Showing papers by "Sos S. Agaian published in 2010"

Boolean Derivatives With Application to Edge Detection for Imaging Systems

Abstract: This paper introduces a new concept of Boolean derivatives as a fusion of partial derivatives of Boolean functions (PDBFs). Three efficient algorithms for the calculation of PDBFs are presented. It is shown that Boolean function derivatives are useful for the application of identifying the location of edge pixels in binary images. The same concept is extended to the development of a new edge detection algorithm for grayscale images, which yields competitive results, compared with those of traditional methods. Furthermore, a new measure is introduced to automatically determine the parameter values used in the thresholding portion of the binarization procedure. Through computer simulations, demonstrations of Boolean derivatives and the effectiveness of the presented edge detection algorithm, compared with traditional edge detection algorithms, are shown using several synthetic and natural test images. In order to make quantitative comparisons, two quantitative measures are used: one based on the recovery of the original image from the output edge map and the Pratt's figure of merit.

Image Encryption using the Sudoku Matrix

Abstract: This paper introduces a new effective and lossless image encryption algorithm using a Sudoku Matrix to scramble and encrypt the image. The new algorithm encrypts an image through a three stage process. In the first stage, a reference Sudoku matrix is generated as the foundation for the encryption and scrambling processes. The image pixels' intensities are then changed by using the reference Sudoku matrix values, and then the pixels' positions are shuffled using the Sudoku matrix as a mapping process. The advantages of this method is useful for efficiently encrypting a variety of digital images, such as binary images, gray images, and RGB images without any quality loss. The security keys of the presented algorithm are the combination of the parameters in a 1D chaotic logistic map, a parameter to control the size of Sudoku Matrix and the number of iteration times desired for scrambling. The possible security key space is extremely large. The principles of the presented scheme could be applied to provide security for a variety of systems including image, audio and video systems.

Comparative Study of Histogram Equalization Algorithms for Image Enhancement

Abstract: Histogram equalization is one of the common tools for improving contrast in digital photography, remote sensing, medical imaging, and scientific visualization. It is a process for recovering lost contrast in an image by remapping the brightness values in such a way that equalizes or more evenly distributes its brightness values. However, Histogram Equalization may significantly change the brightness of the entire image and generate undesirable artifacts. Therefore, many Histogram Equalization based algorithms have been developed to overcome this problem. This paper presents a comprehensive review study of Histogram Equalization based algorithms. Computer simulations and analysis are provided to compare the enhancement performance of several Histogram Equalization based algorithms. A secondderivative- like enhancement measure is introduced to quantitatively evaluate their performance for image enhancement.

Human visual system based mammogram enhancement and analysis

Abstract: This paper introduces a new mammogram enhancement algorithm using the human visual system (HVS) based image decomposition. A new enhancement measure based on the second derivative is also introduced to measure and assess the enhancement performance. Experimental results show that the presented algorithm can improve the visual quality of fine details in mammograms. The HVS-based image decomposition can segment the regions/objects from their surroundings. It offers the users flexibility to enhance either sub-images containing only significant illumination information or all the sub-images of the original mammograms. The algorithm can be used in the computer-aided diagnosis systems for breast cancer detection.

Wavelet transform coefficient histogram-based image enhancement algorithms

Abstract: This paper proposes two image enhancement algorithms that are based on utilizing histogram data gathered from wavelet transform domain coefficients. Computer simulations demonstrate that combining the spatial method of histogram equalization with the logarithmic transform domain coefficient histograms achieves a much more balanced enhancement, which outperforms classical histogram equalization algorithms.

