A novel Image mining Technique for Classification of Mammograms using Hybrid Feature Selection

Main Article Content

Aswini Kumar Mohanty
Manas Ranjan Senapati, Saroj Kumar Lenka


The image mining technique deals with the extraction of implicit knowledge and image with data relationship or other patterns not
explicitly stored in the images. It is an extension of data mining to image domain. The main objective of this paper is to apply image mining in
the domain such as breast mammograms to classify and detect the cancerous tissue. Mammogram image can be classified into normal, benign
and malignant class. Total of 26 features including histogram intensity features and GLCM features are extracted from mammogram images. A
hybrid approach of feature selection is proposed which approximately reduces 75% of the features and new decision tree is used for
classification. The most interesting one is that branch and bound algorithm which is used for feature selection provides the best optimal features
and no where it is applied or used for GLCM feature selection from mammogram, Experiments have been taken for a data set of 300 images
taken from MIAS of different types with the aim of improving the accuracy by generating minimum no. of rules to cover more patterns. The
accuracy obtained by this method is approximately 97.7% which is highly encouraging.



Keywords: Mammogram, GLCM feature, Histogram Intensity, Genetic Algorithm, Branch and Bound technique, Decision tree Classification.


Download data is not yet available.

Article Details