Histogram Constraint Based Fast FCM Cluster Image Segmentation

Tian, Junwei; Huang, Yih

doi:10.1109/isie.2007.4374847

Cited by 12 publications

(4 citation statements)

References 25 publications

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“…In essence, the classification of each pixel value is achieved through an iterative objective function [ 3 , 4 ]. Szilágyi et al [ 5 ] proposed a multiple FCM cascade algorithms model, but this model is trained and tested only on a limited data set, with a general degree of generalization; in addition, there is an improved method based on FCM for brain tumor image segmentation [ 6 , 7 ]. The threshold segmentation algorithm uses image gray value as a similarity measure, which can be categorized into the global threshold and local threshold.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of MRI of Brain Tumor Using Deep Convolutional Network

Zhou

et al. 2022

BioMed Research International

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Computer-aided diagnosis and treatment of multimodal magnetic resonance imaging (MRI) brain tumor image segmentation has always been a hot and significant topic in the field of medical image processing. Multimodal MRI brain tumor image segmentation utilizes the characteristics of each modal in the MRI image to segment the entire tumor and tumor core area and enhanced them from normal brain tissues. However, the grayscale similarity between brain tissues in various MRI images is very immense making it difficult to deal with the segmentation of multimodal MRI brain tumor images through traditional algorithms. Therefore, we employ the deep learning method as a tool to make full use of the complementary feature information between the multimodalities and instigate the following research: (i) build a network model suitable for brain tumor segmentation tasks based on the fully convolutional neural network framework and (ii) adopting an end-to-end training method, using two-dimensional slices of MRI images as network input data. The problem of unbalanced categories in various brain tumor image data is overcome by introducing the Dice loss function into the network to calculate the network training loss; at the same time, parallel Dice loss is proposed to further improve the substructure segmentation effect. We proposed a cascaded network model based on a fully convolutional neural network to improve the tumor core area and enhance the segmentation accuracy of the tumor area and achieve good prediction results for the substructure segmentation on the BraTS 2017 data set.

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Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of MRI of Brain Tumor Using Deep Convolutional Network

Zhou

et al. 2022

BioMed Research International

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“…(1) Firstly, the proposed method uses the fuzzy c-means (FCM) segmentation algorithm [29] to get the mask binary image and then extract the GTFM of the finger vein image by the vertical phase difference coding method proposed in Section II.…”

Section: The Overall Flow Of Proposed Methodsmentioning

confidence: 99%

“…The known regions and the unknown regions can be determined by the mask binary image, in which the value of pixels in the known region is 1, while that in the unknown region is 0. And the mask binary image can be obtained by the fuzzy c-means (FCM) segmentation algorithm [29]. GTFM B ε (p)| known represents the Gabor texture feature of all known pixels in B ε (p).…”

Section: B Determination and Update Of The Main Texture Orientationmentioning

confidence: 99%

Finger Vein Image Inpainting With Gabor Texture Constraints

et al. 2020

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The texture edge continuity of a finger vein image is very important for the accuracy of feature extraction. However, the traditional inpainting methods which, without accurate texture constraints, are easy to cause the vein texture of the inpainted image to be blurred and break. A finger vein image inpainting method with Gabor texture constraints is proposed. The proposed method effectively protects the texture edge continuity of the inpainted image. Firstly, using the proposed vertical phase difference coding method, the Gabor texture feature matrix of the finger vein image, which can accurately describe the texture information, can be extracted from the Gabor filtering responses. Then, according to the local texture continuity of the finger vein image, the known pixels, which have different texture orientations with the center pixel in the patch, are filtered out using the Gabor texture constraining mechanism during the inpainting process. The proposed method eliminates irrelevant information interference in the inpainting process and has a more precise texture propagation. Simulation experiments of artificially synthetic images and acquired images show that the finger vein images inpainted by the proposed method have better texture continuity and higher image quality than the traditional methods which do not have accurate texture constraints. The proposed method improves the recognition performance of the finger vein identification system with the acquired damaged images.INDEX TERMS Finger vein image, Gabor filter, image inpainting, texture feature, vertical phase difference coding.HUAXIA WANG (Member, IEEE) received the B.Eng. degree in information engineering from

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“…Laia and Liaw (Lai and Liaw, 2008) proposed a modified k-Means algorithm to speed up the clustering process for larger data sets with higher dimension. Many other partitioning-based methods were proposed to achieve a faster execution (Wang and Rau, 2001;Junwei and Yongxuan, 2007), but also for a more robust and less noise-sensitive clustering (Liew et al, 2000;Chang and Yeh, 2005;Li et al, 2007;Awad et al, 2009).…”

Section: Related Workmentioning

confidence: 99%

Swarm intelligence: novel tools for optimization, feature extraction, and multi-agent system modeling

Jevtić¹

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Animal swarms in nature are able to adapt to dynamic changes in their environment, and through cooperation they can solve problems that are crucial for their survival. Only by means of local interactions with other members of the swarm and with the environment, they can achieve a common goal more efficiently than it would be done by a single individual. This problem-solving behavior that results from the multiplicity of such interactions is referred to as Swarm Intelligence. The mathematical models of swarming behavior in nature were initially proposed to solve optimization problems. Nevertheless, this decentralized approach can be a valuable tool for a variety of applications, where emerging global patterns represent a solution to the task at hand. Methods for the solution of difficult computational problems based on Swarm Intelligence have been experimentally demonstrated and reported in the literature. However, a general framework that would facilitate their design does not exist yet.In this dissertation, a new general design methodology for Swarm Intelligence tools is proposed. By defining a discrete space in which the members of the swarm can move, and by modifying the rules of local interactions and setting the adequate objective function for solutions evaluation, the proposed methodology is tested in various domains. The dissertation presents a set of case studies, and focuses on two general approaches. One approach is to apply Swarm Intelligence as a tool for optimization and feature extraction, and the other approach is to model multi-agent systems such that they resemble swarms of animals in nature providing them with the ability to autonomously perform a task at hand.Artificial swarms are designed to be autonomous, scalable, robust, and adaptive to the changes in their environment. In this work, the methods that exploit one or more of these features are presented. First, the proposed methodology is validated in a real-world scenario seen as a combinatorial optimization problem. Then a set of novel tools for feature extraction, more precisely the adaptive edge detection and the broken-edge linking in digital images is proposed. A novel data clustering algorithm is also proposed and applied to image segmentation. Finally, a scalable algorithm based on the proposed methodology is developed for distributed task allocation in multi-agent systems, and applied to a swarm of robots. The newly proposed general methodology provides a guideline for future developers of the Swarm Intelligence tools.During the years of my Ph.D. studies, many people have formed an important part of my life and to all of them I wish to express my sincerest gratitude for the support they have given me.I thank my advisor Professor Diego Andina de la Fuente for the opportunity to be a part of the Group for Automation in Signal and Communications. I am grateful for his guidance and his continuing support, and for allowing me to choose the research topic of my interest. In his words: "It is important that you choose a topic that interests...

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Histogram Constraint Based Fast FCM Cluster Image Segmentation

Cited by 12 publications

References 25 publications

Automatic Segmentation of MRI of Brain Tumor Using Deep Convolutional Network

Automatic Segmentation of MRI of Brain Tumor Using Deep Convolutional Network

Finger Vein Image Inpainting With Gabor Texture Constraints

Swarm intelligence: novel tools for optimization, feature extraction, and multi-agent system modeling

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