Optimal image representations for massdetection in digital mammography
2005
This work addresses a two–class classification problem related to one of the leading
cause of death among women worldwide, namely breast cancer. The two
classes to separate are tumoral masses and normal breast tissue.
The proposed approach does not rely on any feature extraction step aimed at
finding few measurable quantities characterizing masses. On the contrary, the
mammographic regions of interest are passed to the classifier—a Support Vector
Machine (SVM)—in their raw form, for instance as vectors of gray–level values.
In this sense, the approach adopted is a featureless approach, since no feature is
extracted from the region of interest, but its image representation embodies itself
all the features to classify.
In order to find the optimal image representation, several ones are evaluated by
means of Receiver Operating Characteristic (ROC) curve analysis. Image representations
explored include pixel–based, wavelet–based, steer–based and ranklet–
based ones. In particular, results demonstrate that the best classification performances
are achieved by the ranklet–based image representation. Due to its good
results, its performances are further explored by applying SVM Recursive Feature
Elimination (SVM–RFE), namely recursively eliminating some of the less
discriminant ranklets coefficients according to the cost function of SVM. Experiments
show good classification performances even after a significant reduction of
the number of ranklet coefficients.
Finally, the ranklet–based and wavelet–based image representations are practically
applied to a real–time working Computer–Aided Detection (CAD) system
developed by our group for tumoral mass detection. The classification performances
achieved by the proposed algorithm are interesting, with a false–positive
rate of 0.5 marks per–image and 77% of cancers marked per–case.
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