Fuzzy Techniques in Mammographic Image Processing

Rick, Andreas; Bothorel, Sylvie; Bouchon‐Meunier, Bernadette; Muller, Serge; Rifqi, Maria

doi:10.1007/978-3-7908-1847-5_12

Cited by 11 publications

(4 citation statements)

References 18 publications

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“…The notions of typicality degrees and prototypes have been exploited for machine learning, both in supervised [23] and unsupervised settings [17]. In the latter case, the underlying principle consists in assigning points to data subsets so that they have high typicality degrees for the subset they are assigned to.…”

Section: B Prototypes and Typicality Degreesmentioning

confidence: 99%

Subspace Clustering and Feature Typicality Degrees: a Prospective Study

Lesot¹,

d’Allonnes

2020

2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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Subspace clustering can offer, beside a decomposition of data into homogeneous and distinct clusters, a characterisation of the subspaces in which the clusters live. This paper explores the possibility of capturing the notion of characteristic features in the framework of typicality degrees, as typical features. To that aim, it discusses the notion of typicality degrees for features and proposes an Alternating Cluster Estimation algorithm, named TbSC, to exploit these degrees within subspace clustering. It illustrates their differences experimentally using simple data sets.

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Section: B Prototypes and Typicality Degreesmentioning

confidence: 99%

Subspace Clustering and Feature Typicality Degrees: a Prospective Study

Lesot¹,

d’Allonnes

2020

2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

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“…Besides Grim's et al proposal, although using feature vectors different to local neighborhood gray levels, gaussian mixture models have been used in mammography to segment breast tissue, cancer risk assessment, and segmentation of suspicious regions [6] and earlier discussions of applications in adjustment of univariate gray level histograms and shape analysis of potentially malignant lesions [7].…”

Section: Introductionmentioning

confidence: 99%

Efficient implementation of the EM algorithm for mammographic image texture analysis with multivariate Gaussian mixtures

Gallego-Ortiz

Femandez-Mc-Cann²

2011

2011 IEEE Statistical Signal Processing Workshop (SSP)

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In this paper we present an efficient implementation of the EM algorithm for estimating multivariate gaussian mixture model parameters in the context of local-neighborhood image texture analysis. We illustrate its application in a study case of mass detection in mammography, providing a detailed description of a feasible and efficient implementation. Our proposed method overcomes numerical variable underflow problems by means of logarithmic and exponential manipulations and saves computational time using a look up table approach. We reduced computation time to 57.14% with respect to direct computation, achieving numerical conditions for convergence.

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“…This segmentation is also realized thanks to a fuzzy representation of imprecision and uncertainty (more details can be found in [28]). Each microcalcification is then described by means of 5 fuzzy attributes computed from its fuzzy contour.…”

Section: Application To Mammographymentioning

confidence: 99%

Fuzzy Prototypes: From a Cognitive View to a Machine Learning Principle

Lesot¹,

Rifqi²,

Bouchon‐Meunier³

Fuzzy Sets and Their Extensions: Representation, Aggregation and Models

Self Cite

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Abstract. Cognitive psychology works have shown that the cognitive representation of categories is based on a typicality notion: all objects of a category do not have the same representativeness, some are more characteristic or more typical than others, and better exemplify their category. Categories are then defined in terms of prototypes, i.e. in terms of their most typical elements. Furthermore, these works showed that an object is all the more typical of its category as it shares many features with the other members of the category and few features with the members of other categories.In this paper, we propose to profit from these principles in a machine learning framework: a formalization of the previous cognitive notions is presented, leading to a prototype building method that makes it possible to characterize data sets taking into account both common and discriminative features. Algorithms exploiting these prototypes to perform tasks such as classification or clustering are then presented.The formalization is based on the computation of typicality degrees that measure the representativeness of each data point. These typicality degrees are then exploited to define fuzzy prototypes: in adequacy with human-like description of categories, we consider a prototype as an intrinsically imprecise notion. The fuzzy logic framework makes it possible to model sets with unsharp boundaries or vague and approximate concepts, and appears most appropriate to model prototypes.We then exploit the computed typicality degrees and the built fuzzy prototypes to perform machine learning tasks such as classification and clustering. We present several algorithms, justifying in each case the chosen parameters. We illustrate the results obtained on several data sets corresponding both to crisp and fuzzy data.

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Fuzzy Techniques in Mammographic Image Processing

Cited by 11 publications

References 18 publications

Subspace Clustering and Feature Typicality Degrees: a Prospective Study

Subspace Clustering and Feature Typicality Degrees: a Prospective Study

Efficient implementation of the EM algorithm for mammographic image texture analysis with multivariate Gaussian mixtures

Fuzzy Prototypes: From a Cognitive View to a Machine Learning Principle

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