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

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.

scholarly journals Classification for Liver Diseases Based on Ultrasound Image Texture Features

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.

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

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.

scholarly journals Performance Evaluation of Contour Based Segmentation Methods for Ultrasound Images

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
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.

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

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.

scholarly journals Early detection of joint abnormalities from ultrasound images

2018 ◽  
Vol 7 (2.25) ◽  
pp. 105
Author(s):  
R J. Hemalatha ◽  
Dr V. Vijaybaskar ◽  
A Josephin Arockia Dhivya ◽  
. .
Keyword(s):  
Early Detection ◽  
Ultrasound Image ◽  
Computer Software ◽  
Pso Algorithm ◽  
Optimization Techniques ◽  
Optimization Approach ◽  
Ultrasound Images ◽  
Processing Techniques ◽  
Modern Image

Musculoskeletal ultrasound is effective for the early detection of joint abnormalities like erosion, effusion, synovitis and inflammation. Computer software is developed for segmentation of joint ultrasound image to diagnose the defect. The objective of developing this paper is to achieve early diagnosis of joint disorders by segmentation of ultrasound image with different algorithms. Ultrasound machine with high resolution probe can be used for development & findings of joints by the orthopaedician, rheumatologist and sports physician. These find-ings are done by processing the ultrasound images of patient joint using modern image processing techniques. Therefore algorithms has been designed and developed for analysis of medical images that is musculo ultrasound image based on optimization approach, using genet-ic algorithm and PSO algorithm. To improve the better quality of the image many improvisation techniques have been introduced. Hence, these algorithms perform better segmentation and identification of joint abnormalities. The analysis of ultrasound image is directly based on image segmentation steps, pre-processing, filtering, feature extraction and analysis of these extracted features by finding the output using different optimization techniques. In proposed method, efforts have been made to exhibit the procedure for finding and segmenting the mus-culoskeletal images of abnormal joints. The present approaches are segmentation operation on ultrasound images by applying genetic and PSO algorithm. The comparison between these algorithms is done, such that the algorithm itself analyses the whole image and perform the segmentation and detection of abnormalities perfectly   

STAGING OF PROSTATE CANCER USING 3-DIMENSIONAL TRANSRECTAL ULTRASOUND IMAGES: A PILOT STUDY

1999 ◽  
pp. 1318-1321 ◽  
Author(s):  
SUDHANSHU GARG ◽  
BJ??RN FORTLING ◽  
DAVID CHADWICK ◽  
MARY C. ROBINSON ◽  
FREDDIE C. HAMDY
Keyword(s):  
Prostate Cancer ◽  
Pilot Study ◽  
Transrectal Ultrasound ◽  
Ultrasound Images ◽  
3 Dimensional

Quantitative colour Doppler and greyscale ultrasound for evaluating prostate cancer

Ultrasound ◽  
2020 ◽  
pp. 1742271X2095282
Author(s):  
Khalid Ashi ◽  
Brooke Kirkham ◽  
Anil Chauhan ◽  
Susan M Schultz ◽  
Bonnie J Brake ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Flow Velocity ◽  
Transrectal Ultrasound ◽  
Doppler Imaging ◽  
Surrounding Tissue ◽  
Ultrasound Images ◽  
Colour Doppler ◽  
Benign Lesions ◽  
Microvascular Flow ◽  
Malignant Lesions

