Automatic landmarks detection in breast reconstruction aesthetic assessment.

This paper addresses a fully automatic landmarks detection method for breast reconstruction aesthetic assessment. The set of landmarks detected are the supraesternal notch (SSN), armpits, nipples, and inframammary fold (IMF). These landmarks are commonly used in order to perform anthropometric measurements for aesthetic assessment. The methodological approach is based on both illumination and morphological analysis. The proposed method has been tested with 21 images. A good overall performance is observed, although several improvements must be achieved in order to refine the detection of nipples and SSNs.

Development and Preliminary Evaluation of a Rapid Oligochromatographic Assay for Specific Detection of New Human Influenza A H1N1 Virus

A new oligochromatographic assay, Speed-Oligo Novel Influenza A H1N1, was designed and optimized for the specific detection of the 2009 influenza A H1N1 virus. The assay is based on a PCR method coupled to detection of PCR products by means of a dipstick device. The target sequence is a 103-bp fragment within the hemagglutinin gene. The analytical sensitivity of the new assay was measured with serial dilutions of a plasmid that contained the target sequence, and we determined that down to one copy per reaction of the plasmid was reliably detected. Diagnostic performance was assessed with 103 RNAs from suspected cases (40 positive and 63 negative results) previously analyzed with a reference real-time PCR technique. All positive cases were confirmed, and no false-positive results were detected with the new assay. No cross-reactions were observed when other viral strains or clinical samples with other respiratory viruses were tested. According to these results, this new assay has 100% sensitivity and specificity. The turnaround time for the whole procedure was 140 min. The assay may be especially useful for the specific detection of 2009 H1N1 virus in laboratories not equipped with real-time PCR instruments

Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker Recognition Techniques

This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition (ASR) techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea (OSA). Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects, we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model (GMM) pattern recognition on speech spectra. Finally, we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81% correct classification rate, which is very promising and underpins the interest in this line of inquiry.