Search results for: Medical Image Watermarking (MIW)

Authors: Yahia Salah, Med Lassaad Kaddachi, Rached Tourki

Abstract:

This paper presents the design, implementation and evaluation of a micro-network, or Network-on-Chip (NoC), based on a generic pipeline router architecture. The router is designed to efficiently support traffic generated by multimedia applications on embedded multi-core systems. It employs a simplest routing mechanism and implements the round-robin scheduling strategy to resolve output port contentions and minimize latency. A virtual channel flow control is applied to avoid the head-of-line blocking problem and enhance performance in the NoC. The hardware design of the router architecture has been implemented at the register transfer level; its functionality is evaluated in the case of the two dimensional Mesh/Torus topology, and performance results are derived from ModelSim simulator and Xilinx ISE 9.2i synthesis tool. An example of a multi-core image processing system utilizing the NoC structure has been implemented and validated to demonstrate the capability of the proposed micro-network architecture. To reduce complexity of the image compression and decompression architecture, the system use image processing algorithm based on classical discrete cosine transform with an efficient zonal processing approach. The experimental results have confirmed that both the proposed image compression scheme and NoC architecture can achieve a reasonable image quality with lower processing time.

Keywords: Generic Pipeline Network-on-Chip Router Architecture, JPEG Image Compression, FPGA Hardware Implementation, Performance Evaluation.

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Authors: Alessandro Bevilacqua, Alessandro Gherardi, Filippo Piccinini

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The mosaicing technique has been employed in more and more application fields, from entertainment to scientific ones. In the latter case, often the final evaluation is still left to human beings, that assess visually the quality of the mosaic. Many times, a lack of objective measurements in microscopic mosaicing may prevent the mosaic from being used as a starting image for further analysis. In this work we analyze three different metrics and indexes, in the domain of signal analysis, image analysis and visual quality, to measure the quality of different aspects of the mosaicing procedure, such as registration errors and visual quality. As the case study we consider the mosaicing algorithm we developed. The experiments have been carried out by considering mosaics with very different features: histological samples, that are made of detailed and contrasted images, and live stem cells, that show a very low contrast and low detail levels.

Keywords: Mosaicing, quality assessment, microscopy, stem cells.

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Authors: Yesubai Rubavathi Charles, Ravi Ramraj

Abstract:

In order to retrieve images efficiently from a large database, a unique method integrating color and texture features using genetic programming has been proposed. Opponent color histogram which gives shadow, shade, and light intensity invariant property is employed in the proposed framework for extracting color features. For texture feature extraction, fast discrete curvelet transform which captures more orientation information at different scales is incorporated to represent curved like edges. The recent scenario in the issues of image retrieval is to reduce the semantic gap between user’s preference and low level features. To address this concern, genetic algorithm combined with relevance feedback is embedded to reduce semantic gap and retrieve user’s preference images. Extensive and comparative experiments have been conducted to evaluate proposed framework for content based image retrieval on two databases, i.e., COIL-100 and Corel-1000. Experimental results clearly show that the proposed system surpassed other existing systems in terms of precision and recall. The proposed work achieves highest performance with average precision of 88.2% on COIL-100 and 76.3% on Corel, the average recall of 69.9% on COIL and 76.3% on Corel. Thus, the experimental results confirm that the proposed content based image retrieval system architecture attains better solution for image retrieval.

Keywords: Content based image retrieval, Curvelet transform, Genetic algorithm, Opponent color histogram, Relevance feedback.

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Authors: Bhatti A., Nahavandi S., Hossny M.

Abstract:

Current advancements in nanotechnology are dependent on the capabilities that can enable nano-scientists to extend their eyes and hands into the nano-world. For this purpose, a haptics (devices capable of recreating tactile or force sensations) based system for AFM (Atomic Force Microscope) is proposed. The system enables the nano-scientists to touch and feel the sample surfaces, viewed through AFM, in order to provide them with better understanding of the physical properties of the surface, such as roughness, stiffness and shape of molecular architecture. At this stage, the proposed work uses of ine images produced using AFM and perform image analysis to create virtual surfaces suitable for haptics force analysis. The research work is in the process of extension from of ine to online process where interaction will be done directly on the material surface for realistic analysis.

