Background. Aneurysms of the distal anterior cerebral artery (DACA) are uncommon; they often form near the pericallosal-callosomarginal junction and are typically small. To our knowledge, giant DACA aneurysms developing from the more distant parts of the anterior cerebral artery (ACA), A4-5, have been described only once in the literature. Case description. A 66-year-old gentleman reported with a brief loss of consciousness followed by weakness in his right lower leg. The patient was admitted with a Glasgow Coma Score (GCS) of 15. A computed tomography (CT) scan of the head revealed a left hyperdense mass in the frontal parasagittal supracallosal region. Contrast MRI revealed a heterogeneously enhancing mass measuring 35x30x25 mm. C

Objective: To evaluate the functional outcomes after extended curettage and reconstruction using a combination of bone graft and bone cement (sandwich). Methodology: In this prospective case series 16 skeletally mature patients with primary giant cell tumor around the knee were included. Patients with previous surgically treated, malignant transformation, degenerative knee changes and those presenting with pathological fracture were excluded. The tumor was excised with bone graft filling space beneath the articular cartilage and a block of gel foam was placed over the cortical surface of picked bone graft. Remaining cavity was filled with polymethylmethacrylate cement (sandwich) with or without internal fixation. The func tional evaluation

Abstract (Iraq post – war reconstruction strategy: analytical study in fact and future prospects.) There search aims at identifying the causes of internal instability in Iraq from the political ,economic , social and security aspects , to diagnose the imbalance and to work towards finding real solutions to this . The study also aims at identifying the reconstruction proposals to completely eliminate the organizationof the terrorist advocate, as well as to know. theproblem of research lies in the ability of Iraqi political elites to achieve internal stability and to employ reconstruction proposals at all levels . The hypothesis of the research lies in the equation that the process of building stability (political – economic)and (socia

Abstract

Although the rapid development in reverse engineering techniques, 3D laser scanners can be considered the modern technology used to digitize the 3D objects, but some troubles may be associate this process due to the environmental noises and limitation of the used scanners. So, in the present paper a data pre-processing algorithm has been proposed to obtain the necessary geometric features and mathematical representation of scanned object from its point cloud which obtained using 3D laser scanner (Matter and Form) through isolating the noised points. The proposed algorithm based on continuous calculations of chord angle between each adjacent pair of points in point cloud. A MATLAB program has been built t

In this paper, the goal of proposed method is to protect data against different types of attacks by unauthorized parties. The basic idea of proposed method is generating a private key from a specific features of digital color image such as color (Red, Green and Blue); the generating process of private key from colors of digital color image performed via the computing process of color frequencies for blue color of an image then computing the maximum frequency of blue color, multiplying it by its number and adding process will performed to produce a generated key. After that the private key is generated, must be converting it into the binary representation form. The generated key is extracted from blue color of keyed image then we selects a c

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

Many approaches of different complexity already exist to edge detection in
color images. Nevertheless, the question remains of how different are the results
when employing computational costly techniques instead of simple ones. This
paper presents a comparative study on two approaches to color edge detection to
reduce noise in image. The approaches are based on the Sobel operator and the
Laplace operator. Furthermore, an efficient algorithm for implementing the two
operators is presented. The operators have been applied to real images. The results
are presented in this paper. It is shown that the quality of the results increases by
using second derivative operator (Laplace operator). And noise reduced in a good

Kidney tumors are of different types having different characteristics and also remain challenging in the field of biomedicine. It becomes very important to detect the tumor and classify it at the early stage so that appropriate treatment can be planned. Accurate estimation of kidney tumor volume is essential for clinical diagnoses and therapeutic decisions related to renal diseases. The main objective of this research is to use the Computer-Aided Diagnosis (CAD) algorithms to help the early detection of kidney tumors that addresses the challenges of accurate kidney tumor volume estimation caused by extensive variations in kidney shape, size and orientation across subjects.
In this paper, have tried to implement an automated segmentati

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database