Search results for: Brain MR image

Authors: Hossein Nezamabadi-pour, Saeid Saryazdi

Abstract:

In this paper, we present a new and effective image indexing technique that extracts features directly from DCT domain. Our proposed approach is an object-based image indexing. For each block of size 8*8 in DCT domain a feature vector is extracted. Then, feature vectors of all blocks of image using a k-means algorithm is clustered into groups. Each cluster represents a special object of the image. Then we select some clusters that have largest members after clustering. The centroids of the selected clusters are taken as image feature vectors and indexed into the database. Also, we propose an approach for using of proposed image indexing method in automatic image classification. Experimental results on a database of 800 images from 8 semantic groups in automatic image classification are reported.

Keywords: Object-based image retrieval, DCT domain, Image indexing, Image classification.

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Authors: Suprijanto, Linda Sari, Vebi Nadhira , IGN. Merthayasa. Farida I.M

Abstract:

Detection of human emotions has many potential applications. One of application is to quantify attentiveness audience in order evaluate acoustic quality in concern hall. The subjective audio preference that based on from audience is used. To obtain fairness evaluation of acoustic quality, the research proposed system for multimodal emotion detection; one modality based on brain signals that measured using electroencephalogram (EEG) and the second modality is sequences of facial images. In the experiment, an audio signal was customized which consist of normal and disorder sounds. Furthermore, an audio signal was played in order to stimulate positive/negative emotion feedback of volunteers. EEG signal from temporal lobes, i.e. T3 and T4 was used to measured brain response and sequence of facial image was used to monitoring facial expression during volunteer hearing audio signal. On EEG signal, feature was extracted from change information in brain wave, particularly in alpha and beta wave. Feature of facial expression was extracted based on analysis of motion images. We implement an advance optical flow method to detect the most active facial muscle form normal to other emotion expression that represented in vector flow maps. The reduce problem on detection of emotion state, vector flow maps are transformed into compass mapping that represents major directions and velocities of facial movement. The results showed that the power of beta wave is increasing when disorder sound stimulation was given, however for each volunteer was giving different emotion feedback. Based on features derived from facial face images, an optical flow compass mapping was promising to use as additional information to make decision about emotion feedback.

Keywords: Multimodal Emotion Detection, EEG, Facial Image, Optical Flow, compass mapping, Brain Wave

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Authors: Rica Jell de Laza, Jose Alberto Fernandez, Andrea Marie Mendoza, Qristin Jeuel Regalado

Abstract:

This study investigates whether participants experience different levels of persuasion depending on the hemisphere of the brain and the tone of voice. The experiment was performed on 96 volunteer undergraduate students taking an introductory course in psychology. The participants took part in a 2 x 3 (Hemisphere: left, right x Tone of Voice: positive, neutral, negative) Mixed Factorial Design to measure how much a person was persuaded. Results showed that the hemisphere of the brain and the tone of voice used did not significantly affect the results individually. Furthermore, there was no interaction effect. Therefore, the hemispheres of the brain and the tone of voice employed play insignificant roles in persuading a person.

Keywords: Dichotic listening, brain hemisphere, tone of voice, persuasion.

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Authors: Chutimon Thitipornvanid, Siripun Sanguansintukul

Abstract:

Choosing the right metadata is a critical, as good information (metadata) attached to an image will facilitate its visibility from a pile of other images. The image-s value is enhanced not only by the quality of attached metadata but also by the technique of the search. This study proposes a technique that is simple but efficient to predict a single human image from a website using the basic image data and the embedded metadata of the image-s content appearing on web pages. The result is very encouraging with the prediction accuracy of 95%. This technique may become a great assist to librarians, researchers and many others for automatically and efficiently identifying a set of human images out of a greater set of images.

Keywords: Metadata, Prediction, Multi-layer perceptron, Human facial image, Image mining.

