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In the recent years, image processing techniques are used as a tool to improve detection and diagnostic capabilities in the medical applications. Among these techniques, medical image enhancement algorithms play an essential role in the removal of the noise, which can be produced by medical instruments and during image transfer. Impulse noise is a major type of noise, which is produced by medical imaging systems, such as MRI, computed tomography (CT), and angiography instruments. An embeddable hardware module, which can denoise medical images before and during surgical operations, could be very helpful. In this paper, an accurate algorithm is proposed for real-time removal of impulse noise in medical images. Our algorithm categorizes all image blocks into three types of edge, smooth, and disordered areas. A different reconstruction method is applied to each category of blocks for noise removal. The proposed method is tested on MR images. Simulation results show acceptable denoising accuracy for various levels of noise. Also, an field programmable gate array (FPGA) implementation of our denoising algorithm shows acceptable hardware resource utilization. Hence, the algorithm is suitable for embedding in medical hardware instruments such as radiosurgery devices.

Polymorphism in the genes encoding CYP2C9 enzyme and VKORC1 reductase significantly influence warfarin dose requirement since patients with CYP2C9*2, CYP2C9*3 and VKORC1 mutant alleles require lower warfarin maintenance doses. Studies have reported the ethnic variations in the frequency of these genes within the various populations in Iran and other parts of the world. However, no such study has been done yet on Kurdish population in Kermanshah. From Kurdish population of Kermanshah province in Iran, a total of 110 patients who had heart surgery and taking warfarin, were genotyped for polymorphisms of VKORC1-1639 G>A, CYP2C9*2, and CYP2C9*3. Polymorphism genotyping was performed by sequencing as well as polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) using restriction enzymes of MspI, AVAII and KpnI, respectively. The frequencies of VKORC1-1639 GG, GA, and AA genotypes were 42%, 36%, and 22%, respectively and for CYP2C9 1*/1*, 1*/2*, 2*/2*, 1*/3*, 3*/3*, 2*/3* were 71%, 17%, 5.4%, 1.8%, 4.5%, and 0%, respectively. The frequency of VKORC1-1639A allele was 42.3% and the frequencies of CYP2C9*2 and *3 alleles were 14% and 5.4%, respectively. It was indicated that low warfarin dose requirements are strongly associated with the presence of CYP2C9 and VKORC1-1639 variant alleles. Our results confirmed the supply to understand the distribution of genomic biomarkers related to the drugs metabolism for future planning health programs.

Noise is an important factor that degrades the quality of medical images. Impulse noise is a common noise caused by malfunctioning of sensor elements or errors in the transmission of images. In medical images due to presence of white foreground and black background, many pixels have intensities similar to impulse noise and hence the distinction between noisy and regular pixels is difficult. Therefore, it is important to design a method to accurately remove this type of noise. In addition to the accuracy, the complexity of the method is very important in terms of hardware implementation. In this paper a low complexity de-noising method is proposed that distinguishes between noisy and non-noisy pixels and removes the noise by local analysis of the image blocks. All steps are designed to have low hardware complexity. Simulation results show that in the case of magnetic resonance images, the proposed method removes impulse noise with an acceptable accuracy.

With the increasing use of telemedicine there is a great demand in real-time processing and transmission of medical images. Noise is one of the important factors that degrade the quality of medical images. Impulse noise is a common noise that could be caused by malfunctioning of sensors or by data transmission errors. It is one the most common noises that have extensively been studied in recent years. For real-time noise removal hardware techniques are more suited, since software methods are complex and slow. Usually hardware techniques have low complexity and low accuracy. In this paper a low complexity, high accuracy, de-noising method is proposed. It first categorizes image pixels into a number of groups. Then noisy pixels are restored in different ways in each category. Local analysis of image blocks allows us to restore a noisy pixel by using its neighboring non-noisy pixels. All steps are designed to have low hardware complexity. Simulation results show that in the case of MR images, the proposed method removes impulse noise with acceptable accuracy.

Camera sensor network detection and localization strategies have a great effect on network lifetime. In order to energy consumption optimization, it is necessary to minimize the number of communications between camera sensor nodes. In all previous works in the field of camera selection, selection a set of cameras which lead to resource saving was studied. Direction consideration in motion of target can be useful in selecting a set of cameras to reduce the communication costs. In this regards we introduce motion direction as a new criterion to improve node selection in detection and localization phase. Based on this new criterion we have analyzed different decision strategies which can be lead to reduction in energy consumption. We applied the best discussed strategy on this paper in a random motion path condition in two phases including detection and localization. Simulation results show that, improvement of network lifetime can be reached by proposed method.