Introducing Kernel Based Morphology as an Enhancement Method for Mass Classification on Mammography

Since mammography images are in low-contrast, applying enhancement techniques as a pre-processing step are wisely recommendedin the classification of the abnormal lesions into benign or malignant. A new kind of structural enhancement is proposed by morphologicaloperator, which introduces an optimal Gaussian Kernel primitive, the kernel parameters are optimized the use of Genetic Algorithm. Wealso take the advantages of optical density (OD) images to promote the diagnosis rate. The proposed enhancement method is appliedon both the gray level (GL) images and their OD values respectively, as a result morphological patterns get bolder on GL images; then,local binary patterns are extracted from this kind of images. Applying the enhancement method on OD images causes more differencesbetween the values therefore a threshold method is applied toremove some background pixels. Those pixels that are more eligible to bemass are remained, and some statistical texture features are extracted from their equivalent GL images. Support vector machine is usedfor both approaches and the final decision is made by combining these two classifiers. The classification performance rate is evaluatedby Az, under the receiver operating characteristic curve. The designed method yields Az = 0.9231, which demonstrates good results.