Mohamed Roushdy

Remote Computer-Aided Breast Cancer Detection and Diagnosis System Based on Cytological Images

The purpose of this study is to develop an intelligent remote detection and diagnosis system for breast cancer based on cytological images. First, this paper presents a fully automated method for cell nuclei detection and segmentation in breast cytological images. The locations of the cell nuclei in the image were detected with circular Hough transform. The elimination of false-positive (FP) findings (noisy circles and blood cells) was achieved using Otsus thresholding method and fuzzy c-means clustering technique. The segmentation of the nuclei boundaries was accomplished with the application of the marker-controlled watershed transform. Next, an intelligent breast cancer classification system was developed. Twelve features were presented to several neural network architectures to investigate the most suitable network model for classifying the tumor effectively. Four classification models were used, namely, multilayer perceptron using back-propagation algorithm, probabilistic neural network (PNN), learning vector quantization, and support vector machine (SVM). The classification results were obtained using tenfold cross validation. The performance of the networks was compared based on resulted error rate, correct rate, sensitivity, and specificity. Finally, we have merged the proposed computer-aided detection and diagnosis system with the telemedicine platform. This is to provide an intelligent, remote detection, and diagnosis system for breast cancer patients based on the Web service. The proposed system was evaluated using 92 breast cytological images containing 11502 cell nuclei. Experimental evidence shows that the proposed method has very effective results even in the case of images with high degree of blood cells and noisy circles. In addition, two benchmark data sets were evaluated for comparison. The results showed that the predictive ability of PNN and SVM is stronger than the others in all evaluated data sets.

Automated cell nuclei segmentation for breast fine needle aspiration cytology

Abstract Breast cancer detection and segmentation of cytological images is the standard clinical practice for the diagnosis and prognosis of breast cancer. This paper presents a fully automated method for cell nuclei detection and segmentation in breast cytological images. The images are enhanced with histogram stretching and contrast-limited adaptive histogram equalization (CLAHE). The locations of the cell nuclei in the image are detected with circular Hough transform (CHT) and local maximum filtering. The elimination of false positive findings (noisy circles and blood cells) is achieved using Otsu’s thresholding method and fuzzy C-means clustering technique. The segmentation of the nuclei boundaries is accomplished with the application of the marker controlled watershed transform in the gradient image, using the nuclei markers extracted in the detection step. The proposed method is evaluated using 92 breast cytological images containing 11,502 cell nuclei. Experimental evidence shows that the proposed method has very effective results even in the case of images with high degree of blood cells, noisy circles.

Quantization Table Estimation in JPEG Images

Most digital image forgery detection techniques require the doubtful image to be uncompressed and in high quality. However, most image acquisition and editing tools use the JPEG standard for image compression. The histogram of Discrete Cosine Transform coefficients contains information on the compression parameters for JPEGs and previously compressed bitmaps. In this paper we present a straightforward method to estimate the quantization table from the peaks of the histogram of DCT coefficients. The estimated table is then used with two distortion measures to deem images as untouched or forged. Testing the procedure on a large set of images gave a reasonable average estimation accuracy of 80% that increases up to 88% with increasing quality factors. Forgery detection tests on four different types of tampering resulted in an average false negative rate of 7.95% and 4.35% for the two measures respectively. acquisition devices (cameras, scanners, medical imaging devices) are configured differently in order to balance compression and quality. As described in (9,10), these differences can be used to identify the source camera model of an image. Moreover, Farid (11) describes JPEG ghosts as an approach to detect parts of an image that were compressed at lower qualities than the rest of the image and uses to detect composites. In this paper we present a straightforward method for estimating the quantization table of single JPEG compressed images and bitmaps. We verify the observation that while ignoring error terms, the maximum peak of the approximated histogram of a DCT coefficient matches the quantization step for that coefficient. This can help in determining compression history, i.e. if the bitmap was previously compressed and the quantization table that was used, which is particularly useful in applications like image authentication, artifact removal, and recompression with less distortion.

Extended Case-Based Behavior Control for Multi-Humanoid Robots

Intelligent control of multi-agent autonomous humanoid robots is a very complex problem, especially in the RoboCup domain. This is due to the dynamics of the environment and the complexity of behaviors that should be executed in real time. Moreover, the overall complexity increases since another mechanism for coordinating the robots behaviors should be involved. Throughout the past years, acceptable results have been obtained using different approaches to solve the behavior control problem (decision trees, fuzzy techniques, support vector machines, reinforcement learning, etc.). As case-based reasoning (CBR) is tightly related to the way humans reason, researches are now concentrated on finding the most relevant solutions that could handle behavior control problems of humanoid soccer robots using CBR techniques. This paper proposes an extended algorithm of a more complex architecture to control more complex soccer behaviors such as dribbling and goal scoring applied to multi-robot scenarios between attacker and goalie. The decomposition of features into a hierarchy of levels has led to reduced complexity of the overall behavior execution in real time. However, the average retrieval accuracy and the average performance accuracy were relatively low (70.3% and 77%, respectively). We intend to combine neural networks with CBR to learn adaptation rules for reducing the complexity of hand-coded rules and improving the overall controller performance.