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Authors: Yi Zhang, Kai Lu, Ying-hui Gao, Mo Wang

Abstract:

Histogram plays an important statistical role in digital image processing. However, the existing quantum image models are deficient to do this kind of image statistical processing because different gray scales are not distinguishable. In this paper, a novel quantum image representation model is proposed firstly in which the pixels with different gray scales can be distinguished and operated simultaneously. Based on the new model, a fast quantum algorithm of constructing histogram for quantum image is designed. Performance comparison reveals that the new quantum algorithm could achieve an approximately quadratic speedup than the classical counterpart. The proposed quantum model and algorithm have significant meanings for the future researches of quantum image processing.

Keywords: Quantum Image Representation, Quantum Algorithm, Image Histogram.

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Authors: K.Somasundaram, S.Domnic

Abstract:

A low bit rate still image compression scheme by compressing the indices of Vector Quantization (VQ) and generating residual codebook is proposed. The indices of VQ are compressed by exploiting correlation among image blocks, which reduces the bit per index. A residual codebook similar to VQ codebook is generated that represents the distortion produced in VQ. Using this residual codebook the distortion in the reconstructed image is removed, thereby increasing the image quality. Our scheme combines these two methods. Experimental results on standard image Lena show that our scheme can give a reconstructed image with a PSNR value of 31.6 db at 0.396 bits per pixel. Our scheme is also faster than the existing VQ variants.

Keywords: Image compression, Vector Quantization, Residual Codebook.

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Authors: R. C. Joshi, Shashikala Tapaswi

Abstract:

The paper proposes an approach using genetic algorithm for computing the region based image similarity. The image is denoted using a set of segmented regions reflecting color and texture properties of an image. An image is associated with a family of image features corresponding to the regions. The resemblance of two images is then defined as the overall similarity between two families of image features, and quantified by a similarity measure, which integrates properties of all the regions in the images. A genetic algorithm is applied to decide the most plausible matching. The performance of the proposed method is illustrated using examples from an image database of general-purpose images, and is shown to produce good results.

Keywords: Image Features, color descriptor, segmented classes, texture descriptors, genetic algorithm.

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Authors: Atanu K Samanta, Asim Ali Khan

Abstract:

Due to the acquisition of huge amounts of brain tumor magnetic resonance images (MRI) in clinics, it is very difficult for radiologists to manually interpret and segment these images within a reasonable span of time. Computer-aided diagnosis (CAD) systems can enhance the diagnostic capabilities of radiologists and reduce the time required for accurate diagnosis. An intelligent computer-aided technique for automatic detection of a brain tumor through MRI is presented in this paper. The technique uses the following computational methods; the Level Set for segmentation of a brain tumor from other brain parts, extraction of features from this segmented tumor portion using gray level co-occurrence Matrix (GLCM), and the Artificial Neural Network (ANN) to classify brain tumor images according to their respective types. The entire work is carried out on 50 images having five types of brain tumor. The overall classification accuracy using this method is found to be 98% which is significantly good.

Keywords: Artificial neural network, ANN, brain tumor, computer-aided diagnostic, CAD system, gray-level co-occurrence matrix, GLCM, level set method, tumor segmentation.

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Authors: J. Mehena, M. C. Adhikary

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In this paper, a spatial multiple-kernel fuzzy C-means (SMKFCM) algorithm is introduced for segmentation problem. A linear combination of multiples kernels with spatial information is used in the kernel FCM (KFCM) and the updating rules for the linear coefficients of the composite kernels are derived as well. Fuzzy cmeans (FCM) based techniques have been widely used in medical image segmentation problem due to their simplicity and fast convergence. The proposed SMKFCM algorithm provides us a new flexible vehicle to fuse different pixel information in medical image segmentation and detection of MR images. To evaluate the robustness of the proposed segmentation algorithm in noisy environment, we add noise in medical brain tumor MR images and calculated the success rate and segmentation accuracy. From the experimental results it is clear that the proposed algorithm has better performance than those of other FCM based techniques for noisy medical MR images.

Keywords: Clustering, fuzzy C-means, image segmentation, MR images, multiple kernels.