Keywords: Haptics, AFM, force feedback, image analysis.

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Authors: Khalid Elasnaoui, Brahim Aksasse, Mohammed Ouanan

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In this paper, we are interested in the problem of finding similar images in a large database. For this purpose we propose a new algorithm based on a combination of the 2-D histogram intersection in the HSV space and statistical moments. The proposed histogram is based on a 3x3 window and not only on the intensity of the pixel. This approach overcome the drawback of the conventional 1-D histogram which is ignoring the spatial distribution of pixels in the image, while the statistical moments are used to escape the effects of the discretisation of the color space which is intrinsic to the use of histograms. We compare the performance of our new algorithm to various methods of the state of the art and we show that it has several advantages. It is fast, consumes little memory and requires no learning. To validate our results, we apply this algorithm to search for similar images in different image databases.

Keywords: 2-D histogram, Statistical moments, Indexing, Similarity distance, Histograms intersection.

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Authors: Khalid Salhi, El Miloud Jaara, Mohammed Talibi Alaoui

Abstract:

In this paper, we present a neural approach for unsupervised natural color-texture image segmentation, which is based on both Kohonen maps and mathematical morphology, using a combination of the texture and the image color information of the image, namely, the fractal features based on fractal dimension are selected to present the information texture, and the color features presented in RGB color space. These features are then used to train the network Kohonen, which will be represented by the underlying probability density function, the segmentation of this map is made by morphological watershed transformation. The performance of our color-texture segmentation approach is compared first, to color-based methods or texture-based methods only, and then to k-means method.

Keywords: Segmentation, color-texture, neural networks, fractal, watershed.

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Authors: Hanan Mahmoud Ezzat Mahmoud, Alaa Abd El Fatah Hefnawy

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In this paper, we present a system for content-based retrieval of large database of classified satellite images, based on user's relevance feedback (RF).Through our proposed system, we divide each satellite image scene into small subimages, which stored in the database. The modified radial basis functions neural network has important role in clustering the subimages of database according to the Euclidean distance between the query feature vector and the other subimages feature vectors. The advantage of using RF technique in such queries is demonstrated by analyzing the database retrieval results.

Keywords: content-based image retrieval, large database of image, RBF neural net, relevance feedback

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Authors: Todsaporn Fuangrod, Daryl J. O'Connor, Boyd MC McCurdy, Peter B. Greer

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An electronic portal image device (EPID) has become a method of patient-specific IMRT dose verification for radiotherapy. Research studies have focused on pre and post-treatment verification, however, there are currently no interventional procedures using EPID dosimetry that measure the dose in real time as a mechanism to ensure that overdoses do not occur and underdoses are detected as soon as is practically possible. As a result, an EPID-based real time dose verification system for dynamic IMRT was developed and was implemented with MATLAB/Simulink. The EPID image acquisition was set to continuous acquisition mode at 1.4 images per second. The system defined the time constraint gap, or execution gap at the image acquisition time, so that every calculation must be completed before the next image capture is completed. In addition, the <=-evaluation method was used for dose comparison, with two types of comparison processes; individual image and cumulative dose comparison monitored. The outputs of the system are the <=-map, the percent of <=<1, and mean-<= versus time, all in real time. Two strategies were used to test the system, including an error detection test and a clinical data test. The system can monitor the actual dose delivery compared with the treatment plan data or previous treatment dose delivery that means a radiation therapist is able to switch off the machine when the error is detected.

