Medical image segmentation is one of the most actively studied fields in the past few decades, as the development of modern imaging modalities such as magnetic resonance imaging (MRI) and computed tomography (CT), physicians and technicians nowadays have to process the increasing number and size of medical images. Therefore, efficient and accurate computational segmentation algorithms become necessary to extract the desired information from these large data sets. Moreover, sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures presented in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning. Many of the proposed algorithms could perform w

Background: Carpal tunnel syndrome is the most common entrapment neuropathy in humans today. For patients in whom conservative treatment fails, surgical decompression is indicated. Various surgical techniques are becoming increasingly popular. Due to the rapid postoperative recovery shown after endoscopic operations, proximal palmar mini-incision for carpal tunnel release is a comparative alternative. Methods: Ninety four patients [113 hands] with a carpal tunnel syndrome underwent carpal tunnel release through a 1-cm longitudinal incision made just over the distal flexor crease. The self- administered Boston Questionnaire was used to assess the severity of patients’ symptoms and their functional status, both before and after the surgical

Image retrieval is used in searching for images from images database. In this paper, content – based image retrieval (CBIR) using four feature extraction techniques has been achieved. The four techniques are colored histogram features technique, properties features technique, gray level co- occurrence matrix (GLCM) statistical features technique and hybrid technique. The features are extracted from the data base images and query (test) images in order to find the similarity measure. The similarity-based matching is very important in CBIR, so, three types of similarity measure are used, normalized Mahalanobis distance, Euclidean distance and Manhattan distance. A comparison between them has been implemented. From the results, it is conclud

Magnetic  Resonance  Imaging  (MRI)  is  one  of  the  most important diagnostic tool. There are many methods to segment the

tumor of human brain. One of these, the conventional method that uses pure image processing techniques that are not preferred because they need human interaction for accurate segmentation. But unsupervised methods do not require any human interference and can segment   the   brain   with   high   precision.   In   this   project,   the unsupervised  classification methods have been used in order to detect the tumor  disease from MRI images.    These metho

The computer vision branch of the artificial intelligence field is concerned with developing algorithms for analyzing video image content. Extracting edge information, which is the essential process in most pictorial pattern recognition problems. A new method of edge detection technique has been introduces in this research, for detecting boundaries.

           Selection of typical lossy techniques for encoding edge video images are also discussed in this research. The concentration is devoted to discuss the Block-Truncation coding technique and Discrete Cosine Transform (DCT) coding technique. In order to reduce the volume of pictorial data which one may need to store or transmit,

The minimization, treatment and disposal of drilling wastes especially oily wastes are important environmental issues.

In this research two fungal isolates named Pleurotus ostreatus and Trichoderma harzianum were chosen carefully f or the purpose of biotreatment of oily drilled cuttings which resulting from  drilling oil wells using oil based muds (OBMs).

A relationship of total petroleum hydrocarbon degradation in oily drilled cuttings with time has been obtained. The results showed that Pleurotus ostreatus and Trichoderma harzianum can be considered hydrocarbon degrading microorganisms and the used biotreatment is cost effective process since most of the materials used in the cultivation and growth of the present f

Multilevel models are among the most important models widely used in the application and analysis of data that are characterized by the fact that observations take a hierarchical form, In our research we examined the multilevel logistic regression model (intercept random and slope random model) , here the importance of the research highlights that the usual regression models calculate the total variance of the model and its inability to read variance and variations between levels ,however in the case of multi-level regression models, the calculation of  the total variance is inaccurate and therefore these models calculate the variations for each level of the model, Where the research aims to estimate the parameters of this m

   In the present study, serum GOT(Glutamic Oxalacetic Transaminase) was purified, the purified enzyme showed the maximum activity at 37ºC and pH 7.5. During purification of serum GOT ion exchange chromatography lead to four separate forms (termed I, II, III and IV). GOT II with  the highest specific activity was pure after chromatography on Sephacryl S300. . S.GOT levels were investigated in serum samples from patient with pulmonary tuberculosis .The S.GOT levels were determined at and 37ć .The study revealed that the serum GOT activity was higher in patient of tuberculous pulmonary than in control subjects.The mean serum GOT activity in the patients group was(90±8 IU/L) as compared to control group (27±0.65I