Background: Determination of sex and estimation of stature from the skeleton is vital to medicolegal investigations. Skull is composed of hard tissue and is the best preserved part of skeleton after death, hence, in many cases it is the only available part for forensic examination. Lateral cephalogram is ideal for the skull examination as it gives details of various anatomical points in a single radiograph. This study was undertaken to evaluate the accuracy of digital cephalometric system as quick, easy and reproducible supplement tool in sex determination in Iraqi samples in different age range using certain linear and angular craniofacial measurements in predicting sex. Materials and Method The sample consisted of 113of true lateral cepha

In this paper the behavior of the quality of the gradient that implemented on an image as a function of noise error is presented. The cross correlation coefficient (ccc) between the derivative of the original image before and after introducing noise error shows dramatic decline compared with the corresponding images before taking derivatives. Mathematical equations have been constructed to control the relation between (ccc) and the noise parameter.

Image compression is one of the data compression types applied to digital images in order to reduce their high cost for storage and/or transmission. Image compression algorithms may take the benefit of visual sensitivity and statistical properties of image data to deliver superior results in comparison with generic data compression schemes, which are used for other digital data. In the first approach, the input image is divided into blocks, each of which is 16 x 16, 32 x 32, or 64 x 64 pixels. The blocks are converted first into a string; then, encoded by using a lossless and dictionary-based algorithm known as arithmetic coding. The more occurrence of the pixels values is codded in few bits compare with pixel values of less occurre

Nowadays, internet security is a critical concern; the One of the most difficult study issues in network security is "intrusion detection". Fight against external threats. Intrusion detection is a novel method of securing computers and data networks that are already in use. To boost the efficacy of intrusion detection systems, machine learning and deep learning are widely deployed. While work on intrusion detection systems is already underway, based on data mining and machine learning is effective, it requires to detect intrusions by training static batch classifiers regardless considering the time-varying features of a regular data stream. Real-world problems, on the other hand, rarely fit into models that have such constraints. Furthermor

For several applications, it is very important to have an edge detection technique matching human visual contour perception and less sensitive to noise. The edge detection algorithm describes in this paper based on the results obtained by Maximum a posteriori (MAP) and Maximum Entropy (ME) deblurring algorithms. The technique makes a trade-off between sharpening and smoothing the noisy image. One of the advantages of the described algorithm is less sensitive to noise than that given by Marr and Geuen techniques that considered to be the best edge detection algorithms in terms of matching human visual contour perception.

Security concerns in the transfer of medical images have drawn a lot of attention to the topic of medical picture encryption as of late. Furthermore, recent events have brought attention to the fact that medical photographs are constantly being produced and circulated online, necessitating safeguards against their inappropriate use. To improve the design of the AES algorithm standard for medical picture encryption, this research presents several new criteria. It was created so that needs for higher levels of safety and higher levels of performance could be met. First, the pixels in the image are diffused to randomly mix them up and disperse them all over the screen. Rather than using rounds, the suggested technique utilizes a cascad