Automatic Segmentation of Intima Media Complex in Carotid Ultrasound Images Using Support Vector Machine

2018 ◽  
Vol 44 (4) ◽  
pp. 3489-3496 ◽  
Author(s):  
Y Nagaraj ◽  
A Hema Sai Teja ◽  
A V Narasimhadhan
Keyword(s):  
Support Vector Machine ◽  
Automatic Segmentation ◽  
Support Vector ◽  
Ultrasound Images ◽  
Carotid Ultrasound

Segmentation and Measurements of Carotid Intima-Media Thickness in Ultrasound Images Using the Improved Convolutional Neural Network and Support Vector Machine

2021 ◽  
Vol 11 (1) ◽  
pp. 15-24
Author(s):  
Dequan Guo ◽  
Gexiang Zhang ◽  
Hui Peng ◽  
Jianying Yuan ◽  
Prithwineel Paul ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Convolutional Neural Network ◽  
Intima Media Thickness ◽  
Ground Truth ◽  
Classification Model ◽  
Support Vector ◽  
Ultrasound Images ◽  
Boundary Points ◽  
Media Thickness

In recent years, diseases of cardiovascular and cerebrovascular have attracted much attention due to main causes in death in human beings. To reduce mortality, there are lots of efforts which are focused on early diagnosis and prevention. It is an important reference index for cardiovascular diseases through the endovascular membrane in carotid artery by medical ultrasound images. The paper proposes a method which finds the region of interest (ROI) by convolutional neural network, segments and measures intima-media membrane mainly using support vector machine (SVM). Essentially, the task of detecting the membrane is one target detection problem. This paper adopts the strategy, named Yon Only Look Once (YOLO), a new detection algorithm, and follows the convolution neural network algorithm based on end-to-end training. Firstly, sufficient samples are extracted according to certain characteristics in the special region. It can be trained by the SVM classification model. Then the ROI is processed and all the pixels are classified into boundary points and non-boundary points through the classification model. Thirdly, the boundary points are selected to obtain the accurate boundary and calculate the intima-media thickness (IMT). In experiments, two hundred ultrasound images are tested, and the results verify that our algorithm is consistent with the results by ground truth (GT). The detection speed of the algorithm in this paper is in real time, and it has high generalization characteristics. The algorithm computes the intima-media thickness in ultrasound images accurately and quickly with 95% consistence to ground truth.

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 Multiple Sclerosis Lesions Segmentation in Magnetic Resonance Imaging using Ensemble Support Vector Machine (ESVM)

2019 ◽  
Author(s):  
S HosseiniPanah ◽  
A Zamani ◽  
F Emadi ◽  
F HamtaeiPour
Keyword(s):  
Magnetic Resonance Imaging ◽  
Multiple Sclerosis ◽  
Support Vector Machine ◽  
Magnetic Resonance ◽  
Automatic Segmentation ◽  
Support Vector ◽  
Morphological Operations ◽  
Resonance Imaging ◽  
Volume Number

Background: Multiple Sclerosis (MS) syndrome is a type of Immune-Mediated disorder in the central nervous system (CNS) which destroys myelin sheaths, and results in plaque (lesion) formation in the brain. From the clinical point of view, investigating and monitoring information such as position, volume, number, and changes of these plaques are integral parts of the controlling process this disease over a period. Visualizing MS lesions in vivo with Magnetic Resonance Imaging (MRI) has a key role in observing the course of the disease.Material and Methods: Two different processing methods were present in this study in order to make an effort to detect and localize lesions in the patients’ FLAIR (Fluid-attenuated inversion recovery) images. Segmentation was performed using Ensemble Support Vector Machine (SVM) classification. The trained data was randomly divided into five equal sections, and each section was fed into the computer as an input to one of the SVM classifiers that led to five different SVM structures.Results: To evaluate results of segmentation, some criteria have been investigated such as Dice, Jaccard, sensitivity, specificity, PPV and accuracy. Both modes of ESVM, including first and second ones have similar results. Dice criterion was satisfied much better with specialist’s work and it is observed that Dice average has 0.57±.15 and 0.6±.12 values in the first and second approach, respectively.Conclusion: An acceptable overlap between those results reported by the neurologist and the ones obtained from the automatic segmentation algorithm was reached using an appropriate pre-processing in the proposed algorithm. Post-processing analysis further reduced false positives using morphological operations and also improved the evaluation criteria, including sensitivity and positive predictive value.

TAYLOR–MONARCH BUTTERFLY OPTIMIZATION-BASED SUPPORT VECTOR MACHINE FOR ACUTE LYMPHOBLASTIC LEUKEMIA CLASSIFICATION WITH BLOOD SMEAR MICROSCOPIC IMAGES

2021 ◽  
pp. 2150041
Author(s):  
G. MERCY BAI ◽  
P. VENKADESH
Keyword(s):  
Support Vector Machine ◽  
Acute Lymphoblastic Leukemia ◽  
Blood Smear ◽  
Lymphoblastic Leukemia ◽  
Automatic Segmentation ◽  
Texture Features ◽  
Monarch Butterfly ◽  
Support Vector ◽  
Training Time ◽  
Monarch Butterfly Optimization

Acute lymphoblastic leukemia (ALL) is a serious hematological neoplasis that is characterized by the development of immature and abnormal growth of lymphoblasts. However, microscopic examination of bone marrow is the only way to achieve leukemia detection. Various methods are developed for automatic leukemia detection, but these methods are costly and time-consuming. Hence, an effective leukemia detection approach is designed using the proposed Taylor–monarch butterfly optimization-based support vector machine (Taylor–MBO-based SVM). However, the proposed Taylor–MBO is designed by integrating the Taylor series and MBO, respectively. The sparking process is designed to perform the automatic segmentation of blood smear images by estimating optimal threshold values. By extracting the features, such as texture features, statistical, and grid-based features from the segmented smear image, the performance of classification is increased with less training time. The kernel function of SVM is enabled to perform the leukemia classification such that the proposed Taylor–MBO algorithm accomplishes the training process of SVM. However, the proposed Taylor–MBO-based SVM obtained better performance using the metrics, such as accuracy, sensitivity, and specificity, with 94.5751, 95.526, and 94.570%, respectively.

scholarly journals Detection and classification of thyroid nodule using Shearlet coefficients and support vector machine

2017 ◽  
Vol 6 (3) ◽  
pp. 50
Author(s):  
Nanda S. ◽  
Sukumar M.
Keyword(s):  
Support Vector Machine ◽  
Thyroid Nodule ◽  
Thyroid Nodules ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Textural Features ◽  
Ultrasound Images ◽  
K Nearest Neighbor ◽  
Benign Nodules

Thyroid nodules have diversified internal components and dissimilar echo patterns in ultrasound images. Textural features are used to characterize these echo patterns. This paper presents a classification scheme that uses shearlet transform based textural features for the classification of thyroid nodules in ultrasound images. The study comprised of 60 thyroid ultrasound images (30 with benign nodules and 30 with malignant nodules). Total of 22 features are extracted. Support vector machine (SVM) and K nearest neighbor (KNN) are used to differentiate benign and malignant nodules. The diagnostic sensitivity, specificity, F1_score and accuracy of both the classifiers are calculated. A comparative study has been carried out with respect to their performances. The sensitivity of SVM with radial basis function (RBF) kernel is 100% as compared to that of KNN with 96.33%. The proposed features can increase the accuracy of the classifier and decrease the rate of misdiagnosis in thyroid nodule classification.

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