Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Breast Ultrasound Images

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

doi:10.12928/telkomnika.v15i4.5021

Cited by 3 publications

(4 citation statements)

References 18 publications

(20 reference statements)

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“…Thus this algorithm can work only with ultrasound image input. Several studies [9][10][11][12] have developed approaches to classify breast nodules based on only one BIRADS characteristic, such as shape, texture, margin, or posterior features. The results of the classification are the type of each characteristic, neither malignant nor benign.…”

Section: Discussionmentioning

confidence: 99%

“…Computer Assisted Diagnosis (CAD), a system that integrates digital image processing techniques with radiology, was created to reduce FPR [8]. Numerous research [9][10][11][12] have created approaches for identifying and classifying breast nodules based on just one BIRADS characteristics, such as shape, texture, margins, or posterior features. However, research on nodule classification using more than one BIRADS trait is still in its early stages.…”

Section: Introductionmentioning

confidence: 99%

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Malignant Detection of Breast Nodules On BIRADS-Based Ultrasound Images Margin, Orientation, And Posterior

Triyani¹,

Khabzli²,

Styorini³

2023

j.electron.electromedical.eng.med.inform

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Breast cancer has the largest prevalence in the world in 2020, with 2,261,419 cases or 11.7%. It is also the leading cause of cancer death, accounting for 6.9% of all cancer deaths. Asia and Indonesia have the greatest prevalence and mortality rates. This is an urgent issue that must be addressed. Ultrasonography (USG) is advised for assessing the features of breast nodules. Breast nodules on ultrasound pictures are interpreted using the Breast Imaging, Reporting, and Data System (BIRADS) category, which has five features. Yet, the probability of a False Positive Result (FPR) on ultrasound imaging is relatively high. Computer Aided Diagnosis (CAD) was created to reduce FPR. However, CAD research based on many BIRADS traits is currently margined. As a result, based on three BIRADS characteristics, namely the margin, posterior, and orientation aspects, this study aims to proposed the methode for diagnosing breast nodule malignancy. The proposed method consists of 4 stages, namely, pre-processing, automatic segmentation, features extraction, and classification. Pre-processing adaptive median filter maximum window size is 11 pixels, linear histogram normalizing, and Reduction Anisotropic Diffusion (SRAD) filter were used to construct the method. The neutrosophic watershed method was used in the suggested automatic segmentation. Based on the nodule's margin, orientation, and posterior, 10 features were proposed: nodule width, gradient, slenderness, margin sharpness, shadow indicators, skewness, energy, entropy, dispersion, and solidity. MLP is a classification approach. The test used 94 nodule pictures and yielded an accuracy of 88.30%, a sensitivity of 82.35%, a specificity of 91.67%, a Kappa of 0.7449, and an AUC of 0.865. As a result, it is feasible to conclude that the proposed method is capable of detecting malignancy in breast nodules in ultrasound images. To make the proposed method more reliable in the future, automatic RoI can be developed.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Malignant Detection of Breast Nodules On BIRADS-Based Ultrasound Images Margin, Orientation, And Posterior

Triyani¹,

Khabzli²,

Styorini³

2023

j.electron.electromedical.eng.med.inform

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“…A comparative analysis has been provided in Table 8 presenting the performance benchmark of the proposed system. Nugroho et al [45] proposed active contours without edges for segmentation, texture, and geometry analysis are performed for feature extraction and achieved 91.3% accuracy using SVM. Moon et al [46] employed fuzzy c-means clustering for segmentation, feature analysis was done using echogenicity and morphology, and accomplished 92.50% sensitivity for malignant lesion using the binary logistic regression technique.…”

Section: Discussionmentioning

confidence: 99%

“…B. Singh et al [47] performed shape-based analysis and achieved 84.6% accuracy using ANN. The proposed CADx system delineates better performance due to better segmentation, hybrid features extraction for the shape, and size of lesions and ensemble method of classification as compared to [45][46][47] in terms of accuracy and sensitivity.…”

Section: Discussionmentioning

confidence: 99%

Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare

et al. 2020

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Breast cancer is a highly prevalent disease in females that may lead to mortality in severe cases. The mortality can be subsided if breast cancer is diagnosed at an early stage. The focus of this study is to detect breast malignancy through computer-aided diagnosis (CADx). In the first phase of this work, Hilbert transform is employed to reconstruct B-mode images from the raw data followed by the marker-controlled watershed transformation to segment the lesion. The methods based only on texture analysis are quite sensitive to speckle noise and other artifacts. Therefore, a hybrid feature set is developed after the extraction of shape-based and texture features from the breast lesion. Decision tree, k-nearest neighbor (KNN), and ensemble decision tree model via random under-sampling with Boost (RUSBoost) are utilized to segregate the cancerous lesions from the benign ones. The proposed technique is tested on OASBUD (Open Access Series of Breast Ultrasonic Data) and breast ultrasound (BUS) images collected at Baheya Hospital Egypt (BHE). The OASBUD dataset contains raw ultrasound data obtained from 100 patients containing 52 malignant and 48 benign lesions. The dataset collected at BHE contains 210 malignant and 437 benign images. The proposed system achieved promising accuracy of 97% with confidence interval (CI) of 91.48% to 99.38% for OASBUD and 96.6% accuracy with CI of 94.90% to 97.86% for the BHE dataset using ensemble method.

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Automatic detection of breast cancer in ultrasound images using Mayfly algorithm optimized handcrafted features

Vijayakumar

Rajinikanth

Kirubakaran

2022

XST

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BACKGROUND: The incidence rates of breast cancer in women community is progressively raising and the premature diagnosis is necessary to detect and cure the disease. OBJECTIVE: To develop a novel automated disuse detection framework to examine the Breast-Ultrasound-Images (BUI). METHODS: This scheme includes the following stages; (i) Image acquisition and resizing, (ii) Gaussian filter-based pre-processing, (iii) Handcrafted features extraction, (iv) Optimal feature selection with Mayfly Algorithm (MA), (v) Binary classification and validation. The dataset includes BUI extracted from 133 normal, 445 benign and 210 malignant cases. Each BUI is resized to 256×256×1 pixels and the resized BUIs are used to develop and test the new scheme. Handcrafted feature-based cancer detection is employed and the parameters, such as Entropies, Local-Binary-Pattern (LBP) and Hu moments are considered. To avoid the over-fitting problem, a feature reduction procedure is also implemented with MA and the reduced feature sub-set is used to train and validate the classifiers developed in this research. RESULTS: The experiments were performed to classify BUIs between (i) normal and benign, (ii) normal and malignant, and (iii) benign and malignant cases. The results show that classification accuracy of > 94%, precision of > 92%, sensitivity of > 92% and specificity of > 90% are achieved applying the developed new schemes or framework. CONCLUSION: In this work, a machine-learning scheme is employed to detect/classify the disease using BUI and achieves promising results. In future, we will test the feasibility of implementing deep-learning method to this framework to further improve detection accuracy.

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Computer Aided Diagnosis using Margin and Posterior Acoustic Featuresfor Breast Ultrasound Images

Abstract: Abstract

Cited by 3 publications

References 18 publications

Malignant Detection of Breast Nodules On BIRADS-Based Ultrasound Images Margin, Orientation, And Posterior

Malignant Detection of Breast Nodules On BIRADS-Based Ultrasound Images Margin, Orientation, And Posterior

Identification of Breast Malignancy by Marker-Controlled Watershed Transformation and Hybrid Feature Set for Healthcare

Automatic detection of breast cancer in ultrasound images using Mayfly algorithm optimized handcrafted features

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