Keywords: real-time dose verification, EPID dosimetry, simulation, dynamic IMRT

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Authors: R. Krishnamoorthi, N. Kannan

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In this paper, a new algorithm for generating codebook is proposed for vector quantization (VQ) in image coding. The significant features of the training image vectors are extracted by using the proposed Orthogonal Polynomials based transformation. We propose to generate the codebook by partitioning these feature vectors into a binary tree. Each feature vector at a non-terminal node of the binary tree is directed to one of the two descendants by comparing a single feature associated with that node to a threshold. The binary tree codebook is used for encoding and decoding the feature vectors. In the decoding process the feature vectors are subjected to inverse transformation with the help of basis functions of the proposed Orthogonal Polynomials based transformation to get back the approximated input image training vectors. The results of the proposed coding are compared with the VQ using Discrete Cosine Transform (DCT) and Pairwise Nearest Neighbor (PNN) algorithm. The new algorithm results in a considerable reduction in computation time and provides better reconstructed picture quality.

Keywords: Orthogonal Polynomials, Image Coding, Vector Quantization, TSVQ, Binary Tree Classifier

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Authors: Pei-Ti Chen, Ren-Hua Kung, Ming –Yi Huang , Fuu-Diing Chen, Lei Pei

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Medical Tourism is a new development in Taiwan recently. The willingness and barriers of potential tourists from China to participate medical tourism are studied. A questionnaire survey is conducted and the SPSS software is used to analyze data. The results show that under one fifth of respondents express full medical tourism participation willingness. Among travel barriers toward medical tourism, “insufficient information of medical tourism trip", “not enough time", “no companion", “worrying about unsatisfied itinerary." are perceived the most important barriers.

Keywords: medical tourism, travel barriers, participation willingness

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Authors: Peng He

Abstract:

Imprecision is a long-standing problem in CAD design and high accuracy image-based reconstruction applications. The visual hull which is the closed silhouette equivalent shape of the objects of interest is an important concept in image-based reconstruction. We extend the domain-theoretic framework, which is a robust and imprecision capturing geometric model, to analyze the imprecision in the output shape when the input vertices are given with imprecision. Under this framework, we show an efficient algorithm to generate the 2D partial visual hull which represents the exact information of the visual hull with only basic imprecision assumptions. We also show how the visual hull from polyhedra problem can be efficiently solved in the context of imprecise input.

Keywords: Geometric Domain, Computer Vision, Computational Geometry, Visual Hull, Image-Based reconstruction, Imprecise Input, CAD object

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Authors: Andrew Reilly, Nancy A. Rudd

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The appearance management behavior of tanning by gay men is examined through the lens of Impression Formation. The study proposes that body image, self-esteem, and internalized homophobia are connected and affect the motives for engaging in sun, salon, and cosmetic tanning. Motives examined were: to look masculine, to look attractive to (potential) partners, to look attractive in general, to socialize, to meet a peer standard, and for personal satisfaction. Using regression analysis to examine data of 103 gay men who engage in at least one method of tanning, results reveal that components of body image and internalized homophobia–but not self-esteem–are linked to various motives and methods of tanning. These findings support and extend the literature of Impression Formation Theory and provide practitioners in the health and healthrelated fields new avenues to pursue when dealing with diseases related to tanning.

Keywords: Body image, gay men, tanning.

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Authors: Seung You Na, Joo Hyun Jung, Jin Young Kim, Mohammad AhangarKiasari

Abstract:

There are various kinds of medical equipment which requires relatively accurate positional adjustments for successful treatment. However, patients tend to move without notice during a certain span of operations. Therefore, it is common practice that accompanying operators adjust the focus of the equipment. In this paper, tracking controllers for medical equipment are suggested to replace the operators. The tracking controllers use AHRS sensor information to recognize the movements of patients. Sensor fusion is applied to reducing the error magnitudes through linear Kalman filters. The image processing of optical markers is included to adjust the accumulation errors of gyroscope sensor data especially for yaw angles. The tracking controller reduces the positional errors between the current focus of a device and the target position on the body of a patient. Since the sensing frequencies of AHRS sensors are very high compared to the physical movements, the control performance is satisfactory. The typical applications are, for example, ESWT or rTMS, which have the error ranges of a few centimeters.

Keywords: AHRS, Sensor fusion, Tracking control, Position and posture.

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra, Abdus Sobur

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of artificial intelligence (AI), specifically deep learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images, representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our approach presents a hybrid model, amalgamating the strengths of two renowned convolutional neural networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: Artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging.