Introduction Although transrectal ultrasound is routinely performed for imaging prostate lesions, colour Doppler imaging visualizing vascularity is not commonly used for diagnosis. The goal of this study was to measure vascular and echogenic differences between malignant and benign lesions of the prostate by quantitative colour Doppler and greyscale transrectal ultrasound. Methods Greyscale and colour Doppler ultrasound images of the prostate were acquired in 16 subjects with biopsy-proven malignant or benign lesions. Echogenicity and microvascular flow velocity of each lesion were measured by quantitative image analysis. Flow velocity was measured over several cardiac cycles and the velocity–time waveform was used to determine microvascular pulsatility index and microvascular resistivity index. The Wilcoxon rank sum test was used to compare the malignant and benign groups. Results Median microvascular flow velocity of the malignant lesions was 1.25 cm/s compared to 0.36 cm/s for the benign lesions. Median pulsatility and resistive indices of the malignant lesions were 1.55 and 0.68, respectively versus 6.38 and 1.0 for the benign lesions. Malignant lesions were more hypoechoic relative to the surrounding tissue, with median echogenicity of 0.24 compared to 0.76 for the benign lesions. The differences between the malignant and benign groups for each measurement were significant (p < 0.01). Conclusion Marked differences were observed in flow velocity, microvascular pulsatility, microvascular resistance, and echogenicity of prostate cancer measured with quantitative colour Doppler and greyscale ultrasound imaging. Vascular differences measured together with echogenicity have the combined potential to characterize malignant and benign prostate lesions.

scholarly journals Artificial Neural Network Application in the Diagnosis of Disease Conditions with Liver Ultrasound Images

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Karthik Kalyan ◽  
Binal Jakhia ◽  
Ramachandra Dattatraya Lele ◽  
Mukund Joshi ◽  
Abhay Chowdhary
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Texture Features ◽  
Speckle Noise ◽  
Training Data ◽  
Ultrasound Images ◽  
Data Set ◽  
Imaging Artifacts ◽  
Artificial Neural ◽  
Ultrasonic Images

The preliminary study presented within this paper shows a comparative study of various texture features extracted from liver ultrasonic images by employing Multilayer Perceptron (MLP), a type of artificial neural network, to study the presence of disease conditions. An ultrasound (US) image shows echo-texture patterns, which defines the organ characteristics. Ultrasound images of liver disease conditions such as “fatty liver,” “cirrhosis,” and “hepatomegaly” produce distinctive echo patterns. However, various ultrasound imaging artifacts and speckle noise make these echo-texture patterns difficult to identify and often hard to distinguish visually. Here, based on the extracted features from the ultrasonic images, we employed an artificial neural network for the diagnosis of disease conditions in liver and finding of the best classifier that distinguishes between abnormal and normal conditions of the liver. Comparison of the overall performance of all the feature classifiers concluded that “mixed feature set” is the best feature set. It showed an excellent rate of accuracy for the training data set. The gray level run length matrix (GLRLM) feature shows better results when the network was tested against unknown data.

Evaluation of Transrectal Ultrasound in the Early Detection of Prostate Cancer

1989 ◽  
Vol 142 (4) ◽  
pp. 1008-1010 ◽  
Author(s):  
H. Ballentine Carter ◽  
Ulrike M. Hamper ◽  
Sheila Sheth ◽  
Roger C. Sanders ◽  
Jonathan I. Epstein ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Early Detection ◽  
Transrectal Ultrasound

Segmentation Methods in Ultrasound Images

2011 ◽  
pp. 377-390
Author(s):  
Farhang Sahba
Keyword(s):  
Image Segmentation ◽  
Automatic Segmentation ◽  
Transrectal Ultrasound ◽  
Ultrasound Image ◽  
The Body ◽  
Ultrasound Images ◽  
Sound Waves ◽  
Medical Test ◽  
Segmentation Methods ◽  
Segmentation Task

Ultrasound imaging now has widespread clinical use. It involves exposing a part of the body to highfrequency sound waves in order to generate images of the inside of the body. Because it is a real-time procedure, the ultrasound images show the movement of the body’s internal structure as well. It is usually a painless medical test and its procedures seem to be safe. Despite recent improvement in the quality of information from an ultrasound device, these images are still a challenging case for segmentation. Thus, there is much interest in understanding how to apply an image segmentation task to ultrasound data and any improvements in this regard are desirable. Many methods have been introduced in existing literature to facilitate more accurate automatic or semi-automatic segmentation of ultrasound images. This chapter is a basic review of the works on ultrasound image segmentation classified by application areas, including segmentation of prostate transrectal ultrasound (TRUS), breast ultrasound, and intravascular ultrasound (IVUS) images.

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