Segmentation of liver in ultrasonic images applying local optimal threshold method

Abstract

Low brightness contrast and grey level discontinuities of the ultrasonic liver image make it difficult to segment the object and the background and to extract the edges of the object using the global optimal threshold method. In this paper, we investigate a local optimal threshold method for the segmentation of ultrasound liver image. First of all, the distributed energy of the ultrasound liver image is estimated in the proposed liver segmentation. Then, the polynomials are fitted from the distributed energy data and a peak zone is defined from the minimum of the fitted polynomials. Finally, a few blocked images are divided from the number of the peak zones. Furthermore, multiple local optimal thresholds are obtained from the blocked images using Otsu's method, and the ultrasonic liver image is segmented according to all local optimal thresholds. Experimental results validate the segmentation and edge detection of liver in the ultrasound images.

A region‐based segmentation method for ultrasound images in HIFU therapy

“…(4) The computation of the similarity metric in which a specific superpixel-based similarity metric of spectral clustering is computed. (5) The clustering process in which the final contour of the tumor is extracted by clustering the superpixels using the proposed adaptive multiway spectral clustering method.…”

“…To date, many methods have been developed for the segmentation of various ultrasound images, including breast ultrasound images, 3,4 liver tumor ultrasound images, 5 echocardiography, 6,7 prostate ultrasound images, 8,9 intravascular ultrasound images, [10][11][12][13] and some ultrasound images of other organs or tissues. 14 As Huang et al 15 indicated, the active contour model (ACM) was widely used in the segmentation of all types of ultrasound tumor images in past decades.…”