scholarly journals Segmentation and characterization of masses in breast ultrasound images using active contour

2011 ◽  
Author(s):  
Abdul Kadir Jumaat ◽  
Wan Eny Zarina W.A. Rahman ◽  
Arsmah Ibrahim ◽  
Rozi Mahmud
Keyword(s):  
Active Contour ◽  
Breast Ultrasound ◽  
Ultrasound Images

scholarly journals Comparison of Three Segmentation Methods for Breast Ultrasound Images based on Level Set and Morphological Operations

2017 ◽  
Vol 7 (1) ◽  
pp. 383
Author(s):  
Dewi Putrie Lestari ◽  
Sarifuddin Madenda ◽  
Ernastuti Ernastuti ◽  
Eri Prasetyo Wibowo
Keyword(s):  
Breast Cancer ◽  
Level Set ◽  
Active Contour ◽  
Level Set Methods ◽  
Breast Ultrasound ◽  
Medical Image Segmentation ◽  
Image Processing Technique ◽  
Dice Similarity Coefficient ◽  
Ultrasound Images ◽  
Morphological Operations

Breast cancer is one of the major causes of death among women all over the world. The most frequently used diagnosis tool to detect breast cancer is ultrasound. However, to segment the breast ultrasound images is a difficult thing. Some studies show that the active contour models have been proved to be the most successful methods for medical image segmentation. The level set method is a class of curve evolution methods based on the geometric active contour model. Morphological operation describes a range of image processing technique that deal with the shape of features in an image. Morphological operations are applied to remove imperfections that introduced during segmentation. In this paper, we have evaluated three level set methods that combined with morphological operations to segment the breast lesions. The level set methods that used in our research are the Chan Vese (C-V) model, the Selective Binary and Gaussian Filtering Regularized Level Set (SBGFRLS) model and the Distance Regularized Level Set Evolution (DRLSE) model. Furthermore, to evaluate the method, we compared the segmented breast lesion that obtained by each method with the lesion that obtained manually by radiologists. The evaluation is done by four metrics: Dice Similarity Coefficient (DSC), True-Positive Ratio (TPR), True-Negative Ratio (TNR), and Accuracy (ACC). Our experimental results with 30 breast ultrasound images showed that the C-V model that combined with morphological operations have better performance than the other two methods according to mean value of DSC metrics.

scholarly journals Artificial Intelligence System Reduces False-Positive Findings in the Interpretation of Breast Ultrasound Exams

2021 ◽  
Author(s):  
Yiqiu Shen ◽  
Farah E. Shamout ◽  
Jamie R. Oliver ◽  
Jan Witowski ◽  
Kawshik Kannan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
False Positive ◽  
Imaging Modality ◽  
Characteristic Curve ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Intelligence System ◽  
External Test

AbstractUltrasound is an important imaging modality for the detection and characterization of breast cancer. Though consistently shown to detect mammographically occult cancers, especially in women with dense breasts, breast ultrasound has been noted to have high false-positive rates. In this work, we present an artificial intelligence (AI) system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. To develop and validate this system, we curated a dataset consisting of 288,767 ultrasound exams from 143,203 patients examined at NYU Langone Health, between 2012 and 2019. On a test set consisting of 44,755 exams, the AI system achieved an area under the receiver operating characteristic curve (AUROC) of 0.976. In a reader study, the AI system achieved a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924±0.02 radiologists). With the help of the AI, radiologists decreased their false positive rates by 37.4% and reduced the number of requested biopsies by 27.8%, while maintaining the same level of sensitivity. To confirm its generalizability, we evaluated our system on an independent external test dataset where it achieved an AUROC of 0.911. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis worldwide.

scholarly journals Segmentation of Masses from Breast Ultrasound Images using Parametric Active Contour Algorithm

