Fuzzy C-Means Based Liver CT Image Segmentation with Optimum Number of Clusters

Ali, Abder-Rahman; Couceiro, Micael S.; Hassanien, Aboul Ella; Tolba, Mohamed F.; Snåšel, Václav

doi:10.1007/978-3-319-08156-4_14

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…In our previous work [27], this problem was solved by iteratively increasing the number of clusters. This may be a thorough process if one does not have a priori information about the number of clusters from segmented images of a given dataset usually have.…”

Section: Pso-ncmentioning

confidence: 99%

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

Ali

Couceiro

Hassenian

2014

Communications in Computer and Information Science

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Abstract. This paper proposes a nested particle swarm optimization (PSO) method to find the optimal number of clusters for segmenting a grayscale image. The proposed approach, herein denoted as PSilhOuette, comprises two hierarchically divided PSOs to solve two dependent problems: i) to find the most adequate number of clusters considering the silhouette index as a measure of similarity; and ii) to segment the image using the Fuzzy C-Means (FCM) approach with the number of clusters previously retrieved. Experimental results show that parent particles converge towards maximizing the silhouette value while, at the same time, child particles strive to minimize the FCM objective function.

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Section: Pso-ncmentioning

confidence: 99%

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

Ali

Couceiro

Hassenian

2014

Communications in Computer and Information Science

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“…Because most of the features extracted are continuous values within a certain range it is necessary to cluster the data to discrete values. The clusterization is done using an equivalent ranges for each value given by (1).…”

Section: Training Of the Algorithm Selectormentioning

confidence: 99%

“…For instance, for segmentation in various contexts several dedicated resources exists [27,10,7]. Similarly algorithms for various contexts have been developed such as for natural images [25,38,3,24], for medical images [37,17,29,1] or for biological images [2,28]. The object recognition are received even more attention due to very high interest in object recognition from the industry.…”

Section: Introductionmentioning

confidence: 99%

Symbolic Segmentation Using Algorithm Selection

Lukac,

Abdiyeva,

Kameyama

2015

Preprint

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In this paper we present an alternative approach to symbolic segmentation; instead of implementing a new method we approach symbolic segmentation as an algorithm selection problem. That is, let there be n available algorithms for symbolic segmentation, a selection mechanism forms a set of input features and image attributes and selects on a case by case basis the best algorithm. The selection mechanism is demonstrated from within an algorithm framework where the selection is done in a set of various algorithm networks. Two sets of experiments are performed and in both cases we demonstrate that the algorithm selection allows to increase the result of the symbolic segmentation by a considerable amount.

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“…For instance, for segmentation in various contexts several dedicated resources exist [30,11,8]. Similarly algorithms for various contexts have been developed such as for natural images [28,41,3,27], for medical images [40,19,32,1] or for biological images [2,31]. The object recognition have received even more attention due to very high interest in computer vision from the industry.…”

Section: Introductionmentioning

confidence: 99%

Reasoning and Algorithm Selection Augmented Symbolic Segmentation

Lukáč¹,

Abdiyeva²,

Kameyama³

2016

Preprint

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In this paper we present an alternative method to symbolic segmentation: we approach symbolic segmentation as an algorithm selection problem. That is, let there be a set A of available algorithms for symbolic segmentation, a set of input features F , a set of image attribute A and a selection mechanism S(F, A, A) that selects on a case by case basis the best algorithm. The semantic segmentation is then an optimization process that combines best component segments from multiple results into a single optimal result. The experiments compare three different algorithm selection mechanisms using three selected semantic segmentation algorithms. The results show that using the current state of art algorithms and relatively low accuracy of algorithm selection the accuracy of the semantic segmentation can be improved by 2%.

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Fuzzy C-Means Based Liver CT Image Segmentation with Optimum Number of Clusters

Cited by 10 publications

References 16 publications

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

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

Symbolic Segmentation Using Algorithm Selection

Reasoning and Algorithm Selection Augmented Symbolic Segmentation

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