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Authors: Waqqas-ur-Rehman Butt, Martin Servin, Marion Pause

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In recent years, object detection has gained much attention and very encouraging research area in the field of computer vision. The robust object boundaries detection in an image is demanded in numerous applications of human computer interaction and automated surveillance systems. Many methods and approaches have been developed for automatic object detection in various fields, such as automotive, quality control management and environmental services. Inappropriately, to the best of our knowledge, object detection under illumination with shadow consideration has not been well solved yet. Furthermore, this problem is also one of the major hurdles to keeping an object detection method from the practical applications. This paper presents an approach to automatic object detection in images under non-standardized environmental conditions. A key challenge is how to detect the object, particularly under uneven illumination conditions. Image capturing conditions the algorithms need to consider a variety of possible environmental factors as the colour information, lightening and shadows varies from image to image. Existing methods mostly failed to produce the appropriate result due to variation in colour information, lightening effects, threshold specifications, histogram dependencies and colour ranges. To overcome these limitations we propose an object detection algorithm, with pre-processing methods, to reduce the interference caused by shadow and illumination effects without fixed parameters. We use the Y CrCb colour model without any specific colour ranges and predefined threshold values. The segmented object regions are further classified using morphological operations (Erosion and Dilation) and contours. Proposed approach applied on a large image data set acquired under various environmental conditions for wood stack detection. Experiments show the promising result of the proposed approach in comparison with existing methods.

Keywords: Image processing, Illumination equalization, Shadow filtering, Object detection, Colour models, Image segmentation.

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Authors: Mina Dabirinezhad, Mohsen Bayat Pour, Amin Dabirinejad

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This paper is intended to introduce the technology to solve some of the deficiencies of the direct digital radiology. Nowadays, digital radiology is the latest progression in dental imaging, which has become an essential part of dentistry. There are two main parts of the direct digital radiology comprised of an intraoral X-ray machine and a sensor (digital image receptor). The dentists and the dental nurses experience afflictions during the taking image process by the direct digital X-ray machine. For instance, sometimes they need to readjust the sensor in the mouth of the patient to take the X-ray image again due to the low quality of that. Another problem is, the position of the sensor may move in the mouth of the patient and it triggers off an inappropriate image for the dentists. It means that it is a time-consuming process for dentists or dental nurses. On the other hand, taking several the X-ray images brings some problems for the patient such as being harmful to their health and feeling pain in their mouth due to the pressure of the sensor to the jaw. The author provides a technology to solve the above-mentioned issues that is called “Self-Recognition Direct Digital Radiology” (SDDR). This technology is based on the principle that the intraoral X-ray machine is capable to diagnose the location of the sensor in the mouth of the patient automatically. In addition, to solve the aforementioned problems, SDDR technology brings out fewer environmental impacts in comparison to the previous version.

Keywords: Dental direct digital imaging, digital image receptor, digital x-ray machine, and environmental impacts.

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Authors: Erick López-Ornelas

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As the world changes more rapidly, the demand for update information for resource management, environment monitoring, planning are increasing exponentially. Integration of Remote Sensing with GIS technology will significantly promote the ability for addressing these concerns. This paper presents an alternative way of update GIS applications using image processing and high resolution images. We show a method of high-resolution image segmentation using graphs and morphological operations, where a preprocessing step (watershed operation) is required. A morphological process is then applied using the opening and closing operations. After this segmentation we can extract significant cartographic elements such as urban areas, streets or green areas. The result of this segmentation and this extraction is then used to update GIS applications. Some examples are shown using aerial photography.

Keywords: GIS, Remote Sensing, image segmentation, feature extraction.

