An optimized SVM based possibilistic fuzzy c-means clustering algorithm for tumor segmentation

Kollem, Sreedhar; Reddy, Katta Rama Linga; Rao, D. Srinivasa

doi:10.1007/s11042-020-09675-y

Cited by 29 publications

(7 citation statements)

References 32 publications

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“…Under the new economic normal, China's industrial structure adjustment and upgrading has also entered the “fast lane” of development [ 1 ]. In the new century, China's industrial structure adjustment and upgrading has achieved remarkable results, and the proportion among the three industries is becoming more and more reasonable.…”

Section: Introductionmentioning

confidence: 99%

Selection and Optimization of Regional Economic Industrial Structure Based on Fuzzy k-Means Clustering Algorithm

Yuan¹,

Gao

2022

Computational Intelligence and Neuroscience

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Learning about the regional business model is essential for the sustainable development of the regional economy. From the perspective of urban renewable energy, city A is the product of energy development. This paper analyzes the current situation and existing problems of the industrial model of city A through fuzzy k-means clustering algorithm. The results show that although the optimization of industrial structure in city A has achieved some results, the more intuitive problems mainly include low labor productivity of the primary industry, strong resource dependence, insufficient extension of industrial chain, and slow development of technology intensive industries. This paper uses fuzzy k-means clustering algorithm to select the leading industries from the perspective of the current situation of leading industries, urban development pattern, and regional policies in city A. The results show that, as a renewable resource-based city, the leading industries suitable for the current development of city A include manufacturing, power, alkali gas and water production and supply, transportation, warehousing and postal industry, leasing, and business services. The results of fuzzy k-means clustering algorithm are quite excellent, and the accuracy rate is 93.3%. This paper uses the grey dynamic linear programming model to predict the future development of the Urban A business model and combines the selection of key functions to obtain the best business model: deep and efficient technical equipment as a good goal, achieved through regional logistics, transportation, new services, etc., to enhance the output value of the tertiary industry in city A and optimize the internal structure of the secondary industry in city A.

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Section: Introductionmentioning

confidence: 99%

Selection and Optimization of Regional Economic Industrial Structure Based on Fuzzy k-Means Clustering Algorithm

Yuan¹,

Gao

2022

Computational Intelligence and Neuroscience

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“…Differential (partial and ordinary) equation model approach is effective in a wide range of applications ranging from up-to-date texture image classification [ 42 , 43 ] (SVM) to data-driven and physics-informed neural networks (PINNs) involving inverse problems [ 50 ] and machine learning for fluid mechanics [ 8 ]. In [ 77 ], the authors propose a new physics-based deformation method and efficient character that uses PDE surfaces for an improved modeling framework for creation of detailed 3D virtual geometric models of characters.…”

Section: Related Workmentioning

confidence: 99%

“…In [12] is presented a fractional-order differential equation for modeling of COVID-19 infection of epithelial cells. We refer the interested reader to [1,6,8,26,42,43,45,50,70,77] and the references cited therein for a survey on the partial differential equation modeling approaches along with several fine tuning tools as such as physics-informed, physicallybased simulation, data-driven method and data-driven enrichment for state-of-the-art ideas on development methods of multimedia applications involving differential equation as a crucial tool.…”

Section: Related Workmentioning

confidence: 99%

“…In [ 42 ], the authors proposed an improved PDE based total variation model that enhances grey and colored brain tumor images obtained by magnetic resonance imaging. In addition, in [ 43 ] the authors introduced an optimized support vector machine (SVM) based possibilistic fuzzy c-means clustering algorithm for tumor segmentation supported by an efficient partial differential equation modeling. On the other hand, image classification methods have achieved remarkable success in diverse applications.…”

Section: Introductionmentioning

confidence: 99%

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Texture image classification based on a pseudo-parabolic diffusion model

Vieira

Abreu

Florindo

2022

Multimed Tools Appl

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This work proposes a novel method based on a pseudo-parabolic diffusion process to be employed for texture recognition. The proposed operator is applied over a range of time scales giving rise to a family of images transformed by nonlinear filters. Therefore each of those images are encoded by a local descriptor (we use local binary patterns for that purpose) and they are summarized by a simple histogram, yielding in this way the image feature vector. Three main novelties are presented in this manuscript: (1) The introduction of a pseudo-parabolic model associated with the signal component of binary patterns to the process of texture recognition and a real-world application to the problem of identifying plant species based on the leaf surface image. (2) We also introduce a simple and efficient discrete pseudo-parabolic differential operator based on finite differences as texture descriptors. While the work in [ 26 ] uses complete local binary patterns, here we use the original version of the local binary pattern operator. (3) We also discuss, in more general terms, the possibilities of exploring pseudo-parabolic models for image analysis as they balance two types of processing that are fundamental for pattern recognition, i.e., they smooth undesirable details (possibly noise) at the same time that highlight relevant borders and discontinuities anisotropically. Besides the practical application, the proposed approach is also tested on the classification of well established benchmark texture databases. In both cases, it is compared with several state-of-the-art methodologies employed for texture recognition. Our proposal outperforms those methods in terms of classification accuracy, confirming its competitiveness. The good performance can be justified to a large extent by the ability of the pseudo-parabolic operator to smooth possibly noisy details inside homogeneous regions of the image at the same time that it preserves discontinuities that convey critical information for the object description. Such results also confirm that model-based approaches like the proposed one can still be competitive with the omnipresent learning-based approaches, especially when the user does not have access to a powerful computational structure and a large amount of labeled data for training.

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“…If the threshold is selected too high, then many target pixels will be misjudged as the background of the image, if the threshold is selected too low, the opposite will happen. At present, there are many techniques for threshold determination in images, such as global threshold segmentation, 16 twodimensional maximum entropy segmentation, 17 interclass variance threshold segmentation, 18 co-occurrence matrix threshold segmentation, 19 fuzzy threshold segmentation, 20 and so on.…”

Section: Global Threshold Segmentationmentioning

confidence: 99%

Research on automatic detection technology for the compensation hole shape and position parameters of a brake master cylinder

Zhao

Ding

Cao

et al. 2021

Advances in Mechanical Engineering

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Machine vision is a key technology to achieve high detection accuracy for the compensation hole parameters of automobile brake master cylinders, which influence automobile safety and parking reliability. As an important part of the automobile brake master cylinder, the compensation hole can play an important role in regulating the brake fluid in the reservoir tank and brake chamber, and its dimensional accuracy and processing quality are strictly controlled. Therefore, determining how to accurately obtain images of the compensation hole is a primary problem in the detection of compensation hole parameters. In this paper, fully automatic equipment for compensation hole detection in automobile brake master cylinders is designed using an image processing algorithm to realize the automatic positioning of the compensation hole and automatic detection of size parameters. Experiments show that the automatic positioning and detection time for the compensation hole is less than 8 s, the detection accuracy of the compensation hole size is higher than ±0.021 mm, and the position detection accuracy for the compensation hole is higher than ±0.045 mm. The compensation hole detection technology proposed in this paper provides high real-time performance and good robustness while meeting accuracy requirements and detection speed.

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An optimized SVM based possibilistic fuzzy c-means clustering algorithm for tumor segmentation

Cited by 29 publications

References 32 publications

Selection and Optimization of Regional Economic Industrial Structure Based on Fuzzy k-Means Clustering Algorithm

Selection and Optimization of Regional Economic Industrial Structure Based on Fuzzy k-Means Clustering Algorithm

Texture image classification based on a pseudo-parabolic diffusion model

Research on automatic detection technology for the compensation hole shape and position parameters of a brake master cylinder

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