Ignorance functions. An application to the calculation of the threshold in prostate ultrasound images

Bustince, Humberto; Pagola, Miguel; Barrenechea, Edurne; Fernández, Javier; Melo‐Pinto, Pedro; Couto, Pedro; Tizhoosh, Hamid R.; Montero, Javier

doi:10.1016/j.fss.2009.03.005

Cited by 91 publications

(65 citation statements)

References 22 publications

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“…Type-2 FS (specially IVFS) have also found extensive application in various fields (see for example [6]). …”

Section: Type-1 and Type-fuzzinessmentioning

confidence: 99%

“…Though their influence (more than considerable in fields as decision theory [14,17,26,29] or machine learning [19]) has spread separately, in the last few decades both notions have started to appear together in many developments on these and other fields (see for instance [4,6,13,15,30,31,38]), which comes to show its high relevance as a topic of research inside soft computing [20] and logics [36].…”

Section: Introductionmentioning

confidence: 99%

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On the Semantics of Bipolarity and Fuzziness

Rodríguez

Franco

Montero

2011

Advances in Intelligent and Soft Computing

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This paper analyzes the relationship between fuzziness and bipolarity, notions which were devised to address different kinds of uncertainty: linguistic imprecision, in the former, and knowledge relevance and character or polarity, in the latter. Although different types of fuzziness and bipolarity have been defined, these relations are not always clear. This paper proposes the use of four-valued extensions to provide a formal method to rigorously define and compare the semantics and logical structure of diverse combinations of fuzziness and bipolarity types. As a result, this paper claims that these notions and their different types are independent and not semantically equivalent despite its possible formal equivalence.

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“…Type-2 FS (specially IVFS) have also found extensive application in various fields (see for example [6]). …”

Section: Type-1 and Type-fuzzinessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On the Semantics of Bipolarity and Fuzziness

Rodríguez

Franco

Montero

2011

Advances in Intelligent and Soft Computing

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“…The weak ignorance concept is defined in [15] as a measure of the lack of knowledge that the expert suffers when assigning a numerical value to the membership of an element to a fuzzy set. …”

Section: Weak Ignorance Functionsmentioning

confidence: 99%

Weighted Voting Method Using Atanassov's Intuitionistic Fuzzy Preference Relations and Ignorance Functions

Paternain¹,

Jurío²,

Barrenechea³

et al. 2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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In this work we present a method for constructing an Atanassov's intuitionistic fuzzy preference relation from a fuzzy preference relation that takes into account the ignorance of the expert when evaluating the preferences in the latter. Moreover, we present two algorithms that make use of this method for extending the weighted voting method to the Atanassov's intuitionistic fuzzy setting in decision making problems.

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“…Type II fuzzy set also has an ability to get more additional information in thresholding [9]. The robustness of this method has been proved in [10] when calculating threshold in prostate ultrasound images. These arguments show that ultrafuzziness is superior to conduct image thresholding.…”

Section: Introductionmentioning

confidence: 98%

Ultrafuzziness Optimization Based on Type II Fuzzy Sets for Image Thresholding

Arifin¹,

Heddyanna²,

Studiawan³

2010

itbj.ict.

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Abstract. Image thresholding is one of the processing techniques to provide high quality preprocessed image. Image vagueness and bad illumination are common obstacles yielding in a poor image thresholding output. By assuming image as fuzzy sets, several different fuzzy thresholding techniques have been proposed to remove these obstacles during threshold selection. In this paper, we proposed an algorithm for thresholding image using ultrafuzziness optimization to decrease uncertainty in fuzzy system by common fuzzy sets like type II fuzzy sets. Optimization was conducted by involving ultrafuzziness measurement for background and object fuzzy sets separately. Experimental results demonstrated that the proposed image thresholding method had good performances for images with high vagueness, low level contrast, and grayscale ambiguity.

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Ignorance functions. An application to the calculation of the threshold in prostate ultrasound images

Cited by 91 publications

References 22 publications

On the Semantics of Bipolarity and Fuzziness

On the Semantics of Bipolarity and Fuzziness

Weighted Voting Method Using Atanassov's Intuitionistic Fuzzy Preference Relations and Ignorance Functions

Ultrafuzziness Optimization Based on Type II Fuzzy Sets for Image Thresholding

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