Comparison of Multiresolution Features for Texture Classification of Carotid Atherosclerosis From B-Mode Ultrasound

Tsiaparas, Nikolaos N.; Golemati, Spyretta; Andreadis, Ioannis; Stoitsis, John; Valavanis, Ioannis; Nikita, Konstantina S.

doi:10.1109/titb.2010.2091511

Cited by 82 publications

(52 citation statements)

References 26 publications

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“…Tsiaparas et al (2012) employed Wavelet transform, Ridgelet transform, curvelet transform and Wavelet packet and obtained highest overall classification accuracy of 79.2% for curvelet transform with SVM. Tsiaparas et al (2011) obtained overall classification accuracy of 82.5% for Wavelet packet with SVM classifier. In the proposed method, we have employed contourlet transform and extracted energy, mean standard deviation and entropy features.…”

Section: Discussionmentioning

confidence: 98%

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Carotid Plaque Classification Using Contourlet Features and Support Vector Machines

Thangavel¹

2014

Journal of Computer Science

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The aim of this study is to propose a suitable and reliable system for better diagnosis and treatment of carotid diseases. In this study, Computer Aided Diagnosis (CAD) system has been proposed for classifying carotid artery plaques using Contourlet features. Carotid images have been acquired for 124 subjects with symptoms (Amaurosis Fugax, Stroke or Transient Ischemic Attack) and 133 subjects with no symptoms in the recent past. Images were normalized and plaque regions have been manually segmented by experts and these Region Of Interests (ROI) have been used for further processing. 4 level Contourlet transform has been applied to all ROIs and subimages were produced at different scales and orientations. Energy, Entropy, Mean and Standard deviation features were extracted from all the subimages. The feature selection has been done to select significant features and to ignore insignificant ones. Support Vector Machine classifier (SVM) and Adaboost classifier have been applied to the selected features and plaques were classified as symptomatic or asymptomatic plaques. The contourlet features with Support vector machine classifier produced classification accuracy of 85.6% compared to 81.3% accuracy in Adaboost classifier. The classification results were compared with curvelet transform features and wavelet packet features. The contourlet with SVM classifier yielded better performance compared to curvelet and wavelet packet.

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Section: Discussionmentioning

confidence: 98%

“…Nonlinear correlation coefficient has been used to select the feature based on the threshold value. SVM and probabilistic neural network classifiers were used for classifying carotid plaques (Tsiaparas et al, 2011). The Gabor transform, statistical and fractal features were also extracted for comparing the performance.…”

Section: Jcsmentioning

confidence: 99%

Carotid Plaque Classification Using Contourlet Features and Support Vector Machines

Thangavel¹

2014

Journal of Computer Science

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“…This algorithm is firmly grounded in the framework of statistical learning theory -Vapnik Chervonenkis (VC)theory, which improves the generalization ability of learning machines to unseen data [48,49] . In the last few years Support Vector Machines have shown excellent performance in many real-world medical diagnosis applications [50] including object recognition, and diagnosis in medical [51,52].…”

Section: Support Vector Machinesmentioning

confidence: 99%

An Efficient Wavelet Based Feature Reduction and Classification Technique for the Diagnosis of Dementia

Sivapriya¹

2011

IJCSEIT

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“…The Stationary Wavelet Transform (SWT), a modified time-invariant version of DWT, has been used in texture classification tasks [16]. The FDCT has been effectively used for characterizing carotid atherosclerotic plaque from B-mode ultrasound and discriminating between symptomatic and asymptomatic cases [17].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based on DCE-MRI

Tzalavra

Dalakleidi

Zacharaki

et al. 2016

Machine Learning in Medical Imaging

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Abstract. Although Fourier and Wavelet Transform have been widely used for texture classification methods in medical images, the discrimination performance of FDCT has not been investigated so far in respect to breast cancer detection. Ιn this paper, three multi-resolution transforms, namely the Discrete Wavelet Transform (DWT), the Stationary Wavelet Transform (SWT) and the Fast Discrete Curvelet Transform (FDCT) were comparatively assessed with respect to their ability to discriminate between malignant and benign breast tumors in Dynamic Contrast-Enhanced Magnetic Resonance Images (DCE-MRI). The mean and entropy of the detail sub-images for each decomposition scheme were used as texture features, which were subsequently fed as input into several classifiers. FDCT features fed to a Linear Discriminant Analysis (LDA) classifier produced the highest overall classification performance (93,18 % Accuracy).

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Comparison of Multiresolution Features for Texture Classification of Carotid Atherosclerosis From B-Mode Ultrasound

Cited by 82 publications

References 26 publications

Carotid Plaque Classification Using Contourlet Features and Support Vector Machines

Carotid Plaque Classification Using Contourlet Features and Support Vector Machines

An Efficient Wavelet Based Feature Reduction and Classification Technique for the Diagnosis of Dementia

Comparison of Multi-resolution Analysis Patterns for Texture Classification of Breast Tumors Based on DCE-MRI

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