Quantitative evaluation of diagnostic information around the contours in ultrasound images

To develop a new contour extraction method for identifying abnormal tissue.

We combined two techniques: logarithmic K distribution of a scattering model (method 1) and regional discrimination using the characteristics of local ultrasound images (method 2) into an integrated method (method 3) that provides accurate contours, which are essential for quantitizing border information.

The diagnostic tissue information around the border of an image can be characterized by its shape and texture statistics. The degrees of circularity and irregularity and the depth-width ratio were calculated for the extracted contours of breast tumors. In addition, gradients, separability, and variance between the two regions along the contour and the area and variance of the internal echoes, were calculated as indices of diagnostic criteria of breast tumors. The quantitized indices were able to discriminate among cysts, fibroadenomas, and cancer.

In many ultrasound images of breast tumors, the combined techniques, the variance ratio of the logarithmic K distribution to the logarithmic Rayleigh distribution and the multilevel technique with local image information can effectively extract abnormal tissue contours.