Binary data encryption using the Sudoku block cipher

Abstract: This paper presents a novel block cipher based on the Sudoku matrix The offered block cipher combines many advantages of chaos-based encryption and traditional transform-based encryption techniques Computer simulations show that a) the encrypted data have very random-like properties under many statistical metrics, b) unlike most chaos-based encryption methods generating unpredictable output, our new method is robust and effective for generating uniform-like encrypted data; c) it has high sensitivity to the KEY The offered scheme can be applied to many different data types, such as audio, image and video

Novel medical image enhancement algorithms

Abstract: In this paper, we present two novel medical image enhancement algorithms. The first, a global image enhancement algorithm, utilizes an alpha-trimmed mean filter as its backbone to sharpen images. The second algorithm uses a cascaded unsharp masking technique to separate the high frequency components of an image in order for them to be enhanced using a modified adaptive contrast enhancement algorithm. Experimental results from enhancing electron microscopy, radiological, CT scan and MRI scan images, using the MATLAB environment, are then compared to the original images as well as other enhancement methods, such as histogram equalization and two forms of adaptive contrast enhancement. An image processing scheme for electron microscopy images of Purkinje cells will also be implemented and utilized as a comparison tool to evaluate the performance of our algorithm.

Selective region encryption using a fast shape adaptive transform

Abstract: Selective regional encryption was performed on nonrectangular, statistically relevant regions of image media by permutation of coefficients in the domain of a fast, shape adaptive, parametric transform in order to partially encrypt the original image. Regions were successfully segmented using a high order information analysis. A simple encryption scheme which exploits the energy compaction properties of a shape adaptive cosine transform was then applied in the transform domain. Computer simulation shows that the method is fast and statistically secure.

A new edge detection algorithm in image processing based on LIP-ratio approach

Abstract: In this paper, we propose a new edge detection method that combines the concepts of Logarithmic Image Processing (LIP) and gray level ratio operations. The presented method provides a new approach in edge detection where edges are detected at different ratios of gray levels independently. The proposed method detects edge details based on the band of gray levels ratio of interest that these details lie within, while current edge detection algorithms control such edge details by threshold variations. In the proposed algorithm, variations of some introduced constant value introduce different edge details and not necessarily more or less details. Extensive simulations demonstrate that this method produces competitive results by suppressing impulsive noise, the ability to trace the development of certain object structures, and segmenting objects that belong to a certain area of interest.

Logical Transform based encryption for multimedia systems

Abstract: The need of encrypted data has increased in our day to day life. In this paper, we discuss an encryption technique based on Logical Transforms. The secret keys used in this technique to encrypt the images were based on the logical transforms that satisfies proposed boolean matrix constraints. In addition novel lossless encryption technique is introduced for the images of arbitrary size and format. This method can also be implemented on hardware.

3D CT baggage image enhancement based on order statistic decomposition

Abstract: Baggage scanning systems are widely used at security checkpoint in airports for homeland security applications. However, the CT baggage images suffer from background noise and the presence of low contrast. To address this problem, this paper introduces a new 3D CT baggage image enhancement algorithm using order statistic decomposition. Computer simulation and comparisons are given to demonstrate the excellent enhancement performance of the presented new algorithm and its capability of significantly improving visual quality of CT images while removing the background noise.

CT baggage image enhancement using a combination of alpha-weighted mean separation and histogram equalization

Abstract: Baggage scanning systems are used for detecting the presence of explosives and other prohibited items in baggage at security checkpoints in airports. However, the CT baggage images contain projection noise and are of low resolution. This paper introduces a new enhancement algorithm combining alpha-weighted mean separation and histogram equalization to enhance the CT baggage images while removing the background projection noise. A new enhancement measure is introduced for quantitative assessment of image enhancement. Simulations and a comparative analysis are given to demonstrate the new algorithm's performance.

Multi-scale image fusion using the Parameterized Logarithmic Image Processing model

Abstract: Image fusion is the process of combining multiple images into a single image which retains the most pertinent information from each original image source. More recently, multi-scale image fusion approaches have emerged as a means of providing a more meaningful fusion which better reflects the human visual system. In this paper, multi-scale decomposition techniques and image fusion algorithms are adapted using the Parameterized Logarithmic Image Processing (PLIP) model, a nonlinear image processing framework which more accurately processes images. Experimental results via computer simulations illustrate the improved performance of the proposed algorithms by both qualitative and quantitative means.

