PSilhOuette: Towards an Optimal Number of Clusters Using a Nested Particle Swarm Approach for Liver CT Image Segmentation

Ali, Abder-Rahman; Couceiro, Micael S.; Hassenian, Aboul Ella

doi:10.1007/978-3-319-13461-1_32

Cited by 2 publications

(1 citation statement)

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“…In [16], a novel segmentation method based on a nested particle swarm optimization (PSO) method is used, to find the optimal number of clusters for segmenting a grayscale image. This method has two functions (i) find the most adequate number of clusters using the silhouette index as a measure of similarity; and (ii) segment the image using the Fuzzy C-Means (FCM) approach using the number of clusters previously retrieved.…”

Section: Introductionmentioning

confidence: 99%

Artificial Bee Colony Based Segmentation for CT Liver Images

Mostafa

Fouad

Elfattah

et al. 2016

Studies in Computational Intelligence

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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

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Section: Introductionmentioning

confidence: 99%