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Authors: Y. Ni, K. Sebri

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The practical implementation of audio-video coupled speech recognition systems is mainly limited by the hardware complexity to integrate two radically different information capturing devices with good temporal synchronisation. In this paper, we propose a solution based on a smart CMOS image sensor in order to simplify the hardware integration difficulties. By using on-chip image processing, this smart sensor can calculate in real time the X/Y projections of the captured image. This on-chip projection reduces considerably the volume of the output data. This data-volume reduction permits a transmission of the condensed visual information via the same audio channel by using a stereophonic input available on most of the standard computation devices such as PC, PDA and mobile phones. A prototype called VMIKE (Visio-Microphone) has been designed and realised by using standard 0.35um CMOS technology. A preliminary experiment gives encouraged results. Its efficiency will be further investigated in a large variety of applications such as biometrics, speech recognition in noisy environments, and vocal control for military or disabled persons, etc.

Keywords: Audio-Visual Speech recognition, CMOS Smartsensor, On-Chip image processing.

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Authors: Yongseok Kwon, Woowon Jeong, Eunjin Cho, Sangkug Chung, Kyehan Rhee

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Steady streaming flow fields induced by a 500 mm bubble oscillating at 12 kHz were measured using microscopic particle image velocimetry (PIV). The accuracy of velocity measurement using a micro PIV system was checked by comparing the measured velocity fields with the theoretical velocity profiles in fully developed laminar flow. The steady streaming flow velocities were measured in the sagittal plane of the bubble attached on the wall. Measured velocity fields showed upward jet flow with two symmetric counter-rotating vortices, and the maximum streaming velocity was about 12 mm/s, which was within the velocity ranges measured by other researchers. The measured streamlines were compared with the analytical solution, and they also showed a reasonable agreement.

Keywords: Oscillating bubble, Particle-Image-Velocimetry microstreaming.

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Authors: Hsing-Yi Kao, Der-Ming Liou

Abstract:

Transferring patient information between medical care sites is necessary to deliver better patient care and to reduce medical cost. So developing of electronic medical records is an important trend for the world.The Continuity of Care Document (CCD) is product of collaboration between CDA and CCR standards. In this study, we will develop a system to generate medical records with entry level based on CCD template module.

Keywords: Continuity Care Document, medical record, entrylevel

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Authors: Terrence Chen, Thomas S. Huang

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In this paper, we present the region based hidden Markov random field model (RBHMRF), which encodes the characteristics of different brain regions into a probabilistic framework for brain MR image segmentation. The recently proposed TV+L1 model is used for region extraction. By utilizing different spatial characteristics in different brain regions, the RMHMRF model performs beyond the current state-of-the-art method, the hidden Markov random field model (HMRF), which uses identical spatial information throughout the whole brain. Experiments on both real and synthetic 3D MR images show that the segmentation result of the proposed method has higher accuracy compared to existing algorithms.

Keywords: Finite Gaussian mixture model, Hidden Markov random field model, image segmentation, MRI.

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Authors: Lina, Arlends Chris, Bagus Mulyawan, Agus B. Dharmawan

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Blood cell analysis plays a significant role in the diagnosis of human health. As an alternative to the traditional technique conducted by laboratory technicians, this paper presents an automatic white blood cell (leukocyte) detection system using Image Stitching and Color Overlapping Windows. The advantage of this method is to present a detection technique of white blood cells that are robust to imperfect shapes of blood cells with various image qualities. The input for this application is images from a microscope-slide translation video. The preprocessing stage is performed by stitching the input images. First, the overlapping parts of the images are determined, then stitching and blending processes of two input images are performed. Next, the Color Overlapping Windows is performed for white blood cell detection which consists of color filtering, window candidate checking, window marking, finds window overlaps, and window cropping processes. Experimental results show that this method could achieve an average of 82.12% detection accuracy of the leukocyte images.

Keywords: Color overlapping windows, image stitching, leukocyte detection.

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Authors: Saman Ghaffarian, Ilgın Gökasar

Abstract:

This paper presents a new automatic vehicle detection method from very high resolution aerial images to measure traffic density. The proposed method starts by extracting road regions from image using road vector data. Then, the road image is divided into equal sections considering resolution of the images. Gradient vectors of the road image are computed from edge map of the corresponding image. Gradient vectors on the each boundary of the sections are divided where the gradient vectors significantly change their directions. Finally, number of vehicles in each section is carried out by calculating the standard deviation of the gradient vectors in each group and accepting the group as vehicle that has standard deviation above predefined threshold value. The proposed method was tested in four very high resolution aerial images acquired from Istanbul, Turkey which illustrate roads and vehicles with diverse characteristics. The results show the reliability of the proposed method in detecting vehicles by producing 86% overall F1 accuracy value.

