Majlesi Journal of Multimedia Processing
Volume:3 Issue: 3, Sep 2014

Neuromorphic VLSI is an important tool for investigating and implementing neural algorithms. The DPI-neuron analog implementation of neural network in silicon is the real-time conductance-based that has the low power consumption. This circuit implemented Exponential Integrated-and-Fire model that produces real time spiking behavior. In this paper we modify DPI-neuron circuit which can be produce different type of behavior biological neuron. It is suitable for applications in reconfigurable neuromorphic devices which implement biologically brain circuits. This circuit is simulated by HSPICE software in 0.35mm CMOS technology with supply voltage 3.3Volt.

In recent years multi scale transform application in image processing especially for magnetic resonance (MR) images has been raised. Wavelet transform is introduced as a useful tool in image processing and it is capable of effectively removing noise from magnetic resonance images. The main problem with wavelet transform is that it is not able to distinguish one dimensional or higher dimentional discontinuities in images. A proposed solution for this issue is an inseparable transform which is named Curvelet. Time frequency transform based noise elimination methods, usually rely on thresholding. In curvelet method, by setting a hard threshold at low levels of noise the obtained similarity index is 0.9254. which on average leads to 5 percent improvement compared with wavelet method. The results show the efficiency of this method in different parts of image processing on simulated and actual MR images.

The main purpose of medical image segmentation is to divide the image into anatomically different structures that makes possible the separation of desired parts, such as blood vessels and liver tumors, from their background. Low contrast and resolution make medical image segmentation complex. Moreover, noises and instrumental limitations of reconstruction algorithms and body movements of the patient adds to the complexity of the task. Appropriate initialization and optimal configuration need a high level of manual adjustments. On the other hand, appropriate initialization and optimal configuration of controlling parameters influence the performance of level set segmentation. This paper aimed at analyzing, implementing, and optimizing level set algorithm using fuzzy logic. The algorithm carried out initial segmentation using fuzzy clustering. Controlling parameters and level set initialization were estimated using fuzzy clustering. Finally, level set algorithm was exploited.

Image segmentation is often used as a first essential step in medical image processing. Fuzzy c-mean (FCM) is one of the best and most versatile methods of image segmentation, but this algorithm is not suitable for images with noise and spatial complexities. In this article we proposed a method to modify FCM algorithm using expert manual segmentation as prior knowledge. The proposed algorithm is implemented on real and simulated brain MR images. In real images, similarity index of three classes (white matter, gray matter, cerebrospinal fluid) had notable betterment and in simulated images with different noise levels and high number of clusters, evaluation criteria of white matter and gray matter improved.

Steganography is the art and science of invisible communication. It aims at hiding confidential information in digital media in a way to conceal the existence of information. There are three requirements in each steganographic system: capacity, imperceptibility and robustness. A trade-off between the capacity of the embedded data and the robustness to certain attacks is required, while keeping the perceptual quality of the stego-image at an acceptable level. In this paper, a color image steganography method in spatial domain is presented. In the process of embedding a secret data, a cover image is partitioned into 2*2 non overlapping blocks. A difference value is calculated from the values of the two contiguous pixels in each block. The number of bits that can be embedded in a pixel pair is specified based on the difference value. The difference value then is replaced by a new value to embed the value of a sub-stream of the secret message, and then the pixel value of the stego-image is obtained. Because this method utilizes all three planes of color image, the capacity of embedding data is increased.