Mohammed J. Islam

AN EFFICIENT AUTOMATIC MASS CLASSIFICATION METHOD IN DIGITIZED MAMMOGRAMS USING ARTIFICIAL NEURAL NETWORK

In this paper we present an efficient computer aided mass classification method in digitized mammograms using Artificial Neural Network (ANN), which performs benign-malignant classification on region of interest (ROI) that contains mass. One of the major mammographic characteristics for mass classification is texture. ANN exploits this important factor to classify the mass into benign or malignant. The statistical textural features used in characterizing the masses are mean, standard deviation, entropy, skewness, kurtosis and uniformity. The main aim of the method is to increase the effectiveness and efficiency of the classification process in an objective manner to reduce the numbers of false-positive of malignancies. Three layers artificial neural network (ANN) with seven features was proposed for classifying the marked regions into benign and malignant and 90.91% sensitivity and 83.87% specificity is achieved that is very much promising compare to the radiologists sensitivity 75%.

Survey over VANET Routing Protocols for Vehicle to Vehicle Communication

VANET (Vehicular Ad-hoc Network) is an emerging new technology with some unique characteristics that makes it different from other ad hoc network. Due to rapid topology changing and frequent disconnection it is also difficult to design an efficient routing protocol for routing data among vehicles, called V2V or vehicle to vehicle communication and vehicle to road side infrastructure, called V2I. Because of road accident daily occurrence VANET is one of the influencing areas for the improvement of Intelligent Transportation System (ITS) which can increase road safety and provide traffic information etc. The existing routing protocols for VANET are not efficient to meet every traffic scenarios. Suitable routing protocols are required to establish communication between vehicles in future for road safety. In this paper, we focus on the merits and demerits of routing protocols which will help to develop new routing protocols or improvement of existing routing protocol in near future.

Capsule image segmentation in pharmaceutical applications using edge-based techniques

Real-time quality inspection of gelatin capsules in pharmaceutical applications is an important issue from the point of view of industry productivity and competitiveness. Computer vision-based automatic quality inspection and controller system is one of the solutions to this problem. In computer-vision based system a digital image obtained by a digital camera would usually have 24-bit color image. The analysis of an image with that many levels might require complicated image processing techniques. But in real-time application, where a part has to be inspected within a few milliseconds, either we have to reduce the image to a more manageable number of grey levels, usually two levels (binary image), and at the same time retain all necessary features of the original image or develop a complicated technique. A binary image can be obtained by thresholding the original image into two levels that is used as an input to the high throughput quality inspection system. In this paper, we develop a system using edge-based image segmentation techniques for quality inspection that satisfy the industrial requirements in pharmaceutical applications.

Computer-aided detection and classification of masses in digitized mammograms using artificial neural network

In this paper we present a computer aided diagnosis (CAD) system for mass detection and classification in digitized mammograms, which performs mass detection on regions of interest (ROI) followed by the benign-malignant classification on detected masses. In order to detect mass effectively, a sequence of preprocessing steps are proposed to enhance the contrast of the image, remove the noise effects, remove the x-ray label and pectoral muscle and locate the suspicious masses using Haralick texture features generated from the spatial gray level dependence (SGLD) matrix. The main aim of the CAD system is to increase the effectiveness and efficiency of the diagnosis and classification process in an objective manner to reduce the numbers of false-positive of malignancies. Artificial neural network (ANN) is proposed for classifying the marked regions into benign and malignant and 83.87% correct classification for benign and 90.91% for malignant is achieved.

Neural Network Based Handwritten Digits Recognition- An Experiment and Analysis

— Handwritten digit recognition has become very useful in endeavors of human/computer interaction. Reliable, fast, and flexible recognition methodologies have elevated the utility. This paper presents an experiment and analysis of the Neural Network classifier to recognize handwritten digits based on a standard database. The experimental setup implemented in Matlab determines the ability of a Multi-Layer Neural Network to identify handwritten digit samples 5-9. This network is the representative for recognition of remaining digits 0-4. We consider not only accurate recognition rate, but also training time, recognition time as well as the complexity of the networks. The Multi-Layer Perceptron Network (MLPN) was trained by back propagation algorithm. Network structures vary with the hidden units, learning rates, the number of iterations that seem necessary for the network to converge. Different network structures and their corresponding recognition rates are compared in this paper to find the optimal parameters of the Neural Network for this application. Using the optimal parameters, the network performs with an overall recognition rate 94%.

Uptake of phenol from aqueous solution by burned water hyacinth

Uptake of phenol from aqueous solution by burned water hyacinth The potential of burned water hyacinth (BWH) for phenol adsorption from aqueous solution was studied. Batch kinetic and isotherm studies were carried out under varying experimental conditions of contact time, phenol concentration, adsorbent dosage and pH. The pH at the point of zero charge (pHPZC) of the adsorbent was determined by the titration method and the value of 8.8 ± 0.2 was obtained. The FTIR of the adsorbent was carried out in order to find the potential adsorption sites for the interaction with phenol molecules. The Freundlich and Langmuir adsorption models were used for the mathematical description of adsorption equilibrium and it was found that the experimental data fitted very well to the Langmuir model. Maximum adsorption capacity of the adsorbent was found to be 30.49 mg/g. Batch adsorption models, based on the assumption of the pseudo-first-order and pseudo-second-order models, were applied to examine the kinetics of the adsorption. The results showed that kinetic data closely followed the pseudo-second-order model.

Optimal Parameter Selection Technique for a Neural Network Based Local Thresholding Method

Abstract Thresholding of a given image into binary image is a necessary step for most image analysis and recognition techniques. In document recognition application, success of OCR mostly depends on the quality of the thresholded image. Non-uniform illumination, low contrast and complex background make it challenging in this application. In this paper, selection of optimal parameters for Neural Network (NN) based local thresholding approach for grey scale composite document image with non-uniform background is proposed. NN-based local image thresholding technique uses 8 statistical and textural image features to obtain a feature vector for each pixel from a window of size (2n + 1)x(2n + 1), where n ≥ 1. An exhaustive search was conducted on these features and found pixel value, mean and entropy are the optimal features at window size 3x3. To validate these 3 features some non-uniform watermarked document images with known binary document images called base documents are used. Characters were extracted from these watermarked documents using the proposed 3 features. The difference between the thresholded document and base document is the noise. A quantitative measure Peak-Signal-to-Noise ratio (PSNR) is used to measure the noise. In case of unknown base document characters were extracted through the proposed 3 features and used in a commercial OCR to obtain the character recognition rate. The average recognition rate 99.25% and PSNR shows that the proposed 3 features are the optimal compare to the NN-based thresholding technique with different parameters presented in the literature.