An algorithm for visualizing and detecting edges in RGB color images using logarithmic ratio approach

Abstract: In this paper, we present an algorithm for visualizing edges in RGB colored images as well as edge detection using logarithmic ratio approach. The developed visualization algorithm has a superior performance in highlighting more unforeseen color edges than the standard RGB-grayscale conversion method can present. Also, by integrating the proposed algorithm with a logarithmic-ratio based edge detector operator, the developed algorithm outperforms the standard edge detection operators in gradient colored images where color boundaries transitions are hard to detect.

Human visual system-based edge detection using image contrast enhancement and logarithmic ratio

Abstract: In this paper, we introduce a human visual system (HVS)-based edge detection algorithm. The introduced algorithm integrates image enhancement, edge detection and logarithmic ratio filtering algorithms to develop an effective edge detection method. Also a parameter (â) is introduced to control the level of detected edge details and functions as a primary threshold parameter. The introduced algorithm functions in tracking and segmenting significant dark gray levels in an image. Simulation results have shown the effectiveness of the introduced algorithm compared to other traditional methods such as Canny's algorithm in preserving object's topology and shape. The developed algorithm functions at various applications where measurements and segmentation of dark gray level spots for classification and tracking purposes are required such as road cracks, lunar surface images, and remote objects.

New normalized expansions for redundant number systems: adaptive data hiding techniques

Abstract: In this paper, we propose a new adaptive embedding technique which decomposes the image into various bitplanes based on redundant number systems. This technique is driven by three separate functions: 1) Adaptive selection of locations and number of bits per pixel to embed. 2) Adaptive selection of bit-plane decomposition for the cover image. 3) Adaptive selection of manner in which the information is inserted. Through the application of sensitive directional-based statistical estimation and a recorded account of actions taken, the proposed algorithms are able to provide the desired level of security, both visually and statistically. In comparison with other methods offering the same level of security, the new technique is able to offer a greater embedding capacity.

Image fusion using the Parameterized Logarithmic Dual Tree Complex Wavelet Transform

Abstract: Image fusion combines multiple images into a single image containing the relevant information from each of the original source images. This paper introduces a new Parameterized Logarithmic Dual Tree Complex Wavelet Transform (PL-DT-CWT) and its application for image fusion. The new transform combines the Dual Tree Complex Wavelet Transform (DT-CWT) with the Parameterized Logarithmic Image Processing (PLIP) model, a nonlinear image processing framework for processing images. Experimental results via computer simulations illustrate the improved performance of the proposed algorithms by both qualitative and quantitative means.

Nonlinear filtering for enhancing prostate MR images via alpha-trimmed Mean Separation

Abstract: This paper introduces a new enhancement algorithm for prostate MR images using a new nonlinear filtering operation and an alpha-trimmed Mean Separation. A new enhancement measure is also introduced to measure and assess the enhanced results. Experimental results show that the presented algorithm can significantly improve the contrast of prostate MR images. It has a potential application in prostate cancer detection.

Web based integrated framework for security applications

Abstract: This paper presents a multi tier web architecture that integrates web technology, Database system, and Batch processing tools for the development of a real time threat detection system. Four data repository models are introduced for effective data storage and retrieval. The baseline feature vectors are introduced and stored in the database table using a batch job. The batch job performs load balancing by calculating the new feature vector using the offline server and updates the online database server. The illustrative application uses the Hierarchical Multi level HVS segmentation, ratio based edge detection, and support vector machine for threat recognition and detection. The 64 bit edge based feature vector is generated for the baseline images and the input test object images using the cell edge distribution approach. The experimental results demonstrate that the presented framework is efficient in facilitating accurate threat detection and support the development of portable, reusable and scalable object recognition applications for heterogeneous distributed environment.

