scholarly journals LRSCnet: Local Reference Semantic Code learning for breast tumor classification in ultrasound images

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Guang Zhang ◽  
Yanwei Ren ◽  
Xiaoming Xi ◽  
Delin Li ◽  
Jie Guo ◽  
...  
Keyword(s):  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Two Stage ◽  
Predictive Values ◽  
Semantic Code ◽  
Local Structures ◽  
Local Reference ◽  
Classification Information ◽  
High Level

Abstract Purpose This study proposed a novel Local Reference Semantic Code (LRSC) network for automatic breast ultrasound image classification with few labeled data. Methods In the proposed network, the local structure extractor is firstly developed to learn the local reference which describes common local characteristics of tumors. After that, a two-stage hierarchical encoder is developed to encode the local structures of lesion into the high-level semantic code. Based on the learned semantic code, the self-matching layer is proposed for the final classification. Results In the experiment, the proposed method outperformed traditional classification methods and AUC (Area Under Curve), ACC (Accuracy), Sen (Sensitivity), Spec (Specificity), PPV (Positive Predictive Values), and NPV(Negative Predictive Values) are 0.9540, 0.9776, 0.9629, 0.93, 0.9774 and 0.9090, respectively. In addition, the proposed method also improved matching speed. Conclusions LRSC-network is proposed for breast ultrasound images classification with few labeled data. In the proposed network, a two-stage hierarchical encoder is introduced to learn high-level semantic code. The learned code contains more effective high-level classification information and is simpler, leading to better generalization ability.

scholarly journals Breast Ultrasound Image Synthesis using Deep Convolutional Generative Adversarial Networks

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 176 ◽  
Author(s):  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Yuka Kikuchi ◽  
Leona Katsuta ◽  
...  
Keyword(s):  
Ultrasound Image ◽  
Image Synthesis ◽  
Breast Ultrasound ◽  
Generative Adversarial Networks ◽  
Ultrasound Images ◽  
Clinical Value ◽  
Adversarial Networks ◽  
Significant Difference ◽  
Definition Of ◽  
The Masses

Deep convolutional generative adversarial networks (DCGANs) are newly developed tools for generating synthesized images. To determine the clinical utility of synthesized images, we generated breast ultrasound images and assessed their quality and clinical value. After retrospectively collecting 528 images of 144 benign masses and 529 images of 216 malignant masses in the breasts, synthesized images were generated using a DCGAN with 50, 100, 200, 500, and 1000 epochs. The synthesized (n = 20) and original (n = 40) images were evaluated by two radiologists, who scored them for overall quality, definition of anatomic structures, and visualization of the masses on a five-point scale. They also scored the possibility of images being original. Although there was no significant difference between the images synthesized with 1000 and 500 epochs, the latter were evaluated as being of higher quality than all other images. Moreover, 2.5%, 0%, 12.5%, 37.5%, and 22.5% of the images synthesized with 50, 100, 200, 500, and 1000 epochs, respectively, and 14% of the original images were indistinguishable from one another. Interobserver agreement was very good (|r| = 0.708–0.825, p < 0.001). Therefore, DCGAN can generate high-quality and realistic synthesized breast ultrasound images that are indistinguishable from the original images.

scholarly journals Two-stage CNNs for computerized BI-RADS categorization in breast ultrasound images

2019 ◽  
Vol 18 (1) ◽  
Author(s):  
Yunzhi Huang ◽  
Luyi Han ◽  
Haoran Dou ◽  
Honghao Luo ◽  
Zhen Yuan ◽  
...  
Keyword(s):  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Two Stage

Adaptive Feature Selection for Ultrasound Image Processing and its Application to Liver Cirrhosis Detection

2013 ◽  
Vol 411-414 ◽  
pp. 1372-1376
Author(s):  
Wei Tin Lin ◽  
Shyi Chyi Cheng ◽  
Chih Lang Lin ◽  
Chen Kuei Yang
Keyword(s):  
Clustering Algorithm ◽  
Ultrasound Image ◽  
Image Features ◽  
Ultrasound Images ◽  
Analytic Hierarchy ◽  
Low Level ◽  
Adaptive Feature Selection ◽  
High Level ◽  
Objects Detection ◽  
Hierarchy Process

