Five rare cases of nasopharyngeal carcinoma with optic nerve involvement are reported. Computerised Tomographic Scan (CT Scan) studies were performed in four of them. Evidence of intracranial spread of the tumour, from the roof of the fossa of Rosenmuller to the apex of the orbit through the cavernous sinus, was noted in three patients. In one of them there was extracranial extension of the tumour, to the orbit through the posterior ethmoid.
In a prospective study of 50 patients with suspected tear of the meniscus of the knee, the clinical diagnosis, arthrographic and arthroscopic findings were compared at arthro-tomy. The clinical diagnosis was correct in 85%, arthrographic in 54%, and arthroscopy in 91%of the patients.
Wavelet packet transform decomposes a signal into a set of orthonormal bases (nodes) and provides opportunities to select an appropriate set of these bases for feature extraction. In this paper, multi-level basis selection (MLBS) is proposed to preserve the most informative bases of a wavelet packet decomposition tree through removing less informative bases by applying three exclusion criteria: frequency range, noise frequency, and energy threshold. MLBS achieved an accuracy of 97.56% for classifying normal heart sound, aortic stenosis, mitral regurgitation, and aortic regurgitation. MLBS is a promising basis selection to be suggested for signals with a small range of frequencies.
Heart murmurs are the first signs of cardiac valve disorders. Several studies have been conducted in recent years to automatically differentiate normal heart sounds, from heart sounds with murmurs using various types of audio features. Entropy was successfully used as a feature to distinguish different heart sounds. In this paper, new entropy was introduced to analyze heart sounds and the feasibility of using this entropy in classification of five types of heart sounds and murmurs was shown. The entropy was previously introduced to analyze mammograms. Four common murmurs were considered including aortic regurgitation, mitral regurgitation, aortic stenosis, and mitral stenosis. Wavelet packet transform was employed for heart sound analysis, and the entropy was calculated for deriving feature vectors. Five types of classification were performed to evaluate the discriminatory power of the generated features. The best results were achieved by BayesNet with 96.94% accuracy. The promising results substantiate the effectiveness of the proposed wavelet packet entropy for heart sounds classification.