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TF and CP are elevated in patients with cancer. The highest values of both procoagulants are in the genitourinary cancer group in agreement with the greater presence of thrombosis observed in this group. Clinical follow up is important in order to determine the potential value of these procoagulants and the tendency to develop thrombosis in patients with cancer.

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Over recent years breast cancer prevention campaigns have resulted in widespread screening. Until now, mammography has been one of the most reliable methods for the early detection of this disease. The high correlation between the appearance of microcalcification clusters and the presence of cancer shows that Computer Aided Detection systems of microcalcifications are extremely useful and helpful in an early detection of breast cancer. Several techniques can be adopted to accomplish this task. In this paper an efficient tool for a fully automatic microcalcification cluster detection/localization is presented. Adopting this procedure, all suspect microcalcifications are preserved and background noise is reduced by thresholding mammograms through a wavelet filter, according to image statistical parameters (i.e. mean gray level pixel value and standard deviation). Moreover, in order to localize singularity points, the reconstructed image is decomposed adopting another wavelet and each decomposition level is processed using a hard threshold technique. To reduce false positive detections in microcalcification recognition, the results obtained in each level are combined with a suitable procedure. The Mammographic Image Analysis Society database is used to test the procedure. The performance obtained highlights the validity of the method; indeed, the evaluated sensitivity parameter (true positive rate) is about 98% at an average rate of 1 false positive per image.

Cardiac signal processing is usually a computationally demanding task as signals are heavily contaminated by noise and other artifacts. In this paper, an effective approach for peak point detection and localization in noisy electrocardiogram (ECG) signals is presented. Six stages characterize the implemented method, which adopts the Hilbert transform and a thresholding technique for the detection of zones inside the ECG signal which could contain a peak. Subsequently, the identified zones are analyzed using the wavelet transform for R point detection and localization. The conceived signal processing technique has been evaluated, adopting ECG signals belonging to MIT-BIH Noise Stress Test Database, which includes specially selected Holter recordings characterized by baseline wander, muscle artifacts and electrode motion artifacts as noise sources. The experimental results show that the proposed method reaches most satisfactory performance, even when challenging ECG signals are adopted. The results obtained are presented, discussed and compared with some other R wave detection algorithms indicated in literature, which adopt the same database as a test bench. In particular, for a signal to noise ratio (SNR) equal to 6 dB, results with minimal interference from noise and artifacts have been obtained, since Se e +P achieve values of 98.13% and 96.91, respectively.

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The analysis of cardiac signals is still regarded as attractive by both the academic community and industry because it helps physicians in detecting abnormalities and improving the diagnosis and therapy of diseases. Electrocardiographic signal processing for detecting irregularities related to the occurrence of low-amplitude waveforms inside the cardiac signal has a considerable workload as cardiac signals are heavily contaminated by noise and other artifacts. This paper presents an effective approach for the detection of ventricular late potential occurrences which are considered as markers of sudden cardiac death risk. Three stages characterize the implemented method which performs a beat-to-beat processing of high-resolution electrocardiograms (HR-ECG). Fifteen lead HR-ECG signals are filtered and denoised for the improvement of signal-to-noise ratio. Five features were then extracted and used as inputs of a classifier based on a machine learning approach. For the performance evaluation of the proposed method, a HR-ECG database consisting of real ventricular late potential (VLP)-negative and semi-simulated VLP-positive patterns was used. Experimental results show that the implemented system reaches satisfactory performance in terms of sensitivity, specificity accuracy, and positive predictivity; in fact, the respective values equal to 98.33%, 98.36%, 98.35%, and 98.52% were achieved.