Automatic Prostate Segmentation in Ultrasound Images using Gradient Vector Flow Active Contour

ABSTRACT: Prostate cancer is one of the leading causes of death by cancer among men in the world. Ultrasonography is said to be the safest technique in medical imaging so it is used extensively in prostate cancer detection. On the other hand, determining of prostate's boundary in TRUS (Transrectal Ultrasound) images is very necessary in lots of treatment methods prostate cancer. So the first and essential step for computer aided diagnosis (CAD) is the automatic prostate segmentation that is an open problem yet. But the low SNR, presence of strong speckle noise, Weak edges and shadow artifacts in these kinds of images limit the effectiveness of classical segmentation schemes. The classical segmentation methods fail completely or require post processing step to remove invalid object boundaries in the segmentation results. This paper has proposed a fully automatic algorithm for prostate segmentation in TRUS images that overcomes the explained problems completely. The presented algorithm contains three main stages. First, morphological smoothing and stick's filter are used for noise removing. A neural network is employed in the second step to find a point in prostate region. Finally in the last step, the prostate boundaries are extracted by GVF active contour. Some experiments for the performance validity of the presented method, compared with the extracted prostate by the proposed algorithm with manually-delineated boundaries by radiologist. The results show that our method extracts prostate boundaries with mean square area error lower than 4.4%

The GVF Snake with a Minimal Path Approach

In this paper we propose a contour extraction method based on the active contour model, which uses the GVF snake to obtain the initial segments for a contour; and then a minimal path method for the refinement stage, to obtain an accurate and more robust result. By employing the minimal path method to find missing segments between pairs of nodes defined on the contour obtained by the GVF snake, our algorithm is able to detect deep concave parts of object boundary, and works well even when the snake initialization is not very good.Department of Electronic and Information EngineeringRefereed conference pape