Analysis of margin sharpness for breast nodule classification on ultrasound images

Nugroho, Hanung Adi; Triyani, Yuli; Rahmawaty, Made; Ardiyanto, Igi

doi:10.1109/iciteed.2017.8250442

Cited by 8 publications

(12 citation statements)

References 12 publications

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“…They obtained a classification accuracy of 82% and a sensitivity of 94% using a support vector machine (SVM) classifier [10]. Nugroho et al also made use of shape-based feature analysis and extraction for the classification of breast nodules with a specific focus on the marginal characteristics of uncircumscribed versus circumscribed margins [11].…”

Section: Related Workmentioning

confidence: 99%

“…In the presented literature it can be seen that there are several methods for the feature extraction and classification of thyroid nodules. Studies [6][7][8][9][10][11] give a broad understanding of the use of shape-based features in medical image analyses. Using various combinations of these shape-based features, studies [12][13][14][15] were able to classify thyroid nodules in US images with significant outcomes.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Thyroid Nodule Classification for Physician Decision Support Using Machine Learning-Evaluated Geometric and Morphological Features

Ataide

Nikhila

Illanes

et al. 2020

Sensors

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The classification of thyroid nodules using ultrasound (US) imaging is done using the Thyroid Imaging Reporting and Data System (TIRADS) guidelines that classify nodules based on visual and textural characteristics. These are composition, shape, size, echogenicity, calcifications, margins, and vascularity. This work aims to reduce subjectivity in the current diagnostic process by using geometric and morphological (G-M) features that represent the visual characteristics of thyroid nodules to provide physicians with decision support. A total of 27 G-M features were extracted from images obtained from an open-access US thyroid nodule image database. 11 significant features in accordance with TIRADS were selected from this global feature set. Each feature was labeled (0 = benign and 1 = malignant) and the performance of the selected features was evaluated using machine learning (ML). G-M features together with ML resulted in the classification of thyroid nodules with a high accuracy, sensitivity and specificity. The results obtained here were compared against state-of the-art methods and perform significantly well in comparison. Furthermore, this method can act as a computer aided diagnostic (CAD) system for physicians by providing them with a validation of the TIRADS visual characteristics used for the classification of thyroid nodules in US images.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Thyroid Nodule Classification for Physician Decision Support Using Machine Learning-Evaluated Geometric and Morphological Features

Ataide

Nikhila

Illanes

et al. 2020

Sensors

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show abstract

“…5". In a recent study, Nugroho et al [49,50] introduced the technique of classification of breast nodule characteristics that allow for differentiation into non-circumcised and circumcised categories. The technique employs marker removal using an adaptive median filter, image normalization, and Speckle Reduction Anisotropic Diffusion (SRAD) filter using preprocessing, neutrosophic, and finally segmentation using the watershed technique.…”

Section: B Watershed-basedmentioning

confidence: 99%

A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

Muhammad

Zeebaree

Brifcani

et al. 2020

JASTT

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The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

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“…This type of analysis was conducted on breast masses acquired with different imaging modalities, such as mammography, 19,20 digital breast tomosynthesis, 21 breast MRI, 22,23 and ultrasound, 24,25 with the aim of quantifying the degree of spiculation through margin sharpness and radial gradient analysis, 19,[22][23][24] the texture and echogenicity along the mass boundary, and the diversity in average intensity values over different margin regions. 21,24 Therefore, several advancements are being pursued in radiomics, both in the development of new descriptors and algorithms, and in their application to different medical imaging modalities. In the same vein, in this study a multi-marker radiomic algorithm able to capture different tumor characteristics was developed, and applied to diagnose breast masses imaged with dedicated breast computed tomography (bCT).…”

Section: Introductionmentioning

confidence: 99%

“…For example, some investigators have performed the radiomic analysis on the mass periphery, relating the information extracted from the mass margin to the tumor phenotype. This type of analysis was conducted on breast masses acquired with different imaging modalities, such as mammography, 19,20 digital breast tomosynthesis, 21 breast MRI, 22,23 and ultrasound, 24,25 with the aim of quantifying the degree of spiculation through margin sharpness and radial gradient analysis, 19,22–24 the texture and echogenicity along the mass boundary, and the diversity in average intensity values over different margin regions 21,24 …”

Section: Introductionmentioning

confidence: 99%

Multi‐marker quantitative radiomics for mass characterization in dedicated breast CT imaging

Caballo¹,

Pangallo

Sanderink³

et al. 2020

Medical Physics

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To develop and evaluate the diagnostic performance of an algorithm for multi-marker radiomic-based classification of breast masses in dedicated breast computed tomography (bCT) images. Methods: Over 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, and margin sharpness were developed and implemented. These included well-established texture and shape feature descriptors, which were supplemented with additional approaches for contour irregularity quantification, spicule and lobe detection, characterization of degree of infiltration, and differences in peritumoral compartments. All descriptors were extracted from a training set of 202 bCT masses (133 benign and 69 malignant), and their individual diagnostic performance was investigated in terms of area under the receiver operating characteristics (ROC) curve (AUC) of single-feature-based linear discriminant analysis (LDA) classifiers. Subsequently, the most relevant descriptors were selected through a multiple-step feature selection process (including stability analysis, statistical significance, evaluation of feature interaction, and dimensionality reduction), and used to develop a final LDA radiomic model for classification of benign and malignant masses, which was then tested on an independent test set of 82 cases (45 benign and 37 malignant). Results: The majority of the individual radiomic descriptors showed, on the training set, an AUC value deriving from a linear decision boundary higher than 0.65, with the lower limit of the associated 95% confidence interval (C.I.) not overlapping with random chance (AUC = 0.5). The final LDA radiomic model resulted in a test set AUC of 0.90 (95% C.I. 0.80-0.96). Conclusions: The proposed multi-marker radiomic approach achieved high diagnostic accuracy in bCT mass classification, using a radiomic signature based on different feature types. While future studies with larger datasets are needed to further validate these results, quantitative radiomics applied to bCT shows potential to improve the breast cancer diagnosis pipeline.

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Analysis of margin sharpness for breast nodule classification on ultrasound images

Cited by 8 publications

References 12 publications

Thyroid Nodule Classification for Physician Decision Support Using Machine Learning-Evaluated Geometric and Morphological Features

Thyroid Nodule Classification for Physician Decision Support Using Machine Learning-Evaluated Geometric and Morphological Features

A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

Multi‐marker quantitative radiomics for mass characterization in dedicated breast CT imaging

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