%0 Journal Article %1 Schmidt99 %A Schmidt, F. %A Sorantin, E. %A Szepesvári, Cs. %A Graif, E. %A Becker, M. %A Mayer, H. %A Hartwagner, K. %D 1999 %J Physics in Medicine and Biology %K application, clinical decision health image informatics, networks, neural processing, support %N 5 %P 1231--1243 %T An Automatic Method for the Identification and Interpretation of Clustered Microcalcifications in Mammograms %U http://stacks.iop.org/0031-9155/44/i=5/a=011 %V 44 %X We investigated a method for a fully automatic identification and interpretation process for clustered microcalcifications in mammograms. Mammographic films of 100 patients containing microcalcifications with known histology were digitized and preprocessed using standard techniques. Microcalcifications detected by an artificial neural network (ANN) were clustered and some cluster features served as the input of another ANN trained to differentiate between typical and atypical clusters, while others were fed into an ANN trained on typical clusters to evaluate these lesions. The measured sensitivity for the detection of grouped microcalcifications was 0.98. For the task of differentiation between typical and atypical clusters an Az value of 0.87 was computed, while for the diagnosis an Az value of 0.87 with a sensitivity of 0.97 and a specificity of 0.47 was obtained. The results show that a fully automatic computer system was developed for the identification and interpretation of clustered microcalcifications in mammograms with the ability to differentiate most benign lesions from malignant ones in an automatically selected subset of cases.

@article{Schmidt99, abstract = {We investigated a method for a fully automatic identification and interpretation process for clustered microcalcifications in mammograms. Mammographic films of 100 patients containing microcalcifications with known histology were digitized and preprocessed using standard techniques. Microcalcifications detected by an artificial neural network (ANN) were clustered and some cluster features served as the input of another ANN trained to differentiate between typical and atypical clusters, while others were fed into an ANN trained on typical clusters to evaluate these lesions. The measured sensitivity for the detection of grouped microcalcifications was 0.98. For the task of differentiation between typical and atypical clusters an Az value of 0.87 was computed, while for the diagnosis an Az value of 0.87 with a sensitivity of 0.97 and a specificity of 0.47 was obtained. The results show that a fully automatic computer system was developed for the identification and interpretation of clustered microcalcifications in mammograms with the ability to differentiate most benign lesions from malignant ones in an automatically selected subset of cases.}, added-at = {2020-03-17T03:03:01.000+0100}, author = {Schmidt, F. and Sorantin, E. and Szepesv{\'a}ri, {Cs}. and Graif, E. and Becker, M. and Mayer, H. and Hartwagner, K.}, bdsk-url-1 = {http://stacks.iop.org/0031-9155/44/i=5/a=011}, biburl = {https://www.bibsonomy.org/bibtex/2bfa826d4e747f6ee7bdd55b150738394/csaba}, date-added = {2010-09-02 09:29:13 -0600}, date-modified = {2010-09-02 13:09:15 -0600}, interhash = {5e01e714f960722f78b454744198eaa4}, intrahash = {bfa826d4e747f6ee7bdd55b150738394}, journal = {Physics in Medicine and Biology}, keywords = {application, clinical decision health image informatics, networks, neural processing, support}, number = 5, pages = {1231--1243}, pdf = {papers/schmidt-99.pdf}, timestamp = {2020-03-17T03:03:01.000+0100}, title = {An Automatic Method for the Identification and Interpretation of Clustered Microcalcifications in Mammograms}, url = {http://stacks.iop.org/0031-9155/44/i=5/a=011}, volume = 44, year = 1999 }