Journal of Medical Signals and Sensors
Volume:5 Issue: 4, Oct-Dec 2015

Although there are many methods for image denoising, but partial differential equation (PDE) based denoising attracted much attention in the field of medical image processing such as magnetic resonance imaging (MRI). The main advantage of PDE-based denoising approach is laid in its ability to smooth image in a nonlinear way, which effectively removes the noise, as well as preserving edge through anisotropic diffusion controlled by the diffusive function. This function was first introduced by Perona and Malik (P-M) in their model. They proposed two functions that are most frequently used in PDE-based methods. Since these functions consider only the gradient information of a diffused pixel, they cannot remove noise in noisy images with low signal-to-noise (SNR). In this paper we propose a modified diffusive function with fractional power that is based on pixel similarity to improve P-M model for low SNR. We also will show that our proposed function will stabilize the P-M method. As experimental results show, our proposed function that is modified version of P-M function effectively improves the SNR and preserves edges more than P-M functions in low SNR.

Acoustic analysis of sounds produced during speech provides significant information about the physiology of larynx and vocal tract. The analysis of voice power spectrum is a fundamental sensitive method of acoustic assessment that provides valuable information about the voice source and characteristics of vocal tract resonance cavities. The changes in long-term average spectrum (LTAS) spectral tilt and harmony to noise ratio (HNR) were analyzed to assess the voice quality before and after functional rhinoplasty in patients with internal nasal valve collapse. Before and 3 months after functional rhinoplasty, 12 participants were evaluated and HNR and LTAS spectral tilt in /a/ and /i/ vowels were estimated. It was seen that an increase in HNR and a decrease in LTAS spectral tilt existed after surgery. Mean LTAS spectral tilt in vowel /a/ decreased from 2.37 ± 1.04 to 2.28 ± 1.17 (P = 0.388), and it was decreased from 4.16 ± 1.65 to 2.73 ± 0.69 in vowel /i/ (P = 0.008). Mean HNR in the vowel /a/ increased from 20.71 ± 3.93 to 25.06 ± 2.67 (P = 0.002), and it was increased from 21.28 ± 4.11 to 25.26 ± 3.94 in vowel /i/ (P = 0.002). Modification of the vocal tract caused the vocal cords to close sufficiently, and this showed that although rhinoplasty did not affect the larynx directly, it changes the structure of the vocal tract and consequently the resonance of voice production. The aim of this study was to investigate the changes in voice parameters after functional rhinoplasty in patients with internal nasal valve collapse by computerized analysis of acoustic characteristics

In this paper, an optimal algorithm is presented for de-noising of medical images. The presented algorithm is based on improved version of local pixels grouping and principal component analysis. In local pixels grouping algorithm, blocks matching based on L2 norm method is utilized, which leads to matching performance improvement. To evaluate the performance of our proposed algorithm, peak signal to noise ratio (PSNR) and structural similarity (SSIM) evaluation criteria have been used, which are respectively according to the signal to noise ratio in the image and structural similarity of two images. The proposed algorithm has two de-noising and cleanup stages. The cleanup stage is carried out comparatively; meaning that it is alternately repeated until the two conditions based on PSNR and SSIM are established. Implementation results show that the presented algorithm has a significant superiority in de-noising. Furthermore, the quantities of SSIM and PSNR values are higher in comparison to other methods.

Arterial pulse measurement is one of the most important methods for evaluation of healthy conditions. In traditional Iranian medicine (TIM), physician may detect radial pulse by holding four fingers on the patient’s wrist. By using this method, under standard condition, the detected pulses are subjective and erroneous, in case of weak and/or abnormal pulses, the ambiguity of diagnosis may rise. In this paper, we present an equipment which is designed and implemented for automation of traditional pulse detection method. By this novel system, the developed noninvasive diagnostic method and database based on the TIM are way forward to apply tradition medicine and diagnose patients with present technology. The accuracy for period measuring is 76% and systolic peak is 72%.

Automatic segmentation of multiple sclerosis (MS) lesions in brain magnetic resonance imaging (MRI) has been widely investigated in the recent years with the goal of helping MS diagnosis and patient follow‑up. In this research work, Gaussian mixture model (GMM) has been used to segment the MS lesions in MRIs, including T1‑weighted (T1‑w), T2‑w, and T2‑fluid attenuation inversion recovery. Usually, GMM is optimized by using expectation‑maximization (EM) algorithm. The drawbacks of this optimization method are, it does not converge to optimal maximum or minimum and furthermore, there are some voxels, which do not fit the GMM model and have to be rejected. So, GMM is time‑consuming and not too much efficient. To overcome these limitations, in this research study, at the first step, GMM was applied to segment only T1‑w images by using 100 various starting points when the maximum number of iterations was considered to be 50. Then segmentation results were used to calculate the parameters of the other two images. Furthermore, FAST‑trimmed likelihood estimator algorithm was applied to determine which voxels should be rejected. The output result of the segmentation was classified in three classes; White and Gray matters, cerebrospinal fluid, and some rejected voxels which prone to be MS. In the next phase, MS lesions were detected by using some heuristic rules. This new method was applied on the brain MRIs of 25 patients from two hospitals. The automatic segmentation outputs were scored by two specialists and the results show that our method has the capability to segment the MS lesions with dice similarity coefficient score of 0.82. The results showed a better performance for the proposed approach, in comparison to those of previous works with less time‑consuming.

In current years, the application of biopotential signals has received a lot of attention in literature. One of these signals is an electromyogram (EMG) generated by active muscles. Surface EMG (sEMG) signal is recorded over the skin, as the representative of the muscle activity. Since its amplitude can be as low as 50 µV, it is sensitive to undesirable noise signals such as power‑line interferences. This study aims at designing a battery‑powered portable four channel sEMG signal acquisition system. The performance of the proposed system was assessed in terms of the input voltage and current noise, noise distribution, and synchronization and input noise level among different channels. The results indicated that the designed system had several inbuilt operational merits such as low referred to input noise (lower than 0.56 µV between 8 Hz and 1000 Hz), considerable elimination of power‑line interference and satisfactory recorded signal quality in terms of signal‑to‑noise ratio. The muscle conduction velocity was also estimated using the proposed system on the brachial biceps muscle during isometric contraction. The estimated values were in then normal ranges. In addition, the system included a modular configuration to increase the number of recording channels up to 96.

To make an accurate estimation of the uptake of radioactivity in an organ using the conjugate view method, corrections of physical factors, such as background activity, scatter, and attenuation are needed. The aim of this study was to evaluate the accuracy of four different methods for background correction in activity quantification of the heart in myocardial perfusion scans. The organ activity was calculated using the conjugate view method. A number of 22 healthy volunteers were injected with 17–19 mCi of 99mTc‑methoxy‑isobutyl‑isonitrile (MIBI) at rest or during exercise. Images were obtained by a dual‑headed gamma camera. Four methods for background correction were applied: (1) Conventional correction (referred to as the Gates’ method), (2) Buijs method, (3) BgdA subtraction, (4) BgdB subtraction. To evaluate the accuracy of these methods, the results of the calculations using the above‑mentioned methods were compared with the reference results. The calculated uptake in the heart using conventional method, Buijs method, BgdA subtraction, and BgdB subtraction methods was 1.4 ± 0.7% (P < 0.05), 2.6 ± 0.6% (P < 0.05), 1.3 ± 0.5% (P < 0.05), and 0.8 ± 0.3% (P < 0.05) of injected dose (I.D) at rest and 1.8 ± 0.6% (P > 0.05), 3.1 ± 0.8% (P > 0.05), 1.9 ± 0.8% (P < 0.05), and 1.2 ± 0.5% (P < 0.05) of I.D, during exercise. The mean estimated myocardial uptake of 99mTc‑MIBI was dependent on the correction method used. Comparison among the four different methods of background activity correction applied in this study showed that the Buijs method was the most suitable method for background correction in myocardial perfusion scan.

In proclaiming an International Year focusing on the topic of light science and its applications, the UN has recognized the importance of raising global awareness about how light-based technologies promote sustainable development and provide solutions to global challenges in energy, education, agriculture, and health. Light plays a vital role in our daily lives and is an imperative cross-cutting discipline of science in the 21st century. It has revolutionized medicine, opened up international communication via the internet, and continues to be central to linking cultural, economic, and political aspects of the global society.