification of MCs in mammography images may be useful as a decision support given by a specialist. In this paper, we construct a mammography image database with medical validation and expert labeling. The test subjects are from a local population located in the Eje cafetero, Colombia. Also, we present a methodology for automatic recognition of microcalcifications based on segmentation with fractal texture analysis (SFTA) and a support vector machine (SVM). For a comparison framework with the state of the art, we compare our methodology with the local binary patterns (LBP) method, that is widely applied in digital images processing. Results show that SFTA methodology for recognition of MCs achieves an accuracy over 92.5% improving significatively when compared to LBP. Also, our database satisfies the epidemiological parameters to represent a local population.
AB - Mammography images are widely used for detection of microcalcifications (MCs), which constitute the early stage of breast cancer. Moreover, these images allow the medical specialist to perform a timely diagnosis and to prevent complications in patients. Automatic identification of MCs in mammography images may be useful as a decision support given by a specialist. In this paper, we construct a mammography image database with medical validation and expert labeling. The test subjects are from a local population located in the Eje cafetero, Colombia. Also, we present a methodology for automatic recognition of microcalcifications based on segmentation with fractal texture analysis (SFTA) and a support vector machine (SVM). For a comparison framework with the state of the art, we compare our methodology with the local binary patterns (LBP) method, that is widely applied in digital images processing. Results show that SFTA methodology for recognition of MCs achieves an accuracy over 92.5% improving significatively when compared to LBP. Also, our database satisfies the epidemiological parameters to represent a local population.
UR - http://www.scopus.com/inward/record.url?scp=84916620655&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-14364-4_81
DO - 10.1007/978-3-319-14364-4_81
M3 - Conference contribution
AN - SCOPUS:84916620655
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 841
EP - 850
BT - Advances in Visual Computing - 10th International Symposium, ISVC 2014, Proceedings
A2 - Bebis, George
A2 - Boyle, Richard
A2 - Parvin, Bahram
A2 - Koracin, Darko
A2 - McMahan, Ryan
A2 - Jerald, Jason
A2 - Zhang, Hui
A2 - Drucker, Steven M.
A2 - Chandra, Kambhamettu
A2 - Maha, El Choubassi
A2 - Deng, Zhigang
A2 - Carlson, Mark
PB - Springer Verlag
T2 - 10th International Symposium on Visual Computing, ISVC 2014
Y2 - 8 December 2014 through 10 December 2014
ER -