Keywords: Aerial images, intelligent transportation systems, traffic density measurement, vehicle detection.

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Authors: A. Sopharak, B. Uyyanonvara, S. Barman

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Microaneurysm is a key indicator of diabetic retinopathy that can potentially cause damage to retina. Early detection and automatic quantification are the keys to prevent further damage. In this paper, which focuses on automatic microaneurysm detection in images acquired through non-dilated pupils, we present a series of experiments on feature selection and automatic microaneurysm pixel classification. We found that the best feature set is a combination of 10 features: the pixel-s intensity of shade corrected image, the pixel hue, the standard deviation of shade corrected image, DoG4, the area of the candidate MA, the perimeter of the candidate MA, the eccentricity of the candidate MA, the circularity of the candidate MA, the mean intensity of the candidate MA on shade corrected image and the ratio of the major axis length and minor length of the candidate MA. The overall sensitivity, specificity, precision, and accuracy are 84.82%, 99.99%, 89.01%, and 99.99%, respectively.

Keywords: Diabetic retinopathy, microaneurysm, naive Bayes classifier

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Authors: Renato Dedic, Madjid Allili, Roger Lecomte, Adbelhamid Benchakroun

Abstract:

The class of geometric deformable models, so-called level sets, has brought tremendous impact to medical imagery. In this paper we present yet another application of level sets to medical imaging. The method we give here will in a way modify the speed term in the standard level sets equation of motion. To do so we build a potential based on the distance and the gradient of the image we study. In turn the potential gives rise to the force field: F~F(x, y) = P ∀(p,q)∈I ((x, y) - (p, q)) |ÔêçI(p,q)| |(x,y)-(p,q)| 2 . The direction and intensity of the force field at each point will determine the direction of the contour-s evolution. The images we used to test our method were produced by the Univesit'e de Sherbrooke-s PET scanners.

Keywords: PET, Cardiac, Heart, Mouse, Geodesic, Geometric, Level Sets, Deformable Models, Edge Detection, Segmentation.

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Authors: Mbainaibeye Jérôme, Noureddine Ellouze

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Wavelet transforms is a very powerful tools for image compression. One of its advantage is the provision of both spatial and frequency localization of image energy. However, wavelet transform coefficients are defined by both a magnitude and sign. While algorithms exist for efficiently coding the magnitude of the transform coefficients, they are not efficient for the coding of their sign. It is generally assumed that there is no compression gain to be obtained from the coding of the sign. Only recently have some authors begun to investigate the sign of wavelet coefficients in image coding. Some authors have assumed that the sign information bit of wavelet coefficients may be encoded with the estimated probability of 0.5; the same assumption concerns the refinement information bit. In this paper, we propose a new method for Separate Sign Coding (SSC) of wavelet image coefficients. The sign and the magnitude of wavelet image coefficients are examined to obtain their online probabilities. We use the scalar quantization in which the information of the wavelet coefficient to belong to the lower or to the upper sub-interval in the uncertainly interval is also examined. We show that the sign information and the refinement information may be encoded by the probability of approximately 0.5 only after about five bit planes. Two maps are separately entropy encoded: the sign map and the magnitude map. The refinement information of the wavelet coefficient to belong to the lower or to the upper sub-interval in the uncertainly interval is also entropy encoded. An algorithm is developed and simulations are performed on three standard images in grey scale: Lena, Barbara and Cameraman. Five scales are performed using the biorthogonal wavelet transform 9/7 filter bank. The obtained results are compared to JPEG2000 standard in terms of peak signal to noise ration (PSNR) for the three images and in terms of subjective quality (visual quality). It is shown that the proposed method outperforms the JPEG2000. The proposed method is also compared to other codec in the literature. It is shown that the proposed method is very successful and shows its performance in term of PSNR.

