Angelini, Enrico ; Campanini, Renato ; Iampieri, Emiro ; Masotti, Matteo ; Petkov, Todor ; Roffilli, Matteo
(2006)
A ranklet-based CAD for digital mammography.
Proceedings of the 8th International Workshop on Digital Mammography
.
pp. 340-346.
Full text available as:
Abstract
A novel approach to the detection of masses and clustered
microcalcification is presented. Lesion detection is considered as a two-class
pattern recognition problem. In order to get an effective and stable
representation, the detection scheme codifies the image by using a ranklet
transform. The vectors of ranklet coefficients obtained are classified by means
of an SVM classifier. Our approach has two main advantages. First it does not
need any feature selected by the trainer. Second, it is quite stable, with respect
to the image histogram. That allows us to tune the detection parameters in one
database and use the trained CAD on other databases without needing any
adjustment. In this paper, training is accomplished on images coming from
different databases (both digitized and digital). Test results are calculated on
images coming from a few FFDM Giotto Image MD clinical units. The
sensitivity of our CAD system is about 85% with a false-positive rate of 0.5
marks per image.
Abstract
A novel approach to the detection of masses and clustered
microcalcification is presented. Lesion detection is considered as a two-class
pattern recognition problem. In order to get an effective and stable
representation, the detection scheme codifies the image by using a ranklet
transform. The vectors of ranklet coefficients obtained are classified by means
of an SVM classifier. Our approach has two main advantages. First it does not
need any feature selected by the trainer. Second, it is quite stable, with respect
to the image histogram. That allows us to tune the detection parameters in one
database and use the trained CAD on other databases without needing any
adjustment. In this paper, training is accomplished on images coming from
different databases (both digitized and digital). Test results are calculated on
images coming from a few FFDM Giotto Image MD clinical units. The
sensitivity of our CAD system is about 85% with a false-positive rate of 0.5
marks per image.
Document type
Article
Creators
Keywords
Ranklet, Support Vector Machine, Computer-Aided Detection, Mammography
Subjects
DOI
Deposit date
25 Sep 2006
Last modified
16 May 2011 12:04
URI
Other metadata
Document type
Article
Creators
Keywords
Ranklet, Support Vector Machine, Computer-Aided Detection, Mammography
Subjects
DOI
Deposit date
25 Sep 2006
Last modified
16 May 2011 12:04
URI
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