Semi-Supervised Ultrasound Image Segmentation Based on Curvelet Features

The research is aimed at the development of an image processing system for classification of pathological area for medical images obtained from computed tomography (CT) scans. We proposed a novel semi-supervised image segmentation method based on the curvelet transform and SVM classfication. Firstly, through curvelet transform ultrasound images were decomposed into different directions and scales, the main distribution curvelet coefficients were extracted by cauchy model to reduce the algorithm time complexity, after inverse curvelet transform to obtaine a series of feature vectors from main distribution curvelet coefficients, then training samples and test samples were constructed; Secondly semi-supervised SVM classifier was designed, in order to reducing the weak classifier error rate, iteratively adjustment method was used to modify the SVM parameters, thus SVM strong classifier was constructed; Finally the expert manual tagging map were taken as reference standards, comparison with the existing method, experimental results shows that our algorithm is high anti-interference and has higher accuracy and effectiveness for ultrasound images pathological region segmentation.

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Ovarian Tumor Texture Classification Based on Sparse Auto-Encoder Network Combined with Multi-Feature Fusion and Random Forest in Ultrasound Image

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2021.3298 ◽

2021 ◽

Vol 11 (2) ◽

pp. 424-431

Author(s):

Yingxin Wang ◽

Qianqian Zeng

Keyword(s):

Neural Network ◽

Feature Fusion ◽

Texture Classification ◽

Ultrasound Image ◽

Texture Features ◽

Recall Rate ◽

Svm Classifier ◽

Ultrasound Images ◽

Feature Extraction Method

Texture analysis has always been active areas of ultrasound image processing research. Using texture features to classify the ultrasound images is the focus of researchers' attention. How to extract representative texture features is an important part of successful texture description. The research goal of this paper is to apply the deep neural network into the ultrasound classification of ovarian tumors, and design a novel type of ovarian cancer diagnosis system. The improved HOG feature extraction method and the gray-level concurrence matrix of LBP image are firstly adopted to extract low-level features; Then, these features are cascaded into a new feature vector, and are input into the auto-encoder neural network to learn the high-level feature. Finally, the SVM classifier is used to achieve the classification of ovarian lesion. A large number of qualitative and quantitative experiments show that the improved method has more performance than the comparisons algorithms for ovarian ultrasound lesion, and it can significantly improve the classification performance while ensuring the accuracy rate and recall rate.

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Performance Evaluation of Contour Based Segmentation Methods for Ultrasound Images

Advances in Multimedia ◽

10.1155/2018/4976372 ◽

2018 ◽

Vol 2018 ◽

pp. 1-8 ◽

Cited By ~ 5

Author(s):

R. J. Hemalatha ◽

V. Vijaybaskar ◽

T. R. Thamizhvani

Keyword(s):

Active Contour ◽

Performance Metrics ◽

Ultrasound Image ◽

Medical Image Segmentation ◽

Similarity Criteria ◽

Support Vector ◽

Svm Classifier ◽

Ultrasound Images ◽

Segmentation Method ◽

Region Detection

Active contour methods are widely used for medical image segmentation. Using level set algorithms the applications of active contour methods have become flexible and convenient. This paper describes the evaluation of the performance of the active contour models using performance metrics and statistical analysis. We have implemented five different methods for segmenting the synovial region in arthritis affected ultrasound image. A comparative analysis between the methods of segmentation was performed and the best segmentation method was identified using similarity criteria, standard error, and F-test. For further analysis, classification of the segmentation techniques using support vector machine (SVM) classifier is performed to determine the absolute method for synovial region detection. With these results, localized region based active contour named Lankton method is defined to be the best segmentation method.