2010 ◽  
Vol 8 ◽  
pp. 640-647 ◽  
Author(s):  
Abdul Kadir Jumaat ◽  
Wan Eny Zarina Wan Abdul Rahman ◽  
Arsmah Ibrahim ◽  
Rozi Mahmud
Keyword(s):  
Active Contour ◽  
Breast Ultrasound ◽  
Ultrasound Images

Three-dimensional active contour model for characterization of solid breast masses on three-dimensional ultrasound images

2003 ◽  
Author(s):  
Berkman Sahiner ◽  
Aditya Ramachandran ◽  
Heang-Ping Chan ◽  
Marilyn A. Roubidoux ◽  
Lubomir M. Hadjiiski ◽  
...  
Keyword(s):  
Active Contour ◽  
Active Contour Model ◽  
Three Dimensional ◽  
Ultrasound Images ◽  
Breast Masses ◽  
Contour Model

Automated Characterization of Atheromatous Plaque in Intravascular Ultrasound Images Using Neuro Fuzzy Classifier

2012 ◽  
Vol 58 (4) ◽  
pp. 425-431 ◽  
Author(s):  
D. Selvathi ◽  
N. Emimal ◽  
Henry Selvaraj
Keyword(s):  
Intravascular Ultrasound ◽  
Human Error ◽  
Human Life ◽  
Classification Performance ◽  
Atheromatous Plaque ◽  
Ultrasound Images ◽  
Fuzzy Classifier ◽  
Neuro Fuzzy ◽  
Calcified Tissues

Abstract The medical imaging field has grown significantly in recent years and demands high accuracy since it deals with human life. The idea is to reduce human error as much as possible by assisting physicians and radiologists with some automatic techniques. The use of artificial intelligent techniques has shown great potential in this field. Hence, in this paper the neuro fuzzy classifier is applied for the automated characterization of atheromatous plaque to identify the fibrotic, lipidic and calcified tissues in Intravascular Ultrasound images (IVUS) which is designed using sixteen inputs, corresponds to sixteen pixels of instantaneous scanning matrix, one output that tells whether the pixel under consideration is Fibrotic, Lipidic, Calcified or Normal pixel. The classification performance was evaluated in terms of sensitivity, specificity and accuracy and the results confirmed that the proposed system has potential in detecting the respective plaque with the average accuracy of 98.9%.

Nipple Localization in Automated Whole Breast Ultrasound Coronal Scans Using Ensemble Learning

2020 ◽  
Vol 43 (1) ◽  
pp. 29-45
Author(s):  
Alex Noel Joseph Raj ◽  
Ruban Nersisson ◽  
Vijayalakshmi G. V. Mahesh ◽  
Zhemin Zhuang
Keyword(s):  
Texture Features ◽  
Breast Ultrasound ◽  
Support Vector ◽  
Ultrasound Images ◽  
Shadow Area ◽  
Hu Moments ◽  
Artificial Neural Network Ann ◽  
Cade System ◽  
Occurrence Matrix ◽  
Shape Complexity

Nipple is a vital landmark in the breast lesion diagnosis. Although there are advanced computer-aided detection (CADe) systems for nipple detection in breast mediolateral oblique (MLO) views of mammogram images, few academic works address the coronal views of breast ultrasound (BUS) images. This paper addresses a novel CADe system to locate the Nipple Shadow Area (NSA) in ultrasound images. Here the Hu Moments and Gray-level Co-occurrence Matrix (GLCM) were calculated through an iterative sliding window for the extraction of shape and texture features. These features are then concatenated and fed into an Artificial Neural Network (ANN) to obtain probable NSA’s. Later, contour features, such as shape complexity through fractal dimension, edge distance from the periphery and contour area, were computed and passed into a Support Vector Machine (SVM) to identify the accurate NSA in each case. The coronal plane BUS dataset is built upon our own, which consists of 64 images from 13 patients. The test results show that the proposed CADe system achieves 91.99% accuracy, 97.55% specificity, 82.46% sensitivity and 88% F-score on our dataset.

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