Automatic plaque segmentation based on hybrid fuzzy clustering and k nearest neighborhood using virtual histology intravascular ultrasound images

Rezaei, Zahra; Selamat, Ali; Taki, Arash; Rahim, Mohd Shafry Mohd; Kadir, Mohammed Rafiq Abdul

doi:10.1016/j.asoc.2016.12.048

Cited by 24 publications

(33 citation statements)

References 30 publications

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“…In machine learning, kNN is classified as a lazy algorithm [68,69]. It stores all observations that it reads then predicts other observations based on distance functions.…”

Section: The K-nearest Neighborsmentioning

confidence: 99%

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Bui

et al. 2020

Applied Sciences

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In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.

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“…In machine learning, kNN is classified as a lazy algorithm [68,69]. It stores all observations that it reads then predicts other observations based on distance functions.…”

Section: The K-nearest Neighborsmentioning

confidence: 99%

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Bui

et al. 2020

Applied Sciences

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“…Although there are several existing works in the literature in the domain of TCFA detection, it still remains a challenging task [3]. In our previous work [45], hybrid Fuzzy C-means and K-Nearest Neighbour (HFCM-kNN) was proposed to accurately segment VH-IVUS images. The proposed technique was capable of eliminating outliers and detecting clusters with different densities in VH-IVUS images.…”

Section: Related Workmentioning

confidence: 99%

“…Due to the heterogeneity of the atherosclerotic tissues, a robust texture discriminator needs to be developed [26]. However, in our previous work [45], only geometric features were extracted and fed to the classifiers. Therefore, the second contribution of this work is with regard to hybrid geometric and texture features, whereas existing research on TCFA detection has only focused on geometric features.…”

Section: Related Workmentioning

confidence: 99%

“…The PSO algorithm is then applied to optimise the seed points. After the clustering process, cluster labelling is applied, and the colour feature matrix is extracted from the segmented images [45]. Pixel-wise classification is performed to test the image using the proposed algorithm, namely, mED.…”

Section: Proposed Approachmentioning

confidence: 99%

“…A VH-IVUS image represents a matrix with dimensions M × N × 3, which are quantised as integers with a value of 0 to 255 for each fundamental colour (Red, Green, Blue) [69]. The Media-Adventitia (M-A) border, grey in colour, creates an unwanted cluster, so it can be removed from the VH-IVUS image [45].…”

Section: Pre-processingmentioning

confidence: 99%

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Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

et al. 2018

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Atherosclerotic plaque rupture is the most common mechanism responsible for a majority of sudden coronary deaths. The precursor lesion of plaque rupture is thought to be a thin cap fibroatheroma (TCFA), or “vulnerable plaque”. Virtual Histology-Intravascular Ultrasound (VH-IVUS) images are clinically available for visualising colour-coded coronary artery tissue. However, it has limitations in terms of providing clinically relevant information for identifying vulnerable plaque. The aim of this research is to improve the identification of TCFA using VH-IVUS images. To more accurately segment VH-IVUS images, a semi-supervised model is developed by means of hybrid K-means with Particle Swarm Optimisation (PSO) and a minimum Euclidean distance algorithm (KMPSO-mED). Another novelty of the proposed method is fusion of different geometric and informative texture features to capture the varying heterogeneity of plaque components and compute a discriminative index for TCFA plaque, while the existing research on TCFA detection has only focused on the geometric features. Three commonly used statistical texture features are extracted from VH-IVUS images: Local Binary Patterns (LBP), Grey Level Co-occurrence Matrix (GLCM), and Modified Run Length (MRL). Geometric and texture features are concatenated in order to generate complex descriptors. Finally, Back Propagation Neural Network (BPNN), kNN (K-Nearest Neighbour), and Support Vector Machine (SVM) classifiers are applied to select the best classifier for classifying plaque into TCFA and Non-TCFA. The present study proposes a fast and accurate computer-aided method for plaque type classification. The proposed method is applied to 588 VH-IVUS images obtained from 10 patients. The results prove the superiority of the proposed method, with accuracy rates of 98.61% for TCFA plaque.

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Convolutional Neural Network for Atherosclerotic Plaque Multiclass Semantic Image Segmentation in Transverse Ultrasound Images of Carotid Artery

Dašić,

Pavić,

Blagojević

et al. 2024

Lecture Notes in Networks and Systems

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Automatic plaque segmentation based on hybrid fuzzy clustering and k nearest neighborhood using virtual histology intravascular ultrasound images

Cited by 24 publications

References 30 publications

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

Thin Cap Fibroatheroma Detection in Virtual Histology Images Using Geometric and Texture Features

Convolutional Neural Network for Atherosclerotic Plaque Multiclass Semantic Image Segmentation in Transverse Ultrasound Images of Carotid Artery

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