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Classification of agricultural fields using time series of dual polarimetry TerraSAR-X images

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-2-w3-191-2014 ◽

2014 ◽

Vol XL-2/W3 ◽

pp. 191-196 ◽

Cited By ~ 2

Author(s):

S. Mirzaee ◽

M. Motagh ◽

H. Arefi ◽

M. Nooryazdan

Keyword(s):

Environmental Changes ◽

Support Vector ◽

Svm Classifier ◽

Sensing Applications ◽

Imaging Characteristics ◽

Training Samples ◽

Study Results ◽

Phenological Changes ◽

Multi Temporal

Due to its special imaging characteristics, Synthetic Aperture Radar (SAR) has become an important source of information for a variety of remote sensing applications dealing with environmental changes. SAR images contain information about both phase and intensity in different polarization modes, making them sensitive to geometrical structure and physical properties of the targets such as dielectric and plant water content. In this study we investigate multi temporal changes occurring to different crop types due to phenological changes using high-resolution TerraSAR-X imagers. The dataset includes 17 dual-polarimetry TSX data acquired from June 2012 to August 2013 in Lorestan province, Iran. Several features are extracted from polarized data and classified using support vector machine (SVM) classifier. Training samples and different features employed in classification are also assessed in the study. Results show a satisfactory accuracy for classification which is about 0.91 in kappa coefficient.

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Texture Feature-Based Classification on Transrectal Ultrasound Image for Prostatic Cancer Detection

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/7359375 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Xiaofu Huang ◽

Ming Chen ◽

Peizhong Liu ◽

Yongzhao Du

Keyword(s):

Prostate Cancer ◽

Early Detection ◽

Markov Random Fields ◽

Transrectal Ultrasound ◽

Ultrasound Image ◽

Texture Features ◽

Svm Classifier ◽

Ultrasound Images ◽

Malignant Tumours ◽

Data Set

Prostate cancer is one of the most common cancers in men. Early detection of prostate cancer is the key to successful treatment. Ultrasound imaging is one of the most suitable methods for the early detection of prostate cancer. Although ultrasound images can show cancer lesions, subjective interpretation is not accurate. Therefore, this paper proposes a transrectal ultrasound image analysis method, aiming at characterizing prostate tissue through image processing to evaluate the possibility of malignant tumours. Firstly, the input image is preprocessed by optical density conversion. Then, local binarization and Gaussian Markov random fields are used to extract texture features, and the linear combination is performed. Finally, the fused texture features are provided to SVM classifier for classification. The method has been applied to data set of 342 transrectal ultrasound images obtained from hospitals with an accuracy of 70.93%, sensitivity of 70.00%, and specificity of 71.74%. The experimental results show that it is possible to distinguish cancerous tissues from noncancerous tissues to some extent.

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A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/5894010 ◽

2020 ◽

Vol 2020 ◽

pp. 1-12

Author(s):

Mengwan Wei ◽

Yongzhao Du ◽

Xiuming Wu ◽

Qichen Su ◽

Jianqing Zhu ◽

...

Keyword(s):

Breast Tumor ◽

Texture Features ◽

Morphological Features ◽

High Dimensional ◽

Svm Classifier ◽

Ultrasound Images ◽

Malignant Breast Tumor ◽

Malignant Breast ◽

Low Dimensional

The classification of benign and malignant based on ultrasound images is of great value because breast cancer is an enormous threat to women’s health worldwide. Although both texture and morphological features are crucial representations of ultrasound breast tumor images, their straightforward combination brings little effect for improving the classification of benign and malignant since high-dimensional texture features are too aggressive so that drown out the effect of low-dimensional morphological features. For that, an efficient texture and morphological feature combing method is proposed to improve the classification of benign and malignant. Firstly, both texture (i.e., local binary patterns (LBP), histogram of oriented gradients (HOG), and gray-level co-occurrence matrixes (GLCM)) and morphological (i.e., shape complexities) features of breast ultrasound images are extracted. Secondly, a support vector machine (SVM) classifier working on texture features is trained, and a naive Bayes (NB) classifier acting on morphological features is designed, in order to exert the discriminative power of texture features and morphological features, respectively. Thirdly, the classification scores of the two classifiers (i.e., SVM and NB) are weighted fused to obtain the final classification result. The low-dimensional nonparameterized NB classifier is effectively control the parameter complexity of the entire classification system combine with the high-dimensional parametric SVM classifier. Consequently, texture and morphological features are efficiently combined. Comprehensive experimental analyses are presented, and the proposed method obtains a 91.11% accuracy, a 94.34% sensitivity, and an 86.49% specificity, which outperforms many related benign and malignant breast tumor classification methods.