Keywords: Image compression, wavelet transform, sign coding, magnitude coding.

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Authors: Michael Zimba, Darlison Nyirenda

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A novel copy-move image forgery, CMIF, detection method is proposed. The proposed method presents a new approach which relies on electrostatic field theory, EFT. Solely for the purpose of reducing the dimension of a suspicious image, the proposed algorithm firstly performs discrete wavelet transform, DWT, of the suspicious image and extracts only the approximation subband. The extracted subband is then bijectively mapped onto a virtual electrostatic field where concepts of EFT are utilized to extract robust features. The extracted features are invariant to additive noise, JPEG compression, and affine transformation. Finally, same affine transformation selection, SATS, a duplication verification method, is applied to detect duplicated regions. SATS is a better option than the common shift vector method because SATS is insensitive to affine transformation. Consequently, the proposed CMIF algorithm is not only fast but also more robust to attacks compared to the existing related CMIF algorithms. The experimental results show high detection rates, as high as 100% in some cases.

Keywords: Affine transformation, Radix sort, SATS, Virtual electrostatic field.

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Authors: Sundararajan Sangeetha, Joseph Jesu Christopher, Swaminathan Ramakrishnan

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In this work, the primary compressive strength components of human femur trabecular bone are qualitatively assessed using image processing and wavelet analysis. The Primary Compressive (PC) component in planar radiographic femur trabecular images (N=50) is delineated by semi-automatic image processing procedure. Auto threshold binarization algorithm is employed to recognize the presence of mineralization in the digitized images. The qualitative parameters such as apparent mineralization and total area associated with the PC region are derived for normal and abnormal images.The two-dimensional discrete wavelet transforms are utilized to obtain appropriate features that quantify texture changes in medical images .The normal and abnormal samples of the human femur are comprehensively analyzed using Harr wavelet.The six statistical parameters such as mean, median, mode, standard deviation, mean absolute deviation and median absolute deviation are derived at level 4 decomposition for both approximation and horizontal wavelet coefficients. The correlation coefficient of various wavelet derived parameters with normal and abnormal for both approximated and horizontal coefficients are estimated. It is seen that in almost all cases the abnormal show higher degree of correlation than normals. Further the parameters derived from approximation coefficient show more correlation than those derived from the horizontal coefficients. The parameters mean and median computed at the output of level 4 Harr wavelet channel was found to be a useful predictor to delineate the normal and the abnormal groups.

Keywords: Image processing, planar radiographs, trabecular bone and wavelet analysis.

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Authors: D.R Prince Williams

Abstract:

In this paper, we introduce the notion θ-Euclidean k-fuzzy ideal in semirings and to study the properties of the image and pre image of a θ -Euclidean k-fuzzy ideal in a semirings under epimorphism.

Keywords: semiring, fuzzy ideal, k–fuzzy ideal, θ -Euclidean Lfuzzyideal, θ -Euclidean fuzzy k–ideal, θ -Euclidean k-fuzzy ideal.

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Authors: Nur Adilah Md Zain, Mohd Salehuddin Mohd Zahari, Mohd Hafiz Hanafiah, Muhammad Izzat Zulkifly

Abstract:

This paper empirically investigates the relationship between Sabah state core tourism products and its destination image. Through a descriptive design using a quantitative method with a self-reported and self-administered questionnaire, this research surveyed the individual international tourists who had visited Sabah and experienced the state’s core tourism products. The research findings clearly indicate that Sabah, one of the states in Malaysia has a lot of valuable resources in the eyes of the international tourists. Interestingly, it was found that Sabah’s core tourism products namely unique marine resources, various nature attractions and cultural diversities have undoubtedly contributed to the state’s tourism image. Good feedbacks and the promising insights from the international tourists’ point of view offer varying consequences, repercussion, and implication to the state government and the relevant authorities. Collaboration and cooperation between all responsible authorities are therefore crucial in strengthening the “total tourism experience” among the international tourists in this state.

Keywords: Tourism core products, marine, cultural, nature, destination image, Sabah.

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