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Authors: Zhepeng Rui, Zhenyu Gu, Caitilin de Bérigny

Abstract:

In the current study, we designed an interactive meditation and brain painting application to cultivate users’ creativity, promote meditation, reduce stress, and improve cognition while attempting to learn a foreign language. User tests and data analyses were conducted on 42 male and 42 female participants to better understand sex-associated psychological and aesthetic differences. Our method utilized brain-computer interfaces to import meditation and attention data to create artwork in meditation-based applications. Female participants showed statistically significantly different language learning outcomes following three meditation paradigms. The art style of brain painting helped females with language memory. Our results suggest that the most ideal methods for promoting memory attention were meditation methods and brain painting exercises contributing to language learning, memory concentration promotion, and foreign word memorization. We conclude that a short period of meditation practice can help in learning a foreign language. These findings provide insights into meditation, creative language education, brain-computer interface, and human-computer interactions.

Keywords: Brain-computer interface, creative thinking, meditation, mental health.

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Authors: Maha Sharkas, Dahlia R. ElShafie, Nadder Hamdy

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Image watermarking has proven to be quite an efficient tool for the purpose of copyright protection and authentication over the last few years. In this paper, a novel image watermarking technique in the wavelet domain is suggested and tested. To achieve more security and robustness, the proposed techniques relies on using two nested watermarks that are embedded into the image to be watermarked. A primary watermark in form of a PN sequence is first embedded into an image (the secondary watermark) before being embedded into the host image. The technique is implemented using Daubechies mother wavelets where an arbitrary embedding factor α is introduced to improve the invisibility and robustness. The proposed technique has been applied on several gray scale images where a PSNR of about 60 dB was achieved.

Keywords: Image watermarking, Multimedia Security, Wavelets, Image Processing.

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Authors: Ratchakit Sakuldee, Somkait Udomhunsakul

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Measurement of the quality of image compression is important for image processing application. In this paper, we propose an objective image quality assessment to measure the quality of gray scale compressed image, which is correlation well with subjective quality measurement (MOS) and least time taken. The new objective image quality measurement is developed from a few fundamental of objective measurements to evaluate the compressed image quality based on JPEG and JPEG2000. The reliability between each fundamental objective measurement and subjective measurement (MOS) is found. From the experimental results, we found that the Maximum Difference measurement (MD) and a new proposed measurement, Structural Content Laplacian Mean Square Error (SCLMSE), are the suitable measurements that can be used to evaluate the quality of JPEG200 and JPEG compressed image, respectively. In addition, MD and SCLMSE measurements are scaled to make them equivalent to MOS, given the rate of compressed image quality from 1 to 5 (unacceptable to excellent quality).

Keywords: JPEG, JPEG2000, objective image quality measurement, subjective image quality measurement, correlation coefficients.

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Authors: Muhammad Wajid Tahir, Rubina Kauser, Majid Ali Tahir

Abstract:

Pakistani doctors (MBBS) are emigrating towards developed countries for professional adjustments. This study aims to highlight causes and consequences of doctors- brain drain from Pakistan. Primary data was collected from Mayo Hospital, Lahore by interviewing doctors (n=100) through systematic random sampling technique. It found that various socio-economic and political conditions are working as push and pull factors for brain drain of doctors in Pakistan. Majority of doctors (83%) declared poor remunerations and professional infrastructure of health department as push factor of doctors- brain drain. 81% claimed that continuous instability in political situation and threats of terrorism are responsible for emigration of doctors. 84% respondents considered fewer opportunities of further studies responsible for their emigration. Brain drain of doctors is affecting health sector-s policies / programs, standard doctor-patient ratios and quality of health services badly.

Keywords: Brain Drain, Emigration, Remuneration, Politicalinstability, MBBS doctors

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Authors: Xueting Liu, Daojin Song, Chuandai Dong, Hongkui Li

Abstract:

From a set of shifted, blurred, and decimated image , super-resolution image reconstruction can get a high-resolution image. So it has become an active research branch in the field of image restoration. In general, super-resolution image restoration is an ill-posed problem. Prior knowledge about the image can be combined to make the problem well-posed, which contributes to some regularization methods. In the regularization methods at present, however, regularization parameter was selected by experience in some cases and other techniques have too heavy computation cost for computing the parameter. In this paper, we construct a new super-resolution algorithm by transforming the solving of the System stem Є=An into the solving of the equations X+A*X-1A=I , and propose an inverse iterative method.

