A picture fuzzy clustering approach for brain tumor segmentation

Kumar, S. V. Aruna; Harish, B. S.; Aradhya, V. N. Manjunath

doi:10.1109/ccip.2016.7802852

Cited by 19 publications

(9 citation statements)

References 17 publications

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“…In this paper, we present some classes of implication operators of picture fuzzy logic and a compositional rule of chai inference in a picture fuzzy logic setting. Some applications of the inference procedures were given in [14][15][16] and some new applications of the new fuzzy theory could be found in [12,13]. We present firstly the compositional rule of chain inference, giving a class of intelligent inference schema for complex computational intelligence problems.…”

Section: Discussionmentioning

confidence: 99%

Compositional Rule of Chain Inference in Computational Intelligence Problems

Cường¹,

Chien²,

Ha³

et al. 2017

Fair - Nghiên Cứu Cơ Bản Và Ứng Dụng Công Nghệ Thông Tin - 2017

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The incorporation of imprecise, linguistic information into logical deduction processes is a significant issue in computational intelligence. Throughout the literature, we can find all sorts of intelligent inference schemes acting under imprecision; common to most approaches is their reliance on if-then rules of the kind "IF X is A THEN Y is B", where A and B are fuzzy sets (FS) in given universes U and V. While the FS-based theory of approximate reasoning is surely a well-established and commonly applied one, there is still for further expanding the expressiveness of the formalism. One such improvement can be obtained by using picture fuzzy sets (PFS), in which the sets A and B are picture fuzzy sets in the corresponding universes U and V. In this paper, we will contribute to the further development of the picture fuzzy logic (PFL) by presenting some new classes of implication operators in PFL and firstly defining the Compositional Rule of Chain Inference (CRCI) in a PFL setting. The new chain inference procedures should be applied in computational intelligence problems

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Section: Discussionmentioning

confidence: 99%

Compositional Rule of Chain Inference in Computational Intelligence Problems

Cường¹,

Chien²,

Ha³

et al. 2017

Fair - Nghiên Cứu Cơ Bản Và Ứng Dụng Công Nghệ Thông Tin - 2017

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“…It gives precise results for clustering which has been proven through numerous researches recently [39,40,41,42,43,44,45]. FC-PFS showed significant roles in weather nowcasting from satellite image sequences [42], brain tumor segmentation [5], recommender systems [40], and stock prediction [39].…”

Section: Introductionmentioning

confidence: 93%

“…In the real case of voting applications, 'positive' refers to the support for a candidate, 'negative' in reverse shows the opposition, and 'neutral' reflects the hesistant group who do not agree and disagree. There are many other cases to demonstrate the usage and practical necessity of the PFS [5].…”

Section: Introductionmentioning

confidence: 99%

Theoretical Analysis of Picture Fuzzy Clustering: Convergence and Property

Phuong

Thong²,

Sơn³

2018

JCC

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Recently, picture fuzzy clustering (FC-PFS) has been introduced as a new computational intelligence tool for various problems in knowledge discovery and pattern recognition. However, an important question that was lacked in the related researches is examination of mathematical properties behind the picture fuzzy clustering algorithm such as the convergence, the boundary or the convergence rate, etc. In this paper, we will prove that FC-PFS converges to at least one local minimum. The similarities and differences between this algorithm and other clustering methods are compared. Analysis on the loss function is also considered.

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“…The new basic connectives in picture fuzzy logic on PFS firstly were presented in [11,25]. These new concepts are supporting to new computing procedures in computational intelligence problems and in other applications (see [17,18,19,20,21,22,23,24]).…”

Section: Introductionmentioning

confidence: 99%

Pythagorean Picture Fuzzy Sets, Part 1- basic notions

Cường

2019

JCC

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Picture fuzzy set (2013) is a generalization of the Zadeh‟ fuzzy set (1965) and the Antanassov‟intuitionistic fuzzy set. The new concept could be useful for many computational intelligentproblems. Basic operators of the picture fuzzy logic were studied by Cuong, Ngan [10,11 ].Newconcept –Pythagorean picture fuzzy set ( PPFS) is a combination of Picture fuzzy set with theYager‟s Pythagorean fuzzy set [12-14].First, in the Part 1 of this paper, we consider basic notionson PPFS as set operators of PPFS‟s , Pythagorean picture relation, Pythagorean picture fuzzy softset. Next, the Part 2 of the paper is devoted to main operators in fuzzy logic on PPFS: picturenegation operator, picture t-norm, picture t-conorm, picture implication operators on PPFS.As aresult we will have a new branch of the picture fuzzy set theory.

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A picture fuzzy clustering approach for brain tumor segmentation

Cited by 19 publications

References 17 publications

Compositional Rule of Chain Inference in Computational Intelligence Problems

Compositional Rule of Chain Inference in Computational Intelligence Problems

Theoretical Analysis of Picture Fuzzy Clustering: Convergence and Property

Pythagorean Picture Fuzzy Sets, Part 1- basic notions

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