Bilateral image subtraction features for multivariate automated classification of breast cancer risk

Jose M. Celaya-Padilla, Juan Rodriguez-Rojas, Jorge I. Galván-Tejada, Antonio Martínez-Torteya, Victor Treviño, José G. Tamez-Peña

Resultado de la investigación

4 Citas (Scopus)

Resumen

Early tumor detection is key in reducing breast cancer deaths and screening mammography is the most widely available method for early detection. However, mammogram interpretation is based on human radiologist, whose radiological skills, experience and workload makes radiological interpretation inconsistent. In an attempt to make mammographic interpretation more consistent, computer aided diagnosis (CADx) systems has been introduced. This paper presents an CADx system aimed to automatically triage normal mammograms form suspicious mammograms. The CADx system co-reregister the left and breast images, then extracts image features from the co-registered mammographic bilateral sets. Finally, an optimal logistic multivariate model is generated by means of an evolutionary search engine. In this study, 440 subjects form the DDSM public data sets were used: 44 normal mammograms, 201 malignant mass mammograms, and 195 mammograms with malignant calci cations. The results showed a cross validation accuracy of 0.88 and an area under receiver operating characteristic (AUC) of 0.89 for the calci cations vs. normal mammograms. The optimal mass vs. normal mammograms model obtained an accuracy of 0.85 and an AUC of 0.88. © 2014 SPIE.
Idioma originalEnglish
DOI
EstadoPublished - 1 ene 2014
Publicado de forma externa
EventoProgress in Biomedical Optics and Imaging - Proceedings of SPIE -
Duración: 1 ene 2014 → …

Conference

ConferenceProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Período1/1/14 → …

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging

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    Celaya-Padilla, J. M., Rodriguez-Rojas, J., Galván-Tejada, J. I., Martínez-Torteya, A., Treviño, V., & Tamez-Peña, J. G. (2014). Bilateral image subtraction features for multivariate automated classification of breast cancer risk. Papel presentado en Progress in Biomedical Optics and Imaging - Proceedings of SPIE, . https://doi.org/10.1117/12.2043870