A modified strategy of fuzzy clustering algorithm for image segmentation

Zhou, Dongguo; Zhou, Hong

doi:10.1007/s00500-014-1481-8

Cited by 18 publications

(5 citation statements)

References 36 publications

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“…If satisfies, end the loop and save the order L of access node, and move to the fifth step; otherwise it will go to the second step. The sixth step is to use the improved A* algorithm to give the path of adjacent nodes in L and merge into the complete path P and output [12].…”

Section: Batch Selection Path Planning For Warehousing and Logistics Robotsmentioning

confidence: 99%

Path Planning for Batch Picking of Warehousing and Logistics Robots Based on Modified A* Algorithm

Li-mei

2018

Int. J. Onl. Eng.

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<p class="0abstract"><span lang="EN-US">To make the picking path planning of warehousing and logistics robot in warehouse more efficient and real-time, a path planning for batch picking of warehousing and logistics robots based on the modified A* algorithm was put forward. First of all, the path planning of the single robot batch picking was realized. Then, the time cost was introduced to further improve the A* algorithm to realize the batch picking path planning of the multi-warehouse logistics robots. Finally, according to the algorithm proposed, .Net visual programming technology was applied to build a complete warehouse logistics robot picking path planning simulation system to verify the effectiveness of the algorithm proposed. The research results showed that the algorithm proposed could meet the needs of efficient and real-time path planning in the process of completing batch picking tasks. To sum up, it lays a solid foundation for the realization of intelligent and fully automated warehouse management.</span></p>

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Section: Batch Selection Path Planning For Warehousing and Logistics Robotsmentioning

confidence: 99%

Path Planning for Batch Picking of Warehousing and Logistics Robots Based on Modified A* Algorithm

Li-mei

2018

Int. J. Onl. Eng.

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“…FCM is one of the most widely used algorithms for image segmentation 11,12 and its success is mainly attributable to the F. 1.…”

Section: A1 Histogram Fuzzy C-means Clusteringmentioning

confidence: 99%

Generic and robust method for automatic segmentation of PET images using an active contour model

2016

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Purpose: Although positron emission tomography (PET) images have shown potential to improve the accuracy of targeting in radiation therapy planning and assessment of response to treatment, the boundaries of tumors are not easily distinguishable from surrounding normal tissue owing to the low spatial resolution and inherent noisy characteristics of PET images. The objective of this study is to develop a generic and robust method for automatic delineation of tumor volumes using an active contour model and to evaluate its performance using phantom and clinical studies. Methods: MASAC, a method for automatic segmentation using an active contour model, incorporates the histogram fuzzy C-means clustering, and localized and textural information to constrain the active contour to detect boundaries in an accurate and robust manner. Moreover, the lattice Boltzmann method is used as an alternative approach for solving the level set equation to make it faster and suitable for parallel programming. Twenty simulated phantom studies and 16 clinical studies, including six cases of pharyngolaryngeal squamous cell carcinoma and ten cases of nonsmall cell lung cancer, were included to evaluate its performance. Besides, the proposed method was also compared with the contourlet-based active contour algorithm (CAC) and Schaefer's thresholding method (ST). The relative volume error (RE), Dice similarity coefficient (DSC), and classification error (CE) metrics were used to analyze the results quantitatively. Results: For the simulated phantom studies (PSs), MASAC and CAC provide similar segmentations of the different lesions, while ST fails to achieve reliable results. For the clinical datasets (2 cases with connected high-uptake regions excluded) (CSs), CAC provides for the lowest mean RE (−8.38% ± 27.49%), while MASAC achieves the best mean DSC (0.71 ± 0.09) and mean CE (53.92% ± 12.65%), respectively. MASAC could reliably quantify different types of lesions assessed in this work with good accuracy, resulting in a mean RE of −13.35% ± 11.87% and −11.15% ± 23.66%, a mean DSC of 0.89 ± 0.05 and 0.71 ± 0.09, and a mean CE of 19.19% ± 7.89% and 53.92% ± 12.65%, for PSs and CSs, respectively. Conclusions:The authors' results demonstrate that the developed novel PET segmentation algorithm is applicable to various types of lesions in the authors' study and is capable of producing accurate and consistent target volume delineations, potentially resulting in reduced intraobserver and interobserver variabilities observed when using manual delineation and improved accuracy in treatment planning and outcome evaluation. C

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“…Due to various situations, for images, issues like small scale of spatial resolution, poor illumination, presence of noise, intensity imbrication leads crisp segmentation a hard task. Among numerous clustering techniques, fuzzy c-means (FCM) (Zhou and Zhou 2014) algorithm is more significant because of its robustness. Although it is robust it works only on the images without noise.…”

Section: Introductionmentioning

confidence: 99%

A new fuzzy clustering algorithm for the segmentation of brain tumor

2015

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This paper introduces a new method of clustering algorithm based on interval-valued intuitionistic fuzzy sets (IVIFSs) generated from intuitionistic fuzzy sets to analyze tumor in magnetic resonance (MR) images by reducing time complexity and errors. Based on fuzzy clustering, during the segmentation process one can consider numerous cases of uncertainty involving in membership function, distance measure, fuzzifier, and so on. Due to poor illumination of medical images, uncertainty emerges in their gray levels. This paper concentrates on uncertainty in the allotment of values to the membership function of the uncertain pixels. Proposed method initially pre-processes the brain MR images to remove noise, standardize intensity, and extract brain region. Subsequently IVIFSs are constructed to utilize in the clustering algorithm. Results are compared with the segmented images obtained using histogram thresholding, k-means, fuzzy c-means, intuitionistic fuzzy c-means, and interval type-2 fuzzy c-means algorithms and it has been proven that the proposed method is more effective.Keywords Brain MR image · Segmentation · Intervalvalued intuitionistic fuzzy set · Brain tumor · Clustering Communicated by V. Loia.

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A modified strategy of fuzzy clustering algorithm for image segmentation

Cited by 18 publications

References 36 publications

Path Planning for Batch Picking of Warehousing and Logistics Robots Based on Modified A* Algorithm

Path Planning for Batch Picking of Warehousing and Logistics Robots Based on Modified A* Algorithm

Generic and robust method for automatic segmentation of PET images using an active contour model

A new fuzzy clustering algorithm for the segmentation of brain tumor

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