An approach to improve the regions of interesting (ROIs) selection accuracy automatically for medical images is proposed. The aim of the study is to select the most interesting regions of image features that good for diffuse objects detection or classification. We use the AHP (Analytic Hierarchy Process) to obtain physicians high-level diagnosis vectors and are clustered using the well-known K-Means clustering algorithm. The system also automatically extracts low-level image features for improving to detect liver diseases from ultrasound images. The weights of low-level features are adaptively updated according the feature variances in the class. Finally, the high-level diagnosis decision is made based on the high-level diagnosis vectors for the top K near neighbors from the medical experts classified database. Experimental results show the effectiveness of the system.

scholarly journals Tumor Segmentation in Breast Ultrasound Image by Means of Res Path Combined with Dense Connection Neural Network

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1565
Author(s):  
Kailuo Yu ◽  
Sheng Chen ◽  
Yanghuai Chen
Keyword(s):  
False Positive Rate ◽  
Ultrasound Image ◽  
True Positive Rate ◽  
Breast Ultrasound ◽  
Tumor Segmentation ◽  
Ultrasound Images ◽  
Feature Maps ◽  
Test Models ◽  
Positive Rate ◽  
The Difference

Over the past few years, researchers have demonstrated the possibilities to use the Computer-Aided Diagnosis (CAD) to provide a preliminary diagnosis. Recently, it is also becoming increasingly common for doctors and computer practitioners to collaborate on developing CAD. Since the early diagnosis of breast cancer is the most critical step, a precise segmentation of breast tumor with accurate edge and shape is vital for accurate diagnoses and reduction in the patients’ pain. In view of the deficient accuracy of existing method, we proposed a novel method based on U-Net to improve the tumor segmentation accuracy in breast ultrasound images. First, Res Path was introduced into the U-Net to reduce the difference between the feature maps of the encoder and decoder. Then, a new connection, dense block from the input of the feature maps in the encoding-to-decoding section, was added to reduce the feature information loss and alleviate the vanishing gradient problem. A breast ultrasound database, which contains 538 tumor images, from Xinhua Hospital in Shanghai and marked by two professional doctors was used to train and test models. We, using ten-fold cross-validation method, compared the U-Net, U-Net with Res Path, and the proposed method to verify the improvements. The results demonstrated an overall improvement by the proposed approach when compared with the other in terms of true-positive rate, false-positive rate, Hausdorff distance indices, Jaccard similarity, and Dice coefficients.

Breast Ultrasound Image Processing

2021 ◽  
pp. 44-69
Author(s):  
Strivathsav Ashwin Ramamoorthy ◽  
Varun P. Gopi
Keyword(s):  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Low Contrast ◽  
Cad Systems ◽  
Cad System ◽  
Ultrasound Image Processing ◽  
Serious Disease ◽  
Aided Diagnosis ◽  
Enhancement Algorithm

Breast cancer is a serious disease among women, and its early detection is very crucial for the treatment of cancer. To assist radiologists who manually delineate the tumour from the ultrasound image an automatic computerized method of detection called CAD (computer-aided diagnosis) is developed to provide valuable inputs for radiologists. The CAD systems is divided into many branches like pre-processing, segmentation, feature extraction, and classification. This chapter solely focuses on the first two branches of the CAD system the pre-processing and segmentation. Ultrasound images acquired depends on the operator expertise and is found to be of low contrast and fuzzy in nature. For the pre-processing branch, a contrast enhancement algorithm based on fuzzy logic is implemented which could help in the efficient delineation of the tumour from ultrasound image.

scholarly journals Multi-Features-Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhemin Zhuang ◽  
Zengbiao Yang ◽  
Shuxin Zhuang ◽  
Alex Noel Joseph Raj ◽  
Ye Yuan ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Deep Learning ◽  
Breast Tumor ◽  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Tumor Diagnosis ◽  
Support Vector ◽  
Ultrasound Images ◽  
Deep Learning Features ◽  
Characteristic Features

Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors.

