Detection is a crucial step towards efficiently diagnosing network traffic anomalies within an Autonomous System (AS). We propose the adoption of nonextensive entropya one-parameter generalization of Shannon entropy-to detect anomalies in network traffic within an AS. Experimental results show that our approach based on nonextensive entropy outperforms previous ones based on classical entropy while providing enhanced flexibility, which is enabled by the possibility of finetuning the sensitivity of the detection mechanism.

Recent statistics show that breast cancer is a major cause of death among women in all of the world. Hence, early diagnostic with Computer Aided Diagnosis (CAD) systems is a very important tool. This task is not easy due to poor ultrasound resolution and large amount of patient data size. Then, initial image segmentation is one of the most important and challenging task. Among several methods for medical image segmentation, the use of entropy for maximization the information between the foreground and background is a well known and applied technique. But, the traditional Shannon entropy fails to describe some physical systems with characteristics such as long-range and longtime interactions. Then, a new kind of entropy, called nonextensive entropy, has been proposed in the literature for generalizing the Shannon entropy. In this paper, we propose the use of non-extensive entropy, also called q-entropy, applied in a CAD system for breast cancer classification in ultrasound of mammographic exams. Our proposal combines the non-extensive entropy, a level set formulation and a Support Vector Machine framework to achieve better performance than the current literature offers. In order to validate our proposal, we have tested our automatic protocol in a data base of 250 breast ultrasound images (100 benign and 150 malignant). With a cross-validation protocol, we demonstrate system's accuracy, sensitivity, specificity, positive predictive value and negative predictive value as: 95%, 97%, 94%, 92% and 98%, respectively, in terms of ROC (Receiver Operating Characteristic) curves and A z areas.

Evaluation of the carotid artery wall is essential for the assessment of a patient's cardiovascular risk or for the diagnosis of cardiovascular pathologies. This paper presents a new, completely user-independent algorithm called carotid artery intima layer regional segmentation (CAILRS, a class of AtheroEdge™ systems), which automatically segments the intima layer of the far wall of the carotid ultrasound artery based on mean shift classification applied to the far wall. Further, the system extracts the lumen-intima and media-adventitia borders in the far wall of the carotid artery. Our new system is characterized and validated by comparing CAILRS borders with the manual tracings carried out by experts. The new technique is also benchmarked with a semi-automatic technique based on a first-order absolute moment edge operator (FOAM) and compared to our previous edge-based automated methods such as CALEX (Molinari et al 2010 J. Ultrasound Med. 29 399-418, 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 1112-24), CULEX (Delsanto et al 2007 IEEE Trans. Instrum. Meas. 56 1265-74, Molinari et al 2010 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 57 1112-24), CALSFOAM (Molinari et al Int. Angiol. (at press)), and CAUDLES-EF (Molinari et al J. Digit. Imaging (at press)). Our multi-institutional database consisted of 300 longitudinal B-mode carotid images. In comparison to semi-automated FOAM, CAILRS showed the IMT bias of -0.035 ± 0.186 mm while FOAM showed -0.016 ± 0.258 mm. Our IMT was slightly underestimated with respect to the ground truth IMT, but showed uniform behavior over the entire database. CAILRS outperformed all the four previous automated methods. The system's figure of merit was 95.6%, which was lower than that of the semi-automated method (98%), but higher than that of the other automated techniques.

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