Detection of Spiculated Masses in Mammograms Based on Fuzzy Image Processing

Hassanien, Aboul Ella; Ali, Jafar; Nobuhara, Hajime

doi:10.1007/978-3-540-24844-6_156

Cited by 28 publications

(12 citation statements)

References 4 publications

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“…[4] presented a rough set approach to feature reduction and generation of classification rules from a set of medical datasets. They introduced a rough set reduction technique to find all reducts of the data that contain the minimal subset of features associated with a class label for classification.…”

Section: Rough Sets In Medical Data Miningmentioning

confidence: 99%

Rough Sets in Medical Informatics Applications

Hassanien

Abraham

Peters

et al. 2009

Advances in Intelligent and Soft Computing

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Rough sets offer an effective approach of managing uncertainties and can be employed for tasks such as data dependency analysis, feature identification, dimensionality reduction, and pattern classification. As these tasks are common in many medical applications it is only natural that rough sets, despite their relative 'youth' compared to other techniques, provide a suitable method in such applications. In this paper, we provide a short summary on the use of rough sets in the medical informatics domain, focussing on applications of medical image segmentation, pattern classification and computer assisted medical decision making.

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Section: Rough Sets In Medical Data Miningmentioning

confidence: 99%

Rough Sets in Medical Informatics Applications

Hassanien

Abraham

Peters

et al. 2009

Advances in Intelligent and Soft Computing

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“…Fuzzy techniques can be applied to different phases of the segmentation process; additionally, fuzzy logic allows to represent the knowledge about the given problem in terms of linguistic rules with meaningful variables, which is the most natural way to express and interpret information. Fuzzy image processing [10,68,73,102,112] is the collection of all approaches that understand, represent and process the images, their segments and features as fuzzy sets. An image I of size M ×N and L gray levels can be considered as an array of fuzzy singletons, each having a value of membership denoting its degree of brightness relative to some brightness levels.…”

Section: Fuzzy Image Processingmentioning

confidence: 99%

“…5. [10] • The coding of image data (fuzzifier), which translates gray-level plane to the membership plane • An inference engine that applies a fuzzy reasoning mechanism to obtain a fuzzy output • Decoding the result of fuzzification (defuzzifier), which translates this latter output into a gray-level plane; and • Knowledge base, which contains both an ensemble of fuzzy rules, known as the rule base, and an ensemble of membership functions known as the database…”

Section: Fuzzy Image Processingmentioning

confidence: 99%

Computational Intelligence in Multimedia Processing: Foundation and Trends

Hassanien

Abraham²,

Kacprzyk³

et al. 2008

Studies in Computational Intelligence

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Summary. This chapter presents a broad overview of Computational Intelligence (CI) techniques including Neural Network (NN), Particle Swarm Optimization (PSO), Evolutionary Algorithm (GA), Fuzzy Set (FS), and Rough Sets (RS). In addition, a very brief introduction to near sets and near images which offer a generalization of traditional rough set theory and a new approach to classifying perceptual objects by means of features in solving multimedia problems is presented. A review of the current literature on CI based approaches to various problems in multimedia computing such as speech, audio and image processing, video watermarking, content-based multimedia indexing and retrieval are presented. We discuss some representative methods to provide inspiring examples to illustrate how CI could be applied to resolve multimedia computing problems and how multimedia could be analyzed, processed, and characterized by computational intelligence. Challenges to be addressed and future directions of research are also presented.

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“…Examples are medical images where specialists are interested in areas corresponding to a tumor or another disease [6], [11], [10]. In those applications some agent, usually an expert user, indicates what region of the image is of interest (commonly specified by one pixel that, in the user's perception, is representative of the region, called a seed).…”

Section: Introductionmentioning

confidence: 99%

On Significant Crisp Representatives of Fuzzy Regions in Colour Images

Prados-Suárez

Sánchez

Chamorro-Martihez

2007

2007 IEEE International Fuzzy Systems Conference

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Abstract-In this paper we propose a technique to obtain a set of crisp representatives of an imprecise region in color images. In our approach, we start by obtaining a fuzzy model of the region by employing a fuzzy region growing procedure. The set of crisp representatives is obtained from that model as the set of α-cuts corresponding to maximal variations of homogeneity. Several experiments and a comparison with both crisp and fuzzy techniques are provided, showing the suitability of the proposal against other approaches.

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Detection of Spiculated Masses in Mammograms Based on Fuzzy Image Processing

Cited by 28 publications

References 4 publications

Rough Sets in Medical Informatics Applications

Rough Sets in Medical Informatics Applications

Computational Intelligence in Multimedia Processing: Foundation and Trends

On Significant Crisp Representatives of Fuzzy Regions in Colour Images

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