A method for contactless palm ROI extraction

Palmprint can be extracted from a hand using a low-cost webcam in a contactless manner. Using a webcam makes the enrollment process fast and convenient for users. Being contactless solve the hygiene issue and avoid copying the latent prints from sensors surface. However, a number of challenges arise in such environment; geometric transformations, the existence of finger rings, hand accessories, and other false objects. This paper proposes a palm ROI extraction method that is robust to these challenges. The method is based on blob analysis, morphological and geometrical operations without a need to pre-train or parameter adjustment. Its tested on three available hand databases that cover these challenges; namely, Sfax, IITD and PolyU 3D/2D. The palm ROI is considered to be wrongly extracted if it contains part of the background. The method achieves an extraction error of 0%, 0.27% and 0.26% for the three DBs, respectively. Applying a massive rotation and scaling tests leads to a minor increase in the extraction errors by 0.24%, 0.35% and 0.84% for the three DBs, respectively.

Comparison of 3D feature registration techniques for indoor mapping

Maps are used extensively to understand the surrounding environment and to navigate through it. This has motivated research in localization and mapping and as a result numerous algorithms have been proposed to construct different types of maps. The mapping problem involves many difficulties such as: the estimation of the sensor position and orientation at each observation, the correct interpretation of data and the error minimization in aligning observations. In this paper a comprehensive overview of the visual SLAM problem is provided along with a comparison of different algorithms used in the construction of 3D maps. The algorithms have been tested on standard 3D datasets of indoor environments.

A Grid based Medical Image Management System using Alchemi

ABSTRACT A large number of medical images in digital format is generated by hospitals every day. It is acknowledged that medical image databases are a key component in diagnosis. The increasing trend towards digitization of medical images creates a need of technologies for storage, and retrieval of medical images. The paper discusses how to query the medical images and presented a medical images management system based on the DICOM (Digital Imaging and Communications in Medicine) standard .Grids are a promising tool to build medical databases and to face health-related challenges involving computations over large datasets. Indeed, grids offer an infrastructure for sharing data and building virtual databases distributed over several medical sites and sharing processing power. Alchemi grid framework has been deployed to provide grid-based environment. Speeding up the retrieval and feature extraction processes was one of the major achievements of this work.

C21. Single image motion deblurring using Genetic Algorithms

One of the key problems of restoring a degraded image from motion blur is the estimation of the unknown blur filter from a single input blurred image. Many blind deconvolution methods typically assume frequency-domain constraints on images, simplified parametric forms for the motion path during camera shake or use multiple input images. The paper proposes an algorithm for removing motion blur from a single input blurred image using Genetic Algorithms. Genetic Algorithms are applied in science and engineering as adaptive algorithms for optimizing practical problems. Also recent research in natural image statistics is exploited, which shows that photographs of natural scenes typically obey heavy-tailed distribution. Both linear and non-linear motion blur are handled. Experiments on a wide data set of standard images degraded with different kernels of different sizes demonstrate the efficiency of the proposed approach especially in small blur lengths.

Studying the Dissemination of the K-core Influence in Twitter Cascades

The k-core of an information graph is a common measure of a node connectedness in diverse applications. The k-core decomposition algorithm categorizes nodes into k-shells based on their connectivity. Previous research claimed that the super-spreaders are those located on the k-core of a social graph and the nodes become of less importance as they get assigned to a k-shell away from the k-core. We aim to evaluate the influence span of the social media super-spreaders, located at the k-core, in terms of the number of k-shells that their influence can reach. We base our methodology on the observation that the k-core size is directly correlated to the graph size under certain conditions. We explain these conditions and then investigate it further on real-life meme cascades extracted from Twitter. We utilize the correlation to assess the effectiveness of the k-core nodes for influence dissemination. The results of the carried-out experiments show that the correlation exists in our studied real-life datasets. A high correlation existed between the k-core size and the sizes of the inner k-shells in all the examined datasets. However, the correlation starts to decrease in the outer k-shells. Further investigations have shown that the k-shells that were less correlated exhibited a higher presence of spam accounts.

Machine Learning Techniques for Intelligent Access Control

Access control is a set of regulations that governs access to certain areas or information. By access we mean entering a specific area, or logging on a machine. The access regulated by a set of rules that specifies who is allowed to get access and what is the restrictions on such access. Across the years several access control systems have been developed. Due to the rapid advancement in technology over the past years, older systems are now easily by passed, thus the need to have new methods of access control. Biometrics is referred to as an authentication technique that relies on a computer system to electronically validate a measurable biological characteristic that is physically unique and cannot be duplicated. Biometrics has been used for ages as access control security system. In this chapter we will present several biometric techniques their usage, advantages and disadvantages.