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"It is realized that an important thing in medical image visualization serving is to be able to see human as observe. Nevertheless, certain noise is rising in image acquisition causes image quality is reducing. An image involvement is a process in which an image can be best analyzed. Denoising is a one of the image enhancement techniques. An adaptive thresholding technique based wavelet serves to reduce noise from medical image. A discrete wavalet transformation is used in this research. The STH (Soft Thresholding), HTH (Hard Thresholding), and MPTH (Multiscale Products Thresholding) methods are used to calculate and compare as medical image Denoising results. Two criteria, MSR (Mean-to-Standard Deviation Ratio) and CNR (Contrast-to-Noise Ratio) have proposed to perform as Denoising at medical image. From the result, it can be concluded that denoising by using MPTH (Multiscale Products Thresholding) method, the values of MSR (Mean-to-Standard Deviation Ratio), CNR (Contrast-to-Noise Ratio) are greater than STH (Soft Thresholding), and HTH (Hard Thresholding) can be obtained."

"Pada penelitian ini telah dibangun, sistem deteksi dan identifikasi untuk pengenalan dan klasifikasi komponen-komponen sinyal EEG, terhadap sinyal EEG asimetri yang diperoleh dari perekaman pada kanal simetri. Sinyal EEG diperoleh dari 7 nara coba pada dua kondisi pikiran, yaitu rileks dan berpikir (non-rileks). Terhadap koefisien wavelet dari sinyal asimetri yang diperoleh selanjutnya dilakukan analisis Power Spectral Density (PSD). Sistem Klasifikasi dibangun berdasarkan spektrum daya pada daerah gehmbang dan dengan uji hipotesis serta pengetahuan apriori tentang karakteristik energi komponen gehmbang EEG.Penggunaan transformasi wavelet dapat mengatasi sifat non-stasioner, dan modifikasi sistem klasifikasi dengan uji hipotesis, meningkatkan keberhasilan klasifikasi, sehingga pada kondisi rileks memberikan hasil 85% dan kondisi non-rileks sebesar 64%.Dibandingkan metoda yang lain, transformasi wavelet juga dapat mereduksi data tanpa kehilangan informasi yang berarti, Hal ini ditunjukkan oleh penyimpangan rekonstruksi koefisien wavelet terhadap sinyal asliyang kecil.Sementara pengaruh posisi elektroda terhadap keberhasilan pengamatan, diketahui bahwa posisi sentral memberikan keberhasilan terbaik sedangkan kanal occipital terburuk. Kondisi rileks ditunjukkan, hasil spektrum daya rata-rata seimbang untuk kanal yang simetrik dibanding kondisi non-rileks, Kurangnya keberhasilan yang diperoleh pada kondisi berpikir disebabkan kitrangnya konsistensi pada kondisi tersebut, disamping kondisi rileks belum sepenuhnya hilang.

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In this research a detection and identification system for pattern recognition and classification of wave components of an asymmetric of two symmetrical EEG signal were developed. The EEG signal was obtained from 7 subjects with two conditions, relax and non-relax. The detection and identification was based on the non-symmetry signal recorded on a symmetric channel test of hypothesis and priori learning of energy characteristic of component of the EEG signal.

In the proposed method, the wavelet approximated coefficient of the non-symmetry EEG signal was analyzed using power spectral density (PSD) method. The wavelet transformation is suitable for non-stationary signal, the results were better for non-relax, that was 64%. Alternatively, in the relax condition, the result was 85%.

The advantage of the wavelet transformation with respect to the other methods is that of can reduce the number of data without loss of information. It was shown by small deflection between reconstruction of wavelet and original signal.

In term of the channel position, occipital channel gives best result for relax condition, while central channel for non-relax. The less success of non-relax conditions because of lack of consistency of the condition, where some of alpha waves were remain."