Automatic multi-thresholds selection for image segmentation based on evolutionary approach

Truong, Quoc Bao; Lee, Byung Ryong

doi:10.1007/s12555-011-0055-0

Cited by 13 publications

(4 citation statements)

References 9 publications

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“…The performance of our proposed computer segmentation method was evaluated by the similarity between the binary mask images generated by the automatic segmentation and the manual ground truth segmentation by the radiologist, represented by Dice coefficient (27): where A1 and A2 represent binary mask images from the proposed method and manual segmentation, respectively. DI > 0.7 indicates a high similarity between the two segmentation results (28).…”

Section: Methodsmentioning

confidence: 99%

Computer-aided pancreas segmentation based on 3D GRE Dixon MRI: a feasibility study

Gong

Yang

et al. 2019

Acta Radiologica Open

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Background Pancreas segmentation is of great significance for pancreatic cancer radiotherapy positioning, pancreatic structure, and function evaluation. Purpose To investigate the feasibility of computer-aided pancreas segmentation based on optimized three-dimensional (3D) Dixon magnetic resonance imaging (MRI). Material and Methods Seventeen healthy volunteers (13 men, 4 women; mean age = 53.4 ± 13.2 years; age range = 28–76 years) underwent routine and optimized 3D gradient echo (GRE) Dixon MRI at 3.0 T. The computer-aided segmentation of the pancreas was executed by the Medical Imaging Interaction ToolKit (MITK) with the traditional segmentation algorithm pipeline (a threshold method and a morphological method) on the opposed-phase and water images of Dixon. The performances of our proposed computer segmentation method were evaluated by Dice coefficients and two-dimensional (2D)/3D visualization figures, which were compared for the opposed-phase and water images of routine and optimized Dixon sequences. Results The dice coefficients of the computer-aided pancreas segmentation were 0.633 ± 0.080 and 0.716 ± 0.033 for opposed-phase and water images of routine Dixon MRI, respectively, while they were 0.415 ± 0.143 and 0.779 ± 0.048 for the optimized Dixon MRI, respectively. The Dice index was significantly higher based on the water images of optimized Dixon than those in the other three groups (all P values < 0.001), including water images of routine Dixon MRI and both of the opposed-phase images of routine and optimized Dixon sequences. Conclusion Computer-aided pancreas segmentation based on Dixon MRI is feasible. The water images of optimized Dixon obtained the best similarity with a good stability.

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Section: Methodsmentioning

confidence: 99%

Computer-aided pancreas segmentation based on 3D GRE Dixon MRI: a feasibility study

Gong

Yang

et al. 2019

Acta Radiologica Open

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“…In our work, image threshold was adopted for pancreas image segmentation. Because of its intuitive properties and simplicity of implementation, image threshold plays an important role in image segmentation [8][9][10]. Its basic objective is to divide a given image into two classes: foreground and background.…”

Section: B Segmentation Methodsmentioning

confidence: 99%

Threshold algorithm for pancreas segmentation in Dixon water magnetic resonance images

Shan

Chen

et al. 2017

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

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Pancreas segmentation is crucial for a computer-aided diagnosis (CAD) system to provide cancer detection and radiation therapy of pancreatic cancer. Because of anatomically high-variability between subjects, achieving high accuracies in pancreas segmentation remains a challenging task. In this work, based on Otsu threshold method and morphological method, we first proposed a segmentation pipeline for pancreas, using Dixon water magnetic resonance image (MRI) data from five healthy volunteers. The threshold method was used to obtain the approximate outline of the pancreas, and the morphological method was used to separate the pancreas from the surrounding tissues. The segmentation results were compared with manual contours using Dice Index (DI) and we achieved DI: 0.80 ± 0.08 which was better than the level Set Methods (LSMs) DI: 0.64 ± 0.08. The proposed method was simple and easy to integrate with the Medical Imaging Interaction Toolkit (MITK) workbench, so it provided an efficient and simple segmentation method for processing large clinical datasets.

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“…The algorithm may predictably not produce satisfactory results in large clusters because it was not extended to segment images beyond three clusters. Metaheuristic methods, such as Genetic Algorithm (GA), Artificial Bee Colony (ABC), Differential Evolution (DE), electromagnetism optimization (EMO), Bat Algorithm (BA), particle swarm optimization (PSO), Darwinian PSO (DPSO), and Fractional-Order DPSO (FODPSO) [1,7,17,[32][33][34][35][36], have also been applied for multilevel thresholding. One of the best algorithms known amongst these metaheuristic algorithms is the PSO.…”

Section: Relevant Literaturementioning

confidence: 99%

Pixel Intensity Clustering Algorithm for Multilevel Image Segmentation

Olugbara

Adetiba

Oyewole

2015

Mathematical Problems in Engineering

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Image segmentation is an important problem that has received significant attention in the literature. Over the last few decades, a lot of algorithms were developed to solve image segmentation problem; prominent amongst these are the thresholding algorithms. However, the computational time complexity of thresholding exponentially increases with increasing number of desired thresholds. A wealth of alternative algorithms, notably those based on particle swarm optimization and evolutionary metaheuristics, were proposed to tackle the intrinsic challenges of thresholding. In codicil, clustering based algorithms were developed as multidimensional extensions of thresholding. While these algorithms have demonstrated successful results for fewer thresholds, their computational costs for a large number of thresholds are still a limiting factor. We propose a new clustering algorithm based on linear partitioning of the pixel intensity set and between-cluster variance criterion function for multilevel image segmentation. The results of testing the proposed algorithm on real images from Berkeley Segmentation Dataset and Benchmark show that the algorithm is comparable with state-of-the-art multilevel segmentation algorithms and consistently produces high quality results. The attractive properties of the algorithm are its simplicity, generalization to a large number of clusters, and computational cost effectiveness.

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Automatic multi-thresholds selection for image segmentation based on evolutionary approach

Cited by 13 publications

References 9 publications

Computer-aided pancreas segmentation based on 3D GRE Dixon MRI: a feasibility study

Computer-aided pancreas segmentation based on 3D GRE Dixon MRI: a feasibility study

Threshold algorithm for pancreas segmentation in Dixon water magnetic resonance images

Pixel Intensity Clustering Algorithm for Multilevel Image Segmentation

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