A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear

Zarandi, M. H. Fazel; Khadangi, Afshin; Karimi, Faranak; Türkşen, I.B.

doi:10.1007/s10278-016-9884-y

Cited by 28 publications

(15 citation statements)

References 48 publications

(49 reference statements)

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“…First, by [19,21,28], such that [19] and [21] both are direct applications to a humanoid robot vision system, although both focused the use of IT2 FLS in different ways, as the first was used for object sample selection and the later was used for feature validity; and [28] is an application in auroral image segmentation. Secondly, the rest of the papers focused their algorithms to specifically solve medical imaging databases of different natures [18,20,22,27,29,31,33], using image segmentation.…”

Section: Discussionmentioning

confidence: 99%

“…In M.H. Fazel Zarandi et al (2016) [33], a T2 fuzzy expert system for meniscal tear diagnosis using MRI images was proposed, where in the preprocessing stage an λ-enhancement algorithm is used, followed by an IT2 FCM clustering algorithm for segmentation, and finally a neural network is used in the classification stage. Experimentation showed that the proposed approach was superior in performance when compared to competing algorithms.…”

Section: T2 Fs In Image Segmentationmentioning

confidence: 99%

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Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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This paper presents a literature review of applications using type-2 fuzzy systems in the area of image processing. Over the last years, there has been a significant increase in research on higher-order forms of fuzzy logic; in particular, the use of interval type-2 fuzzy sets and general type-2 fuzzy sets. The idea of making use of higher orders, or types, of fuzzy logic is to capture and represent uncertainty that is more complex. This paper is focused on image processing systems, which includes image segmentation, image filtering, image classification and edge detection. Various applications are presented where general type-2 fuzzy sets, interval type-2 fuzzy sets, and interval-value fuzzy sets are used; some are compared with the traditional type-1 fuzzy sets and others methodologies that exist in the literature for these areas in image processing. In all accounts, it is shown that type-2 fuzzy sets outperform both traditional image processing techniques as well as techniques using type-1 fuzzy sets, and provide the ability to handle uncertainty when the image is corrupted by noise.

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Section: Discussionmentioning

confidence: 99%

Section: T2 Fs In Image Segmentationmentioning

confidence: 99%

Review of Recent Type-2 Fuzzy Image Processing Applications

et al. 2017

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“…We also compared the convergence times of these methods to address one of our main aims which is rapid learning from limited ground-truth data. Finally, we report the behaviours of these classifiers during the training in terms of training and validation accuracy, F1-score, loss and AUC-ROC [28].…”

Section: Methodsmentioning

confidence: 99%

EM-net: Deep learning for electron microscopy image segmentation

Khadangi

Boudier

Rajagopal

2020

Preprint

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Recent high-throughput electron microscopy techniques such as focused ion-beam scanning electron microscopy (FIB-SEM) provide thousands of serial sections which assist the biologists in studying sub-cellular structures at high resolution and large volume. Low contrast of such images hinder image segmentation and 3D visualisation of these datasets. With recent advances in computer vision and deep learning, such datasets can be segmented and reconstructed in 3D with greater ease and speed than with previous approaches. However, these methods still rely on thousands of ground-truth samples for training and electron microscopy datasets require significant amounts of time for carefully curated manual annotations. We address these bottlenecks with EMnet, a scalable deep convolutional neural network for EM image segmentation. We have evaluated EM-net using two datasets, one of which belongs to an ongoing competition on EM stack segmentation since 2012. We show that EM-net variants achieve better performances than current deep learning methods using small-and medium-sized ground-truth datasets. We also show that the ensemble of top EM-net base classifiers outperforms other methods across a wide variety of evaluation metrics.

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“…In the following general model in Figure 1, we have a database, which consists of 200 patients for the fuzzy classifier, it should be noted that first of all, we have a monitoring data that consisting of 45 samples systolic and 45 diastolic samples which enter the modular neural network and these data are modeled and analyzed to finally give a tendency. Then this information is analyzed and classified by the fuzzy system, which is optimized in the membership functions and rules by a genetic algorithm (GA) [36][37][38][39][40]. Figure 2 shows the specific general model, we have a database, which consists of 200 patients, the modular neuronal network models and learns the information processed to finally give a tendency to base on that information given.…”

Section: Problem Statement and Proposed Methodsmentioning

confidence: 99%

Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification

Guzman

Miramontes

Melín

et al. 2019

Axioms

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The use of artificial intelligence techniques such as fuzzy logic, neural networks and evolutionary computation is currently very important in medicine to be able to provide an effective and timely diagnosis. The use of fuzzy logic allows to design fuzzy classifiers, which have fuzzy rules and membership functions, which are designed based on the experience of an expert. In this particular case a fuzzy classifier of Mamdani type was built, with 21 rules, with two inputs and one output and the objective of this classifier is to perform blood pressure level classification based on knowledge of an expert which is represented in the fuzzy rules. Subsequently different architectures were made in type-1 and type-2 fuzzy systems for classification, where the parameters of the membership functions used in the design of each architecture were adjusted, which can be triangular, trapezoidal and Gaussian, as well as how the fuzzy rules are optimized based on the ranges established by an expert. The main contribution of this work is the design of the optimized interval type-2 fuzzy system with triangular membership functions. The final type-2 system has a better classification rate of 99.408% than the type-1 classifier developed previously in “Design of an optimized fuzzy classifier for the diagnosis of blood pressure with a new computational method for expert rule optimization” with 98%. In addition, we also obtained a better classification rate than the other architectures proposed in this work.

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A Computer-Aided Type-II Fuzzy Image Processing for Diagnosis of Meniscus Tear

Cited by 28 publications

References 48 publications

Review of Recent Type-2 Fuzzy Image Processing Applications

Review of Recent Type-2 Fuzzy Image Processing Applications

EM-net: Deep learning for electron microscopy image segmentation

Optimal Genetic Design of Type-1 and Interval Type-2 Fuzzy Systems for Blood Pressure Level Classification

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