  Automated detection of breast tumor in early stages using B-Mode Ultrasound
image is crucial for preventing widespread breast cancer specially among women.
This paper is primarily focusing on the classification of breast tumors through
statistical modelling such as Rician inverse Gaussian (RiIG) pdf of contourlet
transformed B-Mode image of breast tumors which is not reported yet in other
earlier works. The suitability of RiIG distribution in modeling the contourlet
coefficients is illustrated and compared with that of Nakagami distribution.
The proposed method consists of pre-processing to remove the speckle noise,
segmentation of the lesion region, contourlet transform on the B-Mode
Ultrasound image and using the corresponding contourlet sub-band coefficients
and the RiIG parameters, production of contourlet parametric (CP) images and
weighted contourlet parametric (WCP) images. A number of geometrical,
statistical, and texture features are calculated from B-Mode and the contourlet
parametric images. In order to classify the features, seven different
classifiers are employed. The proposed approach is applied to two different
datasets (Mendeley Data and Dataset B) those are available publicly. It is
shown that with parametric images, accuracies in the range of 94-97% are
achieved for different classifiers. Specifically, with the support vector
machine and k-nearest-neighbor classifier, very high accuracies of 97.2% and
97.55% can be obtained for the Mendeley Data and Dataset B,respectively, using
the weighted contourlet parametric images.The reported classification
performance is also compared with that of other works using the datasets
employed in this paper. It is seen that the proposed approach using weighted
contourlet parametric images can provide a superior performance.
