Fuzzy theoretic model based analysis of image features

Kumar, Mohit; Chatterjee, Subhankar; Zhang, Weiping; Yang, Jinmeng; Kolbe, Lutz M.

doi:10.1016/j.ins.2018.12.024

Cited by 7 publications

(2 citation statements)

References 29 publications

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“…Our approach to the validation of the output produced by any binary image segmentation method is based on statistical modeling; hence the term statistical validation is used 18 . Some approaches to validation (like 19 ) are aimed at defining membership functions based on image descriptors in an alternative to the classical histogram-based image descriptors. Likewise, statistical validation is carried out using a classification experiment whose results are evaluated through a coherence index enabling us to check for the quality of the binary segmentation outcome 8 .…”

Section: Binary Thresholding and Assessing Its Quality Via Statistica...mentioning

confidence: 99%

PARSEG: a computationally efficient approach for statistical validation of botanical seeds’ images

Frigau,

Conversano,

Antoch

2024

Sci Rep

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Human recognition and automated image validation are the most widely used approaches to validate the output of binary segmentation methods but, as the number of pixels in an image easily exceeds several million, they become highly demanding from both practical and computational standpoint. We propose a method, called PARSEG, which stands for PArtitioning, Random Selection, Estimation, and Generalization; being the basic steps within this procedure. Suggested method enables us to perform statistical validation of binary images by selecting the minimum number of pixels from the original image to be used for validation without deteriorating the effectiveness of the validation procedure. It utilizes binary classifiers to accomplish image validation and selects the optimal sample of pixels according to a specific objective function. As a result, the computational complexity of the validation experiment is substantially reduced. The procedure’s effectiveness is illustrated by considering images composed of approximately 13 million pixels from the field of seed recognition. PARSEG provides roughly the same precision of the validation process when extended to the entire image, but it utilizes only about 4% of the original number of pixels, thus reducing, by about 90%, the computing time required to validate a binary segmented image.

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Section: Binary Thresholding and Assessing Its Quality Via Statistica...mentioning

confidence: 99%

PARSEG: a computationally efficient approach for statistical validation of botanical seeds’ images

Frigau,

Conversano,

Antoch

2024

Sci Rep

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“…In order to reduce the noise inherent in these systems and improve diagnostic accuracy, fuzzy learning strategies obtain specific inherent logic of humans, and have been established [3], [4], for example, towards image processing [5], image classification [6], and motor control [7]. Researchers have engaged in developing some new neural networks with inherent and embedded common senses to address highly challenge tasks, such as natural language understanding [8], visual question answering [9], and aspect extraction in opinion mining [10].…”

Section: Introductionmentioning

confidence: 99%

A Heuristic Neural Network Structure Relying on Fuzzy Logic for Images Scoring

Cheng

Wang

et al. 2021

IEEE Trans. Fuzzy Syst.

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Traditional deep learning methods are sub-optimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always presents unclear boundaries (normal more likely normal-probably normal). Thus, human error is common when annotating images. Differing from existing methods that focus on modifying kernel structure of neural networks, this study proposes a dominant fuzzy fully connected layer (FFCL) for Breast Imaging Reporting and Data System (BI-RADS) scoring and validates the universality of this proposed structure. This proposed model aims to develop complementary properties of scoring for semantic paradigms, while constructing fuzzy rules based on analyzing human thought patterns, and to particularly reduce the influence of semantic conglutination. Specifically, this semantic-sensitive defuzzier layer projects features occupied by relative categories into semantic space, and a fuzzy decoder modifies probabilities of the last output layer referring to the global trend. Moreover, the ambiguous semantic space between two relative categories shrinks during the learning phases, as the positive and negative growth trends of one category appearing among its relatives were considered. We first used the Euclidean Distance (ED) to zoom in the distance between the real scores and the predicted scores, and then employed two sample t test method to evidence the advantage of the FFCL architecture. Extensive experimental results performed on the CBIS-DDSM dataset show that our FFCL structure can achieve superior performances for both triple and multiclass classification in BI-RADS scoring, outperforming the state-of-the-art methods. Index Terms Fuzzy deep neural networks, transfer learning, fuzzy fully connected layer, medical image scoring.

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Adaptive fuzzy-SIFT rule-based registration for 3D cardiac motion estimation

Hosseini

Moradi

2021

Appl Intell

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Fuzzy theoretic model based analysis of image features

Cited by 7 publications

References 29 publications

PARSEG: a computationally efficient approach for statistical validation of botanical seeds’ images

PARSEG: a computationally efficient approach for statistical validation of botanical seeds’ images

A Heuristic Neural Network Structure Relying on Fuzzy Logic for Images Scoring

Adaptive fuzzy-SIFT rule-based registration for 3D cardiac motion estimation

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