Keywords: High-resolution MAP image, Reconstruction, Image interpolation, Motion Estimation, Hermitian positive definite solutions.

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Authors: Awais Adnan, Muhammad Ali, Amir Hanif Dar

Abstract:

Image Searching was always a problem specially when these images are not properly managed or these are distributed over different locations. Currently different techniques are used for image search. On one end, more features of the image are captured and stored to get better results. Storing and management of such features is itself a time consuming job. While on the other extreme if fewer features are stored the accuracy rate is not satisfactory. Same image stored with different visual properties can further reduce the rate of accuracy. In this paper we present a new concept of using polynomials of sorted histogram of the image. This approach need less overhead and can cope with the difference in visual features of image.

Keywords: Sorted Histogram, Polynomial Curves, feature pointsof images, Grayscale, visual properties of image.

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Authors: Hui Hui Wang, Dzulkifli Mohamad, N.A Ismail

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This paper attempts to discuss the evolution of the retrieval techniques focusing on development, challenges and trends of the image retrieval. It highlights both the already addressed and outstanding issues. The explosive growth of image data leads to the need of research and development of Image Retrieval. However, Image retrieval researches are moving from keyword, to low level features and to semantic features. Drive towards semantic features is due to the problem of the keywords which can be very subjective and time consuming while low level features cannot always describe high level concepts in the users- mind.

Keywords: content based image retrieval, keyword based imageretrieval, semantic gap, semantic image retrieval.

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Authors: K. Aloui, M. S. Naceur

Abstract:

The main objective of this paper is to provide an efficient tool for delineating brain tumors in three-dimensional magnetic resonance images. To achieve this goal, we use basically a level-sets approach to delineating three-dimensional brain tumors. Then we introduce a compression plan of 3D brain structures based for the meshes simplification, adapted for time to the specific needs of the telemedicine and to the capacities restricted by network communication. We present here the main stages of our system, and preliminary results which are very encouraging for clinical practice.

Keywords: Medical imaging, level-sets, compression, meshess implification, telemedicine.

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Authors: İsmet Öztürk, İbrahim Soğukpınar

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With the fast progression of data exchange in electronic way, information security is becoming more important in data storage and transmission. Because of widely using images in industrial process, it is important to protect the confidential image data from unauthorized access. In this paper, we analyzed current image encryption algorithms and compression is added for two of them (Mirror-like image encryption and Visual Cryptography). Implementations of these two algorithms have been realized for experimental purposes. The results of analysis are given in this paper.

Keywords: image encryption, image cryptosystem, security, transmission

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Authors: K. Sakthivel, R. Nallusamy, C. Kavitha

Abstract:

The goal of image segmentation is to cluster pixels into salient image regions. Segmentation could be used for object recognition, occlusion boundary estimation within motion or stereo systems, image compression, image editing, or image database lookup. In this paper, we present a color image segmentation using support vector machine (SVM) pixel classification. Firstly, the pixel level color and texture features of the image are extracted and they are used as input to the SVM classifier. These features are extracted using the homogeneity model and Gabor Filter. With the extracted pixel level features, the SVM Classifier is trained by using FCM (Fuzzy C-Means).The image segmentation takes the advantage of both the pixel level information of the image and also the ability of the SVM Classifier. The Experiments show that the proposed method has a very good segmentation result and a better efficiency, increases the quality of the image segmentation compared with the other segmentation methods proposed in the literature.

Keywords: Image Segmentation, Support Vector Machine, Fuzzy C–Means, Pixel Feature, Texture Feature, Homogeneity model, Gabor Filter.

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Authors: Jamaliah Mohd. Yusof, Rosidah Musa, Sofiah Abd. Rahman

Abstract:

With previous studies that examined the importance of functional store image and CSR, this study is aimed at examining their effects in the self-congruity model in influencing store loyalty. In particular, this study developed and tested a structural model in the context of retailing industry on the self-congruity theory. Whilst much of the self-congruity studