The availability of images in different applications are augmented owing to the technological advancement. Hence, image compression has proved to be a valuable technique as one solution. This paper gives the review of compression techniques. Based on this, we recommended some general guidelines to choose the best compression algorithm for a medical image. In this method, the ROI is encoded separately using ISOM with high priority and high resolution and the back ground (BG) region which has a lower priority is separately encoded with a low resolution version of the ISOM. Finally, the two regions, are merged together to get the reconstructed image. Our results show that the proposed method gives very good image quality for diagnosis without any degradable loss. The performance of the compression technique is evaluated using the parameters (MSE, and PSNR) and achieved better result compared to other existing methods. As a result, we strongly believe that using our method we can overcome the limitations in storage and transmission of medical images that are produced day by day.

In medical images, noise suppression is a particularly delicate and difficult task. A tradeoff between noise reduction and the preservation of actual image features has to be made in a way that enhances the diagnostically relevant image content. The method of wavelet thresholding has been used extensively for denoising medical images. The idea is to transform the data into the wavelet basis, in which the large coefficients are mainly the signal and the smaller ones represent the noise. By suitably modifying these coefficients, the noise can be removed from the data. In this paper, we evaluate several two-dimensional denoising procedures using medical test images corrupted with additive Gaussian noise. Our results, using the peak-signal-to-noise ratio as a measure of the quality of denoising, show that the NormalShrink method outperforms the other wavelet-based techniques (VisuShrink, BayesShrink). We also demonstrate that garrote shrinkage offers advantages over both hard and soft shrinkage.

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In the framework of the image compression by transform coding, we studied two transformation techniques, respectively, used in the JPEG and JPEG2000 standards: the discrete cosine transform (DCT) and the wavelets transform (WT). We also studied the influence of different coding techniques on the compression rate.We compare the performances of different wavelets. The set of the techniques that we implemented have been applied on different types of images.We verified the compromise that exists between the compression rate and the quality of the reconstructed image.We noted that when we apply the wavelet transform, we succeeded with best results of those found by the use of the DCT.We note also that the quality of reconstruction is better as we increase the order of the filter associated to the Daubechies wavelets.

Capacities of road intersections are a limiting factor and crucial for the performance of road networks. Therefore, for purposes of intersection design and of optimal signal timing, numerous methodologies have been proposed to either estimate or directly measure the capacity of single movements at road intersections. However, both model-based estimation and direct measurement suffer from the large effort that is needed to gather the relevant data. Even worse, once the data are collected they only represent a snapshot of the capacity over time. This paper proposes an alternative approach to estimate capacity of signalized road intersections over time using only automatically generated trajectories of probe vehicles. The obtained capacity can be used to evaluate the effective degree of saturation using real demand, or to assess hypothetic different conditions in demand or signaling. The cyclic operation of signalized intersections allows for the accumulation of trajectories, and thus in practical applications for the compensation of potentially low penetration rates. Within a sequential process the intersection’s cycle time and the approach green time and saturation flow rates are determined. The determination of the cycle time and the green times is based on an existing approach. The derivation of the saturation flow rates relies on its direct dependency to the saturation time headway and uses two parameters to be calibrated. Testing with a commercial dataset on an intersection in Munich produced a good signal timing estimation and saturation flow values that are comparable to a calculation based on the German guideline.