CAROTID WALL MEASUREMENT AND ASSESSMENT BASED ON PIXEL-BASED AND LOCAL TEXTURE DESCRIPTORS

2016 ◽  
Vol 16 (01) ◽  
pp. 1640006 ◽  
Author(s):  
SAMANTA ROSATI ◽  
KRISTEN MARIKO MEIBURGER ◽  
GABRIELLA BALESTRA ◽  
U. RAJENDRA ACHARYA ◽  
FILIPPO MOLINARI
Keyword(s):  
High Risk ◽  
Texture Features ◽  
Intima Media Thickness ◽  
Integrated System ◽  
Low Risk ◽  
Automated System ◽  
Ultrasound Images ◽  
Gray Level ◽  
Local Texture ◽  
Carotid Wall

Aim of this paper is to develop an automated system for the classification and characterization of carotid wall status and to develop a robust system based on local texture descriptors. A database of 200 longitudinal ultrasound images of carotid artery is used. One-hundred images with Intima-Media Thickness (IMT) value higher than 0.8[Formula: see text]mm are considered as high risk. Six different rectangular pixel neighborhoods were considered: four areas centered on the selected element, with sizes [Formula: see text], [Formula: see text], [Formula: see text], and [Formula: see text] pixels, and two noncentered areas with sizes [Formula: see text] pixels upwards and downwards. We have extracted various texture descriptors (31 based on the co-occurrence gray level matrix, 13 based on the spatial gray level dependence matrix, and 20 based on the gray level run length matrix (GLRLM) from neighborhood. We have used Quick Reduct Algorithm to select 12 most discriminant features from extracted 211 features. Each pixel is then assigned to the vessel lumen, to the intima-media complex, or to the adventitia by using an integrated system of three feed-forward neural networks. The boundaries between the three regions are used to estimate the IMT value. The texture features associated with GLRLM are found to be clinically most significant. We have obtained an overall classification accuracy of 79.5%, sensitivity of 87%, and specificity of 72%. We observed a unique classification pattern between low risk and high risk images: in the latter ones, a considerable number of pixels of the intima–media complex ([Formula: see text]) was classified as belonging to the adventitia. This percentage is statistically higher than that of low risk images ([Formula: see text]; [Formula: see text]). Locally extracted and pixel-based descriptors are able to capture the inner characteristics of the carotid wall. The presence of misclassified pixels in the intima–media complex is associated to higher cardiovascular risk.

scholarly journals An integrated system for the complete segmentation of the common carotid artery bifurcation in ultrasound images

2015 ◽  
Vol 1 (1) ◽  
pp. 11 ◽  
Author(s):  
Christos P. Loizou ◽  
Marios Pantziaris
Keyword(s):  
Carotid Artery ◽  
Common Carotid Artery ◽  
Carotid Plaque ◽  
Research Work ◽  
Texture Features ◽  
Intima Media Thickness ◽  
Risk Groups ◽  
Integrated System ◽  
Ultrasound Images ◽  
The Common

The complete segmentation of the common carotid artery (CCA) bifurcation in ultrasound images is important for the evaluation of atherosclerosis disease and the quantification of the risk of stroke. The current research work further evaluates and validates a semi-automated (SA) snake’s based segmentation system suitable for the complete segmentation of the CCA bifurcation in two-dimensional (2D) ultrasound images. The proposed system semi-automatically estimates the intima-media thickness (IMT), the atherosclerotic carotid plaque borders and dimensions, the internal carotid artery (ICA) origin’s stenosis, the carotid diameter (D), as well as other geometric measurements of the atherosclerotic carotid plaque.  The system was evaluated on 300 2D longitudinal ultrasound images of the CCA bifurcation with manual (M) segmentations available from a neurovascular expert. No statistical significant differences between all M and SA IMT, plaque and D segmentation measurements were found. In a future study, texture features extracted from the intima-media complex (IMC) may be used to separate subjects in high and low risk groups, which may develop a stroke. However, a larger scale study is required for evaluating the system before its application in the real clinical practice.

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.

scholarly journals Automatic Localization of the Carotid Wall in Ultrasound Imaging Based on Convolutional Neural Network

2019 ◽  
Author(s):  
Alexandre G. Silva ◽  
Eryk K. Da Cruz ◽  
Rangel Arthur ◽  
Giulliano P. Carnielli ◽  
Henri A. De Godoy ◽  
...  
Keyword(s):  
Neural Network ◽  
Carotid Arteries ◽  
Intima Media Thickness ◽  
Inflammatory Cells ◽  
Carotid Intima Media Thickness ◽  
Ultrasound Images ◽  
Non Invasive ◽  
The World ◽  
Carotid Wall ◽  
Automatic Localization

Atherosclerosis is the leading cause of death in the world. It is a cardiovascular disease characterized by the accumulation of inflammatory cells and lipids inside the artery walls. In Brazil, more than 30% of all deaths are due to cardiovascular diseases. The carotid intima-media thickness, obtained from ultrasound images, maybe an early estimate of atherosclerosis.This test is fast, safe and non-invasive, as well as being reproducible and relatively inexpensive. In this context, this work, based on convolutional neural networks and techniques of mathematical morphology, consists in automatically locating the region that covers the intima and media sublayers of carotid arteries. The proposed method obtained a score of 88% considering the trained model applied to 234 ultrasonographic images in two different datasets. The analysis of the neighborhood of the points obtained can be useful in the evaluation of cardiovascular risk factors.

scholarly journals Detection of high and low-risk HPV DNA in archived breast carcinoma tissues from Ethiopian women

2020 ◽  
Author(s):  
Endale Hadgu Gebregzabher ◽  
Daniel Seifu ◽  
Wondemagegnhu Tigneh ◽  
Yonas Bokretsion ◽  
Abebe Bekele ◽  
...  
Keyword(s):  
Breast Cancer ◽  
High Risk ◽  
Addis Ababa ◽  
Low Risk ◽  
University Hospital ◽  
Automated System ◽  
Breast Cancer Patients ◽  
Cell Cycle Protein ◽  
Hpv Types ◽  
High Risk Hpv

Abstract Background: HPV have been implicated in the development of cancer of the cervix, mouth and throat, anus, penis, vulva, or vagina, but it has not been much considered as a cause of breast cancer. However, a growing number of investigations have linked breast cancer to viral infections. High-risk HPV types, predominantly (HPV-16, -18, -31, -33, -35, -39, -45, -51, -52, -56, -58, and -59) are established as carcinogens in humans, while HPV-68 is probably carcinogenic. In this study we aimed to detect 19 high risk and 9 low risk HPVs from archived breast tumor tissue among Ethiopian women.Methods: In this study, 75 breast cancer patients from Tikur Anbassa Specialized Hospital in Addis Ababa (Ethiopia) were included. HPV detection and genotyping were done using the novel Anyplex™ II HPV-28 Detection Assay at the Orebro University Hospital, Sweden. The AnyplexTMII PCR System detects 19 high-risk HPV types (16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, 69, 73, 82) and 9 low-risk HPV types(6, 11, 40, 42, 43, 44, 54, 61, 70). IHC for p16 was done in automated system using the Dako Autostainer Link.Results: Out of the 75 valid tests 2 were found to be positive (2.7%) for HPV. One of the cases were positive for high risk HPV16 genotype while the other were positive both for high risk HPV39 and low risk HPV6. The cell cycle protein p16 was highly expressed in the case positive for the high risk HPV16 but it was not expressed in the case positive for HPV39.Conclusion: With limited number of cases positive for HPV in this study, it is our conclusion that cervical cancer prevention strategies may help protection of breast cancer only in small groups of patients. Due to limitation of the number of participants in the study as well as possible other mechanisms of carcinogenesis, our observation should be reconfirmed using a larger set of patients and in case-control design.

scholarly journals CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: “Impact of surgical modality choice”

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Ayten Kayi Cangir ◽  
Kaan Orhan ◽  
Yusuf Kahya ◽  
Hilal Özakıncı ◽  
Betül Bahar Kazak ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Risk Groups ◽  
Learning Model ◽  
Low Risk ◽  
Pathology Report ◽  
Histopathological Diagnosis ◽  
Gray Level ◽  
Long Run ◽  
Machine Learning Model

Abstract Introduction Radiomics methods are used to analyze various medical images, including computed tomography (CT), magnetic resonance, and positron emission tomography to provide information regarding the diagnosis, patient outcome, tumor phenotype, and the gene-protein signatures of various diseases. In low-risk group, complete surgical resection is typically sufficient, whereas in high-risk thymoma, adjuvant therapy is usually required. Therefore, it is important to distinguish between both. This study evaluated the CT radiomics features of thymomas to discriminate between low- and high-risk thymoma groups. Materials and methods In total, 83 patients with thymoma were included in this study between 2004 and 2019. We used the Radcloud platform (Huiying Medical Technology Co., Ltd.) to manage the imaging and clinical data and perform the radiomics statistical analysis. The training and validation datasets were separated by a random method with a ratio of 2:8 and 502 random seeds. The histopathological diagnosis was noted from the pathology report. Results Four machine-learning radiomics features were identified to differentiate a low-risk thymoma group from a high-risk thymoma group. The radiomics feature names were Energy, Zone Entropy, Long Run Low Gray Level Emphasis, and Large Dependence Low Gray Level Emphasis. Conclusions The results demonstrated that a machine-learning model and a multilayer perceptron classifier analysis can be used on CT images to predict low- and high-risk thymomas. This combination could be a useful preoperative method to determine the surgical approach for thymoma.

Optimal Feature Selection for Carotid Artery Image Segmentation Using Evolutionary Computation

2014 ◽  
Vol 626 ◽  
pp. 79-86 ◽  
Author(s):  
I. Mohammed Farook ◽  
S. Dhanalakshmi ◽  
V. Manikandan ◽  
C. Venkatesh
Keyword(s):  
Neural Network ◽  
Carotid Artery ◽  
Ultrasound Image ◽  
Intima Media Thickness ◽  
Ultrasound Images ◽  
Gray Level ◽  
Genetic Search ◽  
Heart Attacks ◽  
Hilbert Huang Transform ◽  
Occurrence Matrix

Atherosclerosis is hardening of arteries due to high blood pressure and high cholesterol. It causes heart attacks, stroke and peripheral vascular disease and is the major cause of death. In this paper we have attempted a method to identify the presence of plaque in carotid artery from ultrasound images. The ultrasound image is segmented using improved spatial Fuzzy c means algorithm to identify the presence of plaque in carotid artery. Spatial wavelet, Hilbert Huang Transform (HHT), Moment of Gray Level Histogram (MGLH) and Gray Level Co-occurrence Matrix (GLCM) features are extracted from ultrasound images and the feature set is reduced using genetic search process. The intima media thickness is measured using the proposed method. The IMT values are measured from the segmented image and trained using MLBPNN neural network. The neural network classifies the images into normal and abnormal.

scholarly journals CT Imaging-based Machine Learning Model: A Potential Modality for Predicting Low-Risk and High-Risk Groups of Thymoma “Impact of surgical modality choice”

2021 ◽  
Author(s):  
Ayten KAYICANGIR ◽  
Kaan ORHAN ◽  
Yusuf KAHYA ◽  
Hilal ÖZAKINCI ◽  
Betül Bahar KAZAK ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Risk Groups ◽  
Learning Model ◽  
Low Risk ◽  
Pathology Report ◽  
Histopathological Diagnosis ◽  
Gray Level ◽  
Long Run ◽  
Machine Learning Model

Abstract IntroductionRadiomics has become a hot issue in the medical imaging field, particularly in cancer imaging. Radiomics methods are used to analyze various medical images, including computed tomography (CT), magnetic resonance, and positron emission tomography to provide information regarding the diagnosis, patient outcome, tumor phenotype, and the gene-protein signatures of various diseases.This study evaluated the CT radiomics features of thymomas to discriminate between low- and high-risk thymoma groups.Materials and MethodsIn total, 83 patients with thymoma were included in this study between 2004 and 2019. We used the Radcloud platform (Huiying Medical Technology Co., Ltd.) to manage the imaging and clinical data and perform the radiomics statistical analysis. The training and validation datasets were separated by a random method with a ratio of 2:8 and 502 random seeds. The histopathological diagnosis was noted from the pathology report.ResultsFour machine learning radiomics features were identified to differentiate a low-risk thymoma group from a high-risk thymoma group. The radiomics feature names were Energy, Zone Entropy, Long Run Low Gray Level Emphasis, and Large Dependence Low Gray Level Emphasis.ConclusionsThe results demonstrated that a machine-learning model and a multilayer perceptron classifier analysis can be used on CT images to predict low- and high-risk thymomas. This combination could be a useful preoperative method to determine the surgical approach for thymoma.

Preliminary Study of Chronic Liver Classification on Ultrasound Images Using an Ensemble Model

2018 ◽  
Vol 40 (6) ◽  
pp. 357-379 ◽  
Author(s):  
Puja Bharti ◽  
Deepti Mittal ◽  
Rupa Ananthasivan
Keyword(s):  
Feature Fusion ◽  
Texture Features ◽  
Chronic Liver ◽  
Support Vector ◽  
Ultrasound Images ◽  
Ensemble Model ◽  
Gray Level ◽  
K Nearest Neighbor ◽  
Difference Matrix ◽  
Ensemble Strategy

Chronic liver diseases are fifth leading cause of fatality in developing countries. Their early diagnosis is extremely important for timely treatment and salvage life. To examine abnormalities of liver, ultrasound imaging is the most frequently used modality. However, the visual differentiation between chronic liver and cirrhosis, and presence of heptocellular carcinomas (HCC) evolved over cirrhotic liver is difficult, as they appear almost similar in ultrasound images. In this paper, to deal with this difficult visualization problem, a method has been developed for classifying four liver stages, that is, normal, chronic, cirrhosis, and HCC evolved over cirrhosis. The method is formulated with selected set of “handcrafted” texture features obtained after hierarchal feature fusion. These multiresolution and higher order features, which are able to characterize echotexture and roughness of liver surface, are extracted by using ranklet, gray-level difference matrix and gray-level co-occurrence matrix methods. Thereafter, these features are applied on proposed ensemble classifier that is designed with voting algorithm in conjunction with three classifiers, namely, k–nearest neighbor (k-NN), support vector machine (SVM), and rotation forest. The experiments are conducted to evaluate the (a) effectiveness of “handcrafted” texture features, (b) performance of proposed ensemble model, (c) effectiveness of proposed ensemble strategy, (d) performance of different classifiers, and (e) performance of proposed ensemble model based on Convolutional Neural Networks (CNN) features to differentiate four liver stages. These experiments are carried out on database of 754 segmented regions of interest formed by clinically acquired ultrasound images. The results show that classification accuracy of 96.6% is obtained by use of proposed classifier model.

scholarly journals Automated Intracranial Hematoma Classification in Traumatic Brain Injury (TBI) Patients Using Meta-Heuristic Optimization Techniques

Informatics ◽  
2022 ◽  
Vol 9 (1) ◽  
pp. 4
Author(s):  
Vidhya V ◽  
U. Raghavendra ◽  
Anjan Gudigar ◽  
Praneet Kasula ◽  
Yashas Chakole ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Heuristic Algorithms ◽  
Medical Condition ◽  
Texture Features ◽  
Clinical Diagnostics ◽  
Optimization Techniques ◽  
Automated System ◽  
Gray Level ◽  
Hu Moments

Traumatic Brain Injury (TBI) is a devastating and life-threatening medical condition that can result in long-term physical and mental disabilities and even death. Early and accurate detection of Intracranial Hemorrhage (ICH) in TBI is crucial for analysis and treatment, as the condition can deteriorate significantly with time. Hence, a rapid, reliable, and cost-effective computer-aided approach that can initially capture the hematoma features is highly relevant for real-time clinical diagnostics. In this study, the Gray Level Occurrence Matrix (GLCM), the Gray Level Run Length Matrix (GLRLM), and Hu moments are used to generate the texture features. The best set of discriminating features are obtained using various meta-heuristic algorithms, and these optimal features are subjected to different classifiers. The synthetic samples are generated using ADASYN to compensate for the data imbalance. The proposed CAD system attained 95.74% accuracy, 96.93% sensitivity, and 94.67% specificity using statistical and GLRLM features along with KNN classifier. Thus, the developed automated system can enhance the accuracy of hematoma detection, aid clinicians in the fast interpretation of CT images, and streamline triage workflow.

Is the intima-media thickness of temporal and axillary arteries influenced by cardiovascular risk?

Rheumatology ◽  
2021 ◽  
Author(s):  
María Victoria Martire ◽  
Edoardo Cipolletta ◽  
Andrea Di Matteo ◽  
Marco Di Carlo ◽  
Diogo Jesus ◽  
...  
Keyword(s):  
High Risk ◽  
Axillary Artery ◽  
Superficial Temporal Artery ◽  
Intima Media Thickness ◽  
Temporal Artery ◽  
Low Risk ◽  
Media Thickness ◽  
Temporal Arteries ◽  
The Us ◽  
Very High

Abstract Objectives 1) To measure with ultrasound (US) the intima-media thickness (IMT) of temporal (superficial, parietal and frontal branches) and axillary arteries in subjects without a diagnosis of giant-cell arteritis (GCA) and/or polymyalgia rheumatica (PMR) with different cardiovascular (CV) risk; 2) to assess the performance of previously proposed cut-off values for normal IMT. Methods Subjects ≥ 50 years without a diagnosis of GCA or PMR were included. Bilateral US of the temporal arteries, including the frontal and parietal branches, and axillary arteries was performed by two sonographers using a 10–22 MHz and 6–18 MHz probe. The following previously proposed cut-off for were considered: Superficial temporal artery: 0.42 mm; Frontal branch: 0.34 mm; Parietal branch: 0.29 mm; Axillary artery: 1.0 mm. Results A total of 808 arteries in 101 subjects were evaluated; of these, 31 (30.7%) were classified as very high-CV risk, 7 (6.9%) as high, 34 (33.7%) as moderate and 29 (28.7%) as low-risk. Subjects with very high or high-risk showed higher IMT than those with moderate or low-risk in the superficial temporal arteries [0.23 (SD 0.07) vs 0.20 (SD 0.04), p< 0.01] and in the axillary arteries [0.54 (SD 0.17) vs 0.48 (SD 0.10), p: 0.002]. The IMT was higher than the reference cut-off in 13/808 (1.6%) arteries, in ≥ 1 artery in 10/101 subjects (10.1%). Of these 10 subjects, 8 (80%) were classified as having very high or high risk. Conclusion Our results suggest that CV risk might influence the US-determined IMT of the temporal and axillary arteries in subjects without GCA. Therefore, in patients with suspected GCA, particular attention should be paid when measuring the IMT in those patients with very high/high CV risk.

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