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Classification for Liver Diseases Based on Ultrasound Image Texture Features

10.20944/preprints201810.0073.v1 ◽

2018 ◽

Author(s):

Sendren Sheng-Dong Xu ◽

Chien-Tien Su ◽

Chun-Chao Chang ◽

Pham Quoc Phu

Keyword(s):

Liver Abscess ◽

Liver Diseases ◽

Ultrasound Image ◽

Texture Features ◽

Image Texture ◽

Support Vector ◽

Svm Classifier ◽

Ultrasound Images ◽

Gray Level ◽

Forward Selection

This paper discusses the computer-aided (CAD) classification between Hepatocellular Carcinoma (HCC), i.e., the most common type of liver cancer, and Liver Abscess, based on ultrasound image texture features and Support Vector Machine (SVM) classifier. Among 79 cases of liver diseases, with 44 cases of HCC and 35 cases of liver abscess, this research extracts 96 features of Gray-Level Co-occurrence Matrix (GLCM) and Gray-Level Run-Length Matrix (GLRLM) from the region of interests (ROIs) in ultrasound images. Three feature selection models, i) Sequential Forward Selection, ii) Sequential Backward Selection, and iii) F-score, are adopted to determine the identification of these liver diseases. Finally, the developed system can classify HCC and liver abscess by SVM with the accuracy of 88.875%. The proposed methods can provide diagnostic assistance while distinguishing two kinds of liver diseases by using a CAD system.

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Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review

10.54216/fpa.030102 ◽

2021 ◽

pp. 29-42

Author(s):

admin admin ◽

◽

Adnan Mohsin Abdulazeez

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Recurrent Neural Networks ◽

Ultrasound Image ◽

Smart Devices ◽

Learning Approach ◽

Ultrasound Images ◽

Medical Field ◽

Modern Methods

With the development of technology and smart devices in the medical field, the computer system has become an essential part of this development to learn devices in the medical field. One of the learning methods is deep learning (DL), which is a branch of machine learning (ML). The deep learning approach has been used in this field because it is one of the modern methods of obtaining accurate results through its algorithms, and among these algorithms that are used in this field are convolutional neural networks (CNN) and recurrent neural networks (RNN). In this paper we reviewed what have researchers have done in their researches to solve fetal problems, then summarize and carefully discuss the applications in different tasks identified for segmentation and classification of ultrasound images. Finally, this study discussed the potential challenges and directions for applying deep learning in ultrasound image analysis.

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Impact of Modified Anisotropic Diffusion–based Enhancement Method in Computer-Aided Classification of Focal Liver Lesions

Ultrasonic Imaging ◽

10.1177/0161734616654933 ◽

2016 ◽

Vol 39 (2) ◽

pp. 96-107 ◽

Cited By ~ 4

Author(s):

Deepti Mittal

Keyword(s):

Anisotropic Diffusion ◽

Ultrasound Image ◽

Focal Liver Lesions ◽

P Value ◽

Ultrasound Images ◽

Liver Lesions ◽

Computer Aided ◽

Enhancement Method ◽

The Impact

This work is presented with the objective to assess quantitatively the impact of modified anisotropic diffusion–based enhancement method of Mittal et al. in computer-aided classification of focal liver lesions. This assessment was made before and after enhancement of clinically acquired ultrasound images with the comparison of (a) discrimination capability of radiologically important texture contrast feature using box plot and p-value statistics and (b) test results of designed computer-aided classification schemes to detect/classify focal liver tissues using receiver operating characteristic curves. The results reveal that the application of enhancement method on clinically acquired ultrasound image may effectively improve the confidence of clinicians/radiologists in computer-aided diagnostic solutions to detect and classify focal liver lesions.

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

Journal of Applied Science and Technology Trends ◽

10.38094/jastt1328 ◽

2020 ◽

Vol 1 (3) ◽

pp. 78-91

Author(s):

Muhammad Muhammad ◽

Diyar Zeebaree ◽

Adnan Mohsin Abdulazeez Brifcani ◽

Jwan Saeed ◽

Dilovan Asaad Zebari

Keyword(s):

Breast Cancer ◽

Image Segmentation ◽

Signal To Noise Ratio ◽

Ultrasound Image ◽

Region Of Interest ◽

Speckle Noise ◽

Ultrasound Images ◽

Clustering Methods ◽

Advantages And Disadvantages ◽

Ultrasound Image Segmentation

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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