Introducing kernel based morphology as an enhancement method for mass classification on mammography

Abstract

Since mammography images are in low-contrast, applying enhancement techniques as a pre-processing step are wisely recommended in classification of the abnormal lesions into benign or malignant. A new kind of structural enhancement is proposed by morphological operator which introduces an optimal Gaussian Kernel primitive, the kernel parameters are optimized the use of Genetic Algorithm­­. We also take the advantageous of Optical Density (OD) images to promote the diagnosis rate. OD images are free from scanner type, and their values are the degree of blackness presented at the given point on the film and distinguish small differences. When the proposed enhancement method is applied on both the Gray Level (GL) images and their OD values respectively, morphological patterns get bolder on gray level images, therefore; Local Binary Patterns (LBP) are extracted from this kind of images. Applying the enhancement method on OD images causes to remove some background pixels. Those pixels that are more eligible to be mass are remained, and some statistical texture features are extracted. Support Vector Machine is used for both approaches, and the final decision is made by combining these two classifiers. The classification performance rate is evaluated by A_z, under the receiver operating characteristic (ROC) curve. The designed method yields A_{­­­z} = 0.9231 which demonstrates good results.