Hussain Al-Ahmad

A novel blind image watermarking technique for colour RGB images in the DCT domain using green channel

This paper presents a new algorithm for colour digital image watermarking. The 24 bits/pixel RGB images are used and the watermark is placed on the green channel of the RGB image. The green channel is chosen after an analytical investigation process was carried out using some popular measurement metrics. The analysis and embedding processes have been carried out using the discrete cosine transform DCT. The new watermarking method has shown to be resistant to JPEG compression, cropping, scaling, low-pass, median and removal attack. This algorithm produces more than 65 dB of average PSNR.

Extraction and Segmentation of Sputum Cells for Lung Cancer Early Diagnosis

Lung cancer has been the largest cause of cancer deaths worldwide with an overall 5-year survival rate of only 15%. Its symptoms can be found exclusively in advanced stages where the chances for patients to survive are very low, thus making the mortality rate the highest among all other types of cancer. The present work deals with the attempt to design computer-aided detection or diagnosis (CAD) systems for early detection of lung cancer based on the analysis of sputum color images. The aim is to reduce the false negative rate and to increase the true positive rate as much as possible. The early detection of lung cancer from sputum images is a challenging problem, due to both the structure of the cancer cells and the stained method which are employed in the formulation of the sputum cells. We present here a framework for the extraction and segmentation of sputum cells in sputum images using, respectively, a threshold classifier, a Bayesian classification and mean shift segmentation. Our methods are validated and compared with other competitive techniques via a series of experimentation conducted with a data set of 100 images. The extraction and segmentation results will be used as a base for a CAD system for early detection of lung cancer which will improve the chances of survival for the patient.

Bayesian classification and artificial neural network methods for lung cancer early diagnosis

Lung cancer is a serious illness which can be cured if it is diagnosed at early stages. One technique which is commonly used for early detection of this type of cancer consists of analyzing sputum images. However, the analysis of sputum images is time consuming and requires highly trained personnel to avoid diagnostic errors. Image processing techniques provide a reliable tool for improving the manual screening of sputum samples. In this paper, we address the problem of extraction and segmentation the sputum cells based on the analysis of sputum color image with the aim to attain a high specificity rate and reduce the time consumed to analyze such sputum samples. In order to form general diagnostic rules, we use a Bayesian classifier to extract the sputum cells followed by using a Hopfield Neural Network (HNN) to segment the extracted cells into nuclei and cytoplasm regions from the background region. The final results will be used for a Computer Aided Diagnosis (CAD) system for early detection of lung cancer. We used some performance criteria such as sensitivity, specificity and accuracy to evaluate the proposed methods. Our methods are validated via a series of experimentation conducted with a data set of 88 images.

2-D Walsh coding for robust digital image watermarking

One-dimensional (1D) Walsh coding have previously been proposed to improve the robustness of digital image watermarking techniques. In this paper, it is proposed to use two-dimensional (2D) Walsh coding to achieve further improvements in robustness. In the proposed method, Walsh functions are used to perform 2D coding of the watermark before embedding it in the original image. Simulation results, using both spatial-domain and frequency-domain techniques, indicate that the use of 2D Walsh coding results in improved robustness against JPEG compression attacks compared to 1D Walsh coding. Further improvements can also be achieved by increasing the length of the Walsh sequence. This is, however, at the expense of more degradation to the watermarked image.

Walsh-coded signatures for robust digital image watermarking

This paper proposes the use of 1-D Walsh coding to improve the robustness of digital image watermarking techniques. In the proposed method, Walsh functions are used to encode the watermark before embedding it in the original image. Results indicate that this type of coding results in less visible degradation to the watermarked image than direct scaling. Simulation results using both spatial-domain and frequency-domain techniques indicate that the use of Walsh coding results in improved robustness against JPEG compression attacks. It is also shown that frequency domain watermarking is more resistant to JPEG compression attacks and also less sensitive to the contents of the input original image. Increasing the length of the Walsh function results in more robustness but at the expense of more degradation to the quality of the watermarked image.

A new robust and fragile watermarking scheme for images captured by mobile phone cameras

This paper deals with a watermarking scheme for images captured by mobile phone cameras. The new scheme embeds two watermarks in the images for authentication checking and copyright protection. The mobile phone number including the international code is used as the first robust watermark. The number is embedded using the discrete wavelet transform. Hash code of the image is used as a fragile watermark and inserted in the spatial domain. The scheme is blind and does not require the original image in order to extract the watermarks. The fragile watermark is very sensitive to any kind of attacks or image manipulations. On the other hand, the robust watermark causes minimal distortion to the host images. The robustness of the proposed algorithm has been tested against several attacks and it survived cropping, scaling, filtering, additive noise and JPEG compression.

Cell extraction from sputum images for early lung cancer detection

The context of this work is the design of Computer-Aided Design system for the early detection of lung cancer using digital sputum images. This process requires the detection of the sputum cell region in the sputum image characterized by a cluttered background. In this paper we address this problem using two different methods, namely, a Rule-based method, and Bayesian classification. We describe the two methods and we compare their performances in terms of their behaviors with respect to color representation and color quantization.

The Design of a Resistively Loaded Bowtie Antenna for Applications in Breast Cancer Detection Systems

A resistively loaded bowtie antenna, intended for applications in breast cancer detection, is adaptively modified through modelling and genetic optimization. The required wideband operating characteristic is achieved through manipulation of the resistive loading of the antenna structure, the number of wires, and their angular separation within the equivalent wire assembly. The results show an acceptable impedance bandwidth of 100.75%, with a challenge VSWR <; 2, over the interval from 3.3 GHz to 10.0 GHz. Feasibility studies were made on the antenna sensitivity for operation in a tissue-equivalent dielectric medium. The simulated and measured results are all in close agreement.

A New Watermarking Algorithm for Color Satellite Images Using Color Logos and Hash Functions

This paper deals with a new robust and fragile watermarking algorithm which embeds 3bit index map of color logo watermark in the frequency domain and fragile information in the spatial domain of 24bit satellite color images. The color logo is used for copyright protection and the hash functions are used for checking the authenticity of the satellite image. The new algorithm caused minimal invisible distortion to the images demonstrated by high peak signal to noise ratio and similarity structure index measure values. The new watermarking technique has been successfully tested on a variety of satellite images. The robust watermarking scheme survived many attacks such as JPEG compression, resizing and rotation while the fragile watermark is very sensitive to the slightest modification to any RGB pixel, also it can accurately locate the tampered areas on the satellite color images.

Extraction of sputum cells using thresholding techniques for lung cancer detection

This paper deals with an improved version of a filtering thresholding algorithm for extracting the sputum cell from the raw sputum image for lung cancer early detection. In this method the problem is viewed as a segmentation problem focusing on the extraction of such sputum cells from the images. This will be done by segmenting the image into sputum cell region which includes the nuclei, cytoplasm and the background that includes all the rest. These cells can then be analyzed to see if they are cancerous or not. A database with hundred of sputum color images from normal and abnormal subjects that prepared by the Papanicalaou standard staining method were used. These images were used in testing the new algorithm and the results were compared with the ground truth image of extracted sputum cells. It was noted that the new algorithm offered better extraction results than the previous work. It succeeded in extracting the nuclei and cytoplasm regions. Moreover, it succeeded in determining the best range of thresholding values.