Segmentation of liver tumors in ultrasound images based on scale-space analysis of the continuous wavelet transform

Abstract

We have developed a simple, yet robust method for segmentation of low-contrast objects embedded in noisy images. Our technique has been applied to segmenting of liver tumors in B-scan ultrasound images with hypoechoic rims. In our method, first a B-scan image is processed by a median filter for removal of speckle noise. Then several one-dimensional profiles are obtained along multiple radial directions which pass through the manually identified center of the region of a tumor. After smoothing by a Gaussian kernel smoother, these profiles are processed by Sombrero's continuous wavelets to yield scalograms over a range of scales. The modulus maxima lines, which represent the degree of regularity at individual points on the profiles, are then utilized for identifying candidate points on the boundary of the tumor. These detected boundary points are fitted by an ellipse and are used as an initial configuration of a wavelet snake. The wavelet snake is then deformed so that the accurate boundary of the tumor is found. A preliminary result for several metastases with various sizes of hypoechoic rims showed that our method could extract boundaries of the tumors which were close to the contours drawn by expert radiologists. Therefore, our new method can segment the regions of focal liver disease in sonograms with accuracy, and it can be useful as a preprocessing step in our scheme for automated classification of focal liver disease in sonography.