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Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization
- Source :
- Computer methods and programs in biomedicine. 110(1)
- Publication Year :
- 2011
-
Abstract
- Characterization of carotid atherosclerosis and classification into either symptomatic or asymptomatic is crucial in terms of diagnosis and treatment planning for a range of cardiovascular diseases. This paper presents a computer-aided diagnosis (CAD) system (Atheromatic) that analyzes ultrasound images and classifies them into symptomatic and asymptomatic. The classification result is based on a combination of discrete wavelet transform, higher order spectra (HOS) and textural features. In this study, we compare support vector machine (SVM) classifiers with different kernels. The classifier with a radial basis function (RBF) kernel achieved an average accuracy of 91.7% as well as a sensitivity of 97%, and specificity of 80%. Thus, it is evident that the selected features and the classifier combination can efficiently categorize plaques into symptomatic and asymptomatic classes. Moreover, a novel symptomatic asymptomatic carotid index (SACI), which is an integrated index that is based on the significant features, has been proposed in this work. Each analyzed ultrasound image yields on SACI number. A high SACI value indicates that the image shows symptomatic and low value indicates asymptomatic plaques. We hope this SACI can support vascular surgeons during routine screening for asymptomatic plaques.
- Subjects :
- Discrete wavelet transform
Adult
Male
medicine.medical_specialty
Support Vector Machine
Wavelet Analysis
Health Informatics
Asymptomatic
Image Interpretation, Computer-Assisted
medicine
Humans
Carotid Stenosis
Diagnosis, Computer-Assisted
Ultrasound image
Aged
Ultrasonography
Aged, 80 and over
business.industry
Ultrasound
Tissue characterization
Middle Aged
Plaque, Atherosclerotic
Computer Science Applications
Support vector machine
Classification result
Female
Radiology
medicine.symptom
business
Software
Image based
Algorithms
Subjects
Details
- ISSN :
- 18727565
- Volume :
- 110
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Computer methods and programs in biomedicine
- Accession number :
- edsair.doi.dedup.....0fbe86f4025b4126fac13aa98efc5e1d