scholarly journals Perbandingan Teknik Reduksi Derau Speckle Pada Citra Ultrasonograpi Payudara

2018 ◽  
Vol 4 (2) ◽  
pp. 27-36
Author(s):  
Yuli Triyani
Keyword(s):  
Breast Cancer ◽  
Ultrasound Image ◽  
Speckle Noise ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Reduction Techniques ◽  
Noise Filter ◽  
Incidence And Mortality ◽  
Structural Content ◽  
Aided Diagnosis

Breast cancer is the most commonly diagnosed cancer with the highest prevalence, incidence, and mortality rate for females in Indonesia and worldwide. Ultrasonography is a recommended modality for breast cancer, because it is comfortable, radiation free and it can be widely used. However, ultrasound images often occur in quality degradation caused by speckle noise that appears during image acquisition. It causes difficulty for radiologists or Computer Aided Diagnosis (CAD) systems to diagnose these images. Some techniques are proposed for reducing the speckle noise. This journal aims to compare the performance of 14 noise reduction techniques in breast ultrasound images. Quantitative testing was carried out on 58 breast ultrasound images and 3 artificial breast ultrasound image. The quantitative parameters are used include texture analysis (Mean, Variant, skewness, kurtosis, contrast and entropy) and evaluation of image quality (MSE, RMSE, SNR, SSIM, Structural content and Maximum Difference). The qualitative testing was also carried out with the assessment of 3 radiology specialists on 3 samples of each reduction technique. Based on test results, the 3 best performance filters are DsFsrad, DsFamedian dan DsFhmedian. Keywords: Ultrasound, speckle noise, filter

scholarly journals Classification of Breast Ultrasound Tomography by Using Textural Analysis

2020 ◽  
Vol 17 (2) ◽  
Author(s):  
Chih-Yu Liang ◽  
Tai-Been Chen ◽  
Nan-Han Lu ◽  
Yi-Chen Shen ◽  
Kuo-Ying Liu ◽  
...  
Keyword(s):  
Roc Analysis ◽  
Malignant Tumors ◽  
Rapid Development ◽  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Image Features ◽  
Benign Tumors ◽  
Ultrasound Images ◽  
The Mean

Background: Ultrasound imaging has become one of the most widely utilized adjunct tools in breast cancer screening due to its advantages. The computer-aided detection of breast ultrasound is rapid development via significant features extracted from images. Objectives: The main aim was to identify features of breast ultrasound image that can facilitate reasonable classification of ultrasound images between malignant and benign lesions. Patients and Methods: This research was a retrospective study in which 85 cases (35 malignant [positive group] and 50 benign [negative group] with diagnostic reports) with ultrasound images were collected. The B-mode ultrasound images have manually selected regions of interest (ROI) for estimated features of an image. Then, a fractal dimensional (FD) image was generated from the original ROI by using the box-counting method. Both FD and ROI images were extracted features, including mean, standard deviation, skewness, and kurtosis. These extracted features were tested as significant by t-test, receiver operating characteristic (ROC) analysis and Kappa coefficient. Results: The statistical analysis revealed that the mean texture of images performed the best in differentiating benign versus malignant tumors. As determined by the ROC analysis, the appropriate qualitative values for the mean and the LR model were 0.85 and 0.5, respectively. The sensitivity, specificity, accuracy, positive predicted value (PPV), negative predicted value (NPV), and Kappa for the mean was 0.77, 0.84, 0.81, 0.77, 0.84, and 0.61, respectively. Conclusion: The presented method was efficient in classifying malignant and benign tumors using image textures. Future studies on breast ultrasound texture analysis could focus on investigations of edge detection, texture estimation, classification models, and image features.

scholarly journals Breast Tumor Classification in Ultrasound Images Using Combined Deep and Handcrafted Features

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6838
Author(s):  
Mohammad I. Daoud ◽  
Samir Abdel-Rahman ◽  
Tariq M. Bdair ◽  
Mahasen S. Al-Najar ◽  
Feras H. Al-Hawari ◽  
...  
Keyword(s):  
Sensitivity And Specificity ◽  
Cross Validation ◽  
Ultrasound Image ◽  
Classification Performance ◽  
Breast Ultrasound ◽  
Morphological Features ◽  
Ultrasound Images ◽  
Features Selection ◽  
Selection Algorithm ◽  
The Cross

This study aims to enable effective breast ultrasound image classification by combining deep features with conventional handcrafted features to classify the tumors. In particular, the deep features are extracted from a pre-trained convolutional neural network model, namely the VGG19 model, at six different extraction levels. The deep features extracted at each level are analyzed using a features selection algorithm to identify the deep feature combination that achieves the highest classification performance. Furthermore, the extracted deep features are combined with handcrafted texture and morphological features and processed using features selection to investigate the possibility of improving the classification performance. The cross-validation analysis, which is performed using 380 breast ultrasound images, shows that the best combination of deep features is obtained using a feature set, denoted by CONV features that include convolution features extracted from all convolution blocks of the VGG19 model. In particular, the CONV features achieved mean accuracy, sensitivity, and specificity values of 94.2%, 93.3%, and 94.9%, respectively. The analysis also shows that the performance of the CONV features degrades substantially when the features selection algorithm is not applied. The classification performance of the CONV features is improved by combining these features with handcrafted morphological features to achieve mean accuracy, sensitivity, and specificity values of 96.1%, 95.7%, and 96.3%, respectively. Furthermore, the cross-validation analysis demonstrates that the CONV features and the combined CONV and morphological features outperform the handcrafted texture and morphological features as well as the fine-tuned VGG19 model. The generalization performance of the CONV features and the combined CONV and morphological features is demonstrated by performing the training using the 380 breast ultrasound images and the testing using another dataset that includes 163 images. The results suggest that the combined CONV and morphological features can achieve effective breast ultrasound image classifications that increase the capability of detecting malignant tumors and reduce the potential of misclassifying benign tumors.

scholarly journals Lesion Segmentation in Breast Ultrasound Images using Optimized Marked Watershed Method

2020 ◽  
Author(s):  
Xiaoyan Shen ◽  
He Ma ◽  
Ruibo Liu ◽  
Hong Li ◽  
Jiachuan He ◽  
...  
Keyword(s):  
Malignant Tumors ◽  
Classical Method ◽  
Ultrasound Image ◽  
Breast Ultrasound ◽  
Experimental Result ◽  
Benign Tumors ◽  
Ultrasound Images ◽  
Lesion Segmentation ◽  
Obvious Effect ◽  
Side Window

Abstract Background: Ultrasound is the most popular tool for early detection of breast cancer because of its non radiation and low cost. However, breast ultrasound(BUS) images have low resolution and speckle noise, which make lesion segmentation become a challenge. Most of deep learning(DL) models applied on images segmentation don't have good generalization ability for BUS images. Therefore, it is time to go back to the classical method and consider combining it with DL to achieve more accurate and efficient effect in a semi-automatic way.Methods: This paper mainly proposed an effective and efficient semi-automatic BUS images segmentation method, Adaptive morphological snake and marked watershed( AMSMW). It includes two parts: preprocessing and segmentation. In the first part, we combine contrast limited adaptive histogram equalization(CLAHE) and side window filtering(SWF) methods for the first time. In the second part, We use the proposed adaptive morphological snake algorithm (AMS) to provide a mark for marked watershed(MW) method. Results: we tested on 500 BUS images, whose ratio of benign and malignant is 1:1. After quantitative and qualitative analysis, AMSMW is proven to outperform existing classical methods on the effectiveness and efficiency. Furthermore, we compared with Zhuang’s RDAU-NET on both our dataset and theirs. Experimental result showes AMSMW achieved better performance on most of indicators, including loss, accuracy, sensitivity, dice and F1-score. Conlusions: The new image preprocessing method proposed by us has obvious effect on segmentation of breast ultrasound image. In addition, the proposed adaptive morphology snake method and optimized marked watershed turn out to be more efficient and effective than some relative classical method and the advanced DL method at present. Moreover, by studying on the algorithm’s sensitivity in segmenting benign and malignant tumors, we found that AMSMW is more sensitivity to malignant tumors, and more stable to benign tumors, which is significant for further research of precision medicine.

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