Artificial Bee Colony Based Segmentation for CT Liver Images

Abstract

The objective of this paper is to evaluate an approach for CT liver image segmentation, to separate the liver, and segment it into a set of regions of interest (ROIs). The automated segmentation of liver is an essential phase in all liver diagnosis systems for different types of medical images. In this paper, the artificial bee colony optimization algorithm (ABC) aides to segment the whole liver. It is implemented as a clustering technique to achieve this mission. ABC calculates the centroid values of image clusters in CT images. Using the least distance between every pixel value and different centroids will result in a binary image for each cluster. Applying some morphological operations on every binary clustered image can help to remove small and thin objects. These objects represent parts of flesh tissues adjacent to the liver, sharp edges of other organs and tiny lesions spots inside the liver. This is followed by filling the large regions in each cluster binary image. Summation of the clusters' binary images results in a reasonable image of segmented liver. Then, the segmented image of liver is enhanced using simple region growing technique (RG). Finally, one of ABC algorithm or watershed is applied once to extract the lesioned regions in the liver, which can be used by any classifier to determine the type of lesion. A set of 38 images, taken in pre-contrast phase, was used to segment the liver and test the proposed approach. Testing the results is handled using similarity index to validate the success of the approach. The

Multilevel Thresholding Color Image Segmentation Using a Modified Artificial Bee Colony Algorithm

“…With the target direction effect AT and local effect AL, rule of food source update movement can be described as: (9) ω f = 0.9 − 0.8 cycle CycMax (10) where ω f is the inertia weight of the motion. In KABC algorithm, use Eq.…”

“…These algorithms stand out for their ability of generating high-quality solutions in acceptable periods when solving nonlinear optimization problems. Researchers have applied several nature-inspired algorithms to image segmentation successfully [9]- [11]. For example, Ming-Huwi Horng et al used Artificial Bee Colony (ABC) algorithm to select thresholds which can dramatically speed up threshold searching.…”