Automatic text detection and digital character recognition

Abstract: Text and optical character recognition (OCR) plays an increasingly important role in many modern applications in the field of medicine, finance, transport, and security. While many systems have been designed to identify text within an image, those methods were designed to deal with special types of images, such as those from scanned documents, web page, and newspapers. With variability in image quality, text format, and varying degrees of resolution, accurately extracting data embedded in an image is an open research problem which inspired researchers to focus on text retrieval techniques. In addition, the recognition of degraded text printed on a clear plastic surface has not been addressed. This research provides a solution to the problem of text extraction from images captured by a visual sensor. This research developed tools for extracting text with complex qualities such as dotted text printed on curved reflective material, text containing touching characters, poor background contrast, white, curved, rotated, and/or differing fonts or character width between sets of images. Computer simulation shows that the tools herein successfully handle recognition whether the text on the water bottles was raised, indented, or flat and/or if the text is shiny and the bottle material had a matte finish or vice versa. All of the tools created by this research were integrated into specialized systems with different characteristics such as recognition systems for Ozarka® and Dasani® water bottles, concrete slabs, and license plates. In addition, the system’s ability to read Arabic characters on the license plates illustrates the system’s universality. These systems demonstrated successful recognition with an accuracy rate of 90-93%.

A Novel Multilevel DCT Based Reversible Data Hiding.

Abstract: Discrete Cosine Transform (DCT) and wavelet transform are widely used in image processing such as compression, recognition and information hiding. DCT, a sub-optimal transform, is favorably close to the optimal Karhunen-Loeve Transform (KLT). The popular JPEG image format adopts the DCT as the core transform technique owing to its low computation cost, good decorrelation and energy compaction properties (Rao et al., 1990). In the past decades, another efficient transform, the wavelet transform has been developed to exploit the multi-resolution property in signals. It was then successfully employed in the new image format standard known as JPEG 2000. The applications of DCT and wavelet transform span from signal compression, recognition, feature extraction (Ma et al., 2004; Chen et al., 2006; Jing AbsTRACT

Purkinje cell 3D reconstruction and visualization system

Abstract: This paper presents a new system that reconstructs and visualizes 3D Purkinje cells (neurons) from two-photon microscopy images. The main components of the system are nonlinear diffusion filtering for denoising of each two-photon microscopy slice, increasing the image resolution of each Purkinje cell slice, global image enhancement of each slice as well as local pixel based image enhancement of each slice. Finally, 3D reconstruction and visualization of the Purkinje cell is accomplished using ImageJ. Computer simulations will illustrate the elucidated improvements over the original Purkinje cell images. Specifically, the presented system brought out hidden dendrite branches and allowed for a clearer picture of the entire Purkinje cell.

An Edge Detector based Integrated Database Framework for Security Applications

Abstract: In this paper, an integrated framework comprising of computer vision algorithms, Database system and Batch processing techniques has been developed to facilitate effective automatic threat recognition and detection for security applications. The proposed approach is used for automatic threat detection. The novel features of this structure include utilizing the Human Visual System model for segmentation, and a new ratio based edge detection algorithm that includes a new adaptive hysteresis thresholding method. The feature vectors of the baseline images are generated and stored in a relational database system using a batch window. The batch window is a special process where image processing tasks with similar needs are grouped together and effectively processed to save computing and memory requirements. The feature vectors of the segmented objects are generated using the CED method and are classified using a support vector machine (SVM) based classifier to identify threat objects. The experimental results demonstrate the presented framework efficiency in reducing the classification time and provide accurate detection.

Image enhancement based on transform coefficient histogram shifting and shaping

Abstract: This paper proposes two image enhancement algorithms that are based on utilizing histogram data gathered from wavelet transform domain coefficients. Computer simulations demonstrate that combining the spatial method of histogram equalization with the wavelet transform domain coefficient histograms achieves a much more balanced enhancement, which outperforms classical histogram equalization algorithms.