Elastance is a distinguished marker in diagnosing various arterial diseases as studies have reported carotid artery-related diseases linked with stiffness index (β) values greater than 5. This study was to estimate elasticity of common carotid artery by measuring the diameter during systolic and diastolic phases using pixel tracing of successive frames and blood pressure. The B-mode ultrasonography video containing arterial wall motion was captured and fragmented into image frames. Each pixel on the greyscale image was converted into RGB intensity values. The diameter of the artery as well as the thickness of the wall was measured by tracing the pixel displacements from successive frames during arterial pulsation. The study was conducted on 19 subjects aged 25-40 years. The systolic and diastolic carotid artery lumen diameters and carotid intima-media thickness were calculated as 7.1 ± 0.7, 6.3 ± 0.6 and 0.5 ± 0.05 mm (mean ± standard deviation), respectively. The mean stiffness index (β), Peterson's modulus and Young's modulus of elasticity were 5.2 ± 1.1, 69 ± 15 kPa and 453 ± 99 kPa, respectively. The pixel displacements in tunica intima, tunica media and tunica adventitia were not homogeneous, due to varied macro-constituents such as endothelial tissues, smooth muscle cells, elastin lamina, fibrous tissue and micro-constituents such as collagen, fibroblast and elastin. We found that women have smaller arteries, and the stiffness increased during the systolic phase.
a b s t r a c tEarly detection of breast cancer requires accurate prediction and reliable diagnostic modalities. This allows physicians to distinguish malignant tumours before proceeding with a painful surgical biopsy. The attributes of three non-invasive primary diagnosing modalities, namely symptomatic examination, ultrasound imaging, and mammographic results, were used for the study. A dataset was created using ten selected features from each modality, after iterations during the training phase. The number of satisfying features was used for the creation of a model, which was further categorised as benign or malignant class. The model was evaluated in the testing phase by comparing biopsy results for benign or malignant classification. The statistical analysis proved it as an efficient approach for non-invasive decision-making. The developed model was tested using supervised learning algorithms with three classifiers for 210 cases by comparing the results with the gold standard biopsy results. The sensitivities for the three classifiers were 80%, 73% and 76.5%, while specificities were 96%, 94.4% and 95%, respectively. This method of breast tumour differentiation using the features of the non-invasive modalities can be widely used in telemedicine applications, helping to reduce confirmatory biopsies.
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