Liver segmentation for CT images using GVF snake

Abstract

Accurate liver segmentation on computed tomography (CT) images is a challenging task especially at sites where surrounding tissues (e.g., stomach, kidney) have densities similar to that of the liver and lesions reside at the liver edges. We have developed a method for semiautomatic delineation of the liver contours on contrast-enhanced CT images. The method utilizes a snake algorithm with a gradient vector flow (GVF) field as its external force. To improve the performance of the GVF snake in the segmentation of the liver contour, an edge map was obtained with a Canny edge detector, followed by modifications using a liver template and a concavity removal algorithm. With the modified edge map, for which unwanted edges inside the liver were eliminated, the GVF field was computed and an initial liver contour was formed. The snake algorithm was then applied to obtain the actual liver contour. This algorithm was extended to segment the liver volume in a slice-by-slice fashion, where the result of the preceding slice constrained the segmentation of the adjacent slice. 551 two-dimensional liver images from 20 volumetric images with colorectal metastases spreading throughout the livers were delineated using this method, and also manually by a radiologist for evaluation. The difference ratio, which is defined as the percentage ratio of mismatching volume between the computer and the radiologist's results, ranged from 2.9% to 7.6% with a median value of 5.3%.

Automatic Abdominal Organ Segmentation from CT images

“…gray level based techniques, learning techniques, model fitting techniques, probabilistic atlases, and level set) this problem is still open. Indeed, although the gray level based techniques proposed so far [11,12,5,13,14,15,16,17] obtain the most promising results, they are not robust to database variations; this is because their basic step of organ gray level estimation does not take into account the high variability observed both in the same and in different CT volumes. For this reason, when tested on larger and complex data sets, these methods' performance could decrease significantly.…”

“…The procedure that checks consecutive slices in Liv, is initialized with a starting slice, where the liver is correctly segmented. The methods presented in the literature [14] need the user to select it, so that they are affected from his errors and biases and the repeatability of the results is low; for this reason we automatically find a starting slice, by applying a compactness criterium. To this aim, for each axial slice i = 1, .., P , we compute the number of pixels segmented as liver in i, Num i Liv , and a coefficient, C i Liv , as:…”

Lumbar Spine Disc Herniation Diagnosis with a Joint Shape Model

“…GVF-snake requires an edge map that is a binary image highlighting the desired features (edges) of the image. Most researchers use Canny edge detector or Sobel operator on the original image such as [19] for liver segmentation. We present the GVF-snake with a canny edge map applied on our feature image I. Herniation Classification: We design a binary Bayesian classifier:…”

Level Set Segmentation of Breast Masses in Contrast-Enhanced Dedicated Breast CT and Evaluation of Stopping Criteria

“…This model seeks an object margin that minimizes an energy functional consisting of internal energy and external energy along the deformable contour. Active contour segmentation has been used in medical imaging [13][14][15][16]. In breast imaging, Brake et al used a discrete active contour method to segment mammographic mass lesions [17].…”