References

@article{Milosevic201549,

title = {Comparative analysis of breast cancer detection in mammograms and thermograms},

author = {Marina Milosevic and Dragan Jankovic and Aleksandar Peulic},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925344935\&doi=10.1515%2fbmt-2014-0047\&partnerID=40\&md5=ad9028a1efddb5639a1bb0d373c169b2},

doi = {10.1515/bmt-2014-0047},

year  = {2015},

date = {2015-01-01},

journal = {Biomedizinische Technik},

volume = {60},

number = {1},

pages = {49 \textendash 56},

publisher = {Walter de Gruyter GmbH},

abstract = {In this paper, we present a system based on feature extraction techniques for detecting abnormal patterns in digital mammograms and thermograms. A comparative study of texture-analysis methods is performed for three image groups: mammograms from the Mammographic Image Analysis Society mammographic database; digital mammograms from the local database; and thermography images of the breast. Also, we present a procedure for the automatic separation of the breast region from the mammograms. Computed features based on gray-level co-occurrence matrices are used to evaluate the effectiveness of textural information possessed by mass regions. A total of 20 texture features are extracted from the region of interest. The ability of feature set in differentiating abnormal from normal tissue is investigated using a support vector machine classifier, Naive Bayes classifier and K-Nearest Neighbor classifier. To evaluate the classification performance, five-fold cross-validation method and receiver operating characteristic analysis was performed. © 2015, Walter de Gruyter GmbH. All rights reserved.},

note = {All Open Access, Bronze Open Access},

keywords = {Algorithms; Breast Neoplasms; Female; Humans; Machine Learning; Mammography; Observer Variation; Radiographic Image Interpretation},

pubstate = {published},

tppubtype = {article}

}