Study of the influence of removing speckle noise on the segmentation of uterus ultrasonic images based on Active shape contour algorithm

The study suggests a new approach to segment the ultrasound uterus images to obtain the fetus region. The approach consists of three stages. The first includes the preprocessing in which the speckle noise is removed from the ultrasound images depending on sequential filtering of Gabor filter and median filter. Second, an improved active shape contour independent of edges is applied to segment the uterus images. The last stage is the post processing which depends on the morphological operation to eliminate the undesired region and obtain the region of interest (fetus). The designed system has been tested by means of medical database of ultrasound uterus images downloaded from the ULTRASCAN CENTRE site in Kaloor (India). The experimental tests show that the proposed sequential filtering technique improves the active shape contour algorithm performance significantly, so the system segment the uterus images correctly even in the presence of speckle noise.

Handwritten Signature Verification using Statistical Functions for Signature Image

This research suggests a new method that aims to verify the manual signature image which is written by person, and specify whether this signature back to this person or that forged signature. This was done by extracting geometric features of the signature image and applying statistical functions on them as a way to verify the signature of that person. The features from the signature image have been extracted on many stages so a signature image has been transformed from the gray scale to binary format, and then extracting the statistical features from the original signature image which is the maximum value from the most repeated values in the ones' coordination line that determine the signature shape, in addition to the number of ones which also determine the signature shape. Finally two ranges have been identified for the values accepted for original signature image. By the same way, statistical features have been extracted from the foreign signature image and tested if they aggregate within the specified domain of acceptable values. This research also includes the results of the proposed approach that compared with the previous methods in this scope. The proposed method has been tested to the data base consisting of 16200 signatures back to 300 persons, and as a result the signature image has been verified with a good percentage.

Ear segmentation using likelihood skin detector and morphological operations

This paper proposes a new approach for the segmentation of the side face images to obtain the ear region. The proposed approach is divided into two basic steps: The first step classifies the image pixels into skin and non-skin pixels using likelihood skin detector. This likelihood image is processed by using morphological operations to detect the ear region. In the second step, image containing ear region is isolated from side face image by using one of two methods; the first is based on experiment, while the second is based measurements. The study includes a comparison of the results between the proposed study and previous ones to identify the differences. The proposed approach is applied on a database containing 146 images of 20 persons. These images were taken under different illumination, pose, day, and location variations. The partial occlusion by hair or earing was also taken in account. The results showed that the system achieved a correct segmentation with rate 95.8%.

Access Time Reduction To Image Files Using Enhanced B+ Tree Indexing Method

The Research suggests a novel model aims to reduce the time of search for image files by proposing a new indexing mechanism to avoid the plague algorithm used with indexing so that the access time to these files becomes as less as possible. The first stage in this paper is to clarify the importance of archiving in organizing files via designing a database, storing images in it and recording the times needed to obtain the required files from the database. Then the indexing process for image files stored in the database is applied by proposing a new algorithm -B+ Tree enhanced- for organizing image files according to a certain mechanism to facilitate accessing any file, conducting queries and recording the times used to get those files from the database to compare them with the times required to access files before indexing in order to show the efficiency of the proposed method.

Design a Weighting and Fusion Model of Iris Features To Improve Performance Of Iris Recognition Systems

The study suggests designing a weighting model for iris features and selection of the best ones to show the effect of weighting and selection process on system performance. The search introduces a new weighting and fusion algorithm depends on the inter and intra class differences and the fuzzy logic. The output of the algorithm is the feature’s weight of the selected features. The designed system consists of four stages which are iris segmentation, feature extraction, feature weighting_selection_fusion model implementation and recognition. System suggests using region descriptors for defining the center and radius of iris region, then the iris is cropped and transformed into the polar coordinates via rotation and selection of radius-size pixels of fixed window from center to circumference. Feature extraction stage is done by wavelet vertical details and the statistical metrics of 1st and 2nd derivative of normalized iris image. At weighting and fusion step the best features are selected and fused for classification stage which is done by distance classifier. The algorithm is applied on CASIA database which consists of iris images related to 250 persons. It achieved 100% segmentation precision and 98.7% recognition rate. The results show that segmentation algorithm is robust against illumination and rotation variations and occlusion by eye lash and lid, and the weighting_selection_fusion algorithm enhances the system performance.

Retina Segmentation to Obtain the Optic Nerve Area and Blood Vessels

This paper proposes a new approach for the segmentation of the retina images to obtain the optic nerve and blood vessels regions. We used retinal images from DRIVE and STARE databases which include different situations like illumination variations, different optic nerve positions (left, right and center). Illumination problem has been solved by preprocessing stage including image histogram-based illumination correction. Next, some morphological operations were used to filter the preprocessed image to obtain the ROI region, then, the center and radius of optic nerve were determined, and the optic nerve region was extracted from the original image. In blood vessels segmentation, we applied the illumination correction and median filtering.Then the closing, subtraction and morphological operations were done to get the blood vessels image which was thresholded and thinned to get the final blood vessels image.

Fractal Compression of Stereo Images Using Block Matching and SGM Algorithms to Obtain the Disparity Map

Considering the increasing importance of stereo image compression and Fractal geometry becoming one of the most important fields of modern science ,we applied fractal image compression based on quadtree portioning method and global search algorithm , on a group of stereo image pairs . As the stereo image consists of two planar images , left and right .Both the left image ( reference image ) and the disparity map between left and right images , were compressed using fractal compression . We applied both block matching algorithm and Semi Global Method (SGM)to obtain the disparity map. The left image and the depth map were reconstructed using fractal decompression while the right image (target image ) was reconstructed using the reconstructed left image , disparity map and the error image between the original right image and the reconstructed right image that was build from the left image and the disparity map . The results were evaluated using quality objective measures which are MSE (Mean Square Error ) and PSNR (Peak Signal to Noise Ratio) and efficiency objective measures which are CR(Compression Ratio) and compression time . The results were compared with JPEG compression of stereo pairs based on Discrete Cosine Transform DCT and JPEG2000 compression of stereo pairs on stereo image based on Discrete Wavelet Transform DWT .

Algorithm designing for Features Extraction from CAD Files for Industrial Processes

In this paper, the algorithm was designed for cylinders, slots and pockets extraction from CAD models saved in STL file depending on rule-based method and graph-based method. Besides, windows application was designed using Visual Studio C# which allows the user to import CAD model and features extraction and view their geometric information (cylinder diameter, height, cylinder center coordinates, width, height, length for slots and pockets. In addition, all surfaces that the feature consists from. The proposed algorithm consists from multi-steps are: dividing input model into multi surfaces based on RegionGrowing method, next step is cylinder features extraction depending on rule-based method, slots and bockets extraction depending on graph-based method, calculating geometric information for each extracted feature. The results show that the proposed algorithm can extract cylinders, slots and pockets features from CAD models which saved in STL files and calculates geometric information for each extracted feature.

Prediction of Electric Power Consumption Using RNN Networks

The electric power service in the Syrian Arab Republic suffers from many difficulties resulting from the lack of resources (fuel), in addition to the sabotage of many generation centers by terrorist groups, which led to the implementation of rationing programs in the governorates according to the consumption of those governorates and the production centers located in them. (factories, pumping centers, hospitals and the population). Forecasting electric energy consumption also requires knowledge of daily consumption quantities, consumption times and other influencing factors that constitute large amounts of data. Predicting the exact electrical load is still a challenging task due to many problems such as the non-linear nature of the time series or the seasonal patterns it displays, which are very time consuming and affect the accuracy of the prediction performance. The process can be improved by using RNNs.[2] Initially, the optimal and appropriate consumption for the region was determined, compared with production and the possibility of passing the surplus to other backup operations or providing production centers with the surplus that could be obtained through the previous forecasting process. Also, Recurrent Neural Networks (RNN) were used, which are time series based on data sequences according to time indices and their ability to predict future values ​​based on past data. Then the performance of those networks was compared with DNN networks (Dense Neural Network) to obtain an optimal future prediction that can be served by the Ministry of Electricity in the Syrian Arab Republic and to solve the problem of predicting the electrical load compared to previous studies. The time-based successive division method has also been adopted, which has the ability to work more accurately for randomly sampled data. For cases of low regulation of the hourly data for wattage consumption, we can sample a set of data over time and take 20 percent of the data for example as training and test samples. Based on the prediction values ​​resulting from this study, work is being done to distribute electrical energy in the most appropriate manner and in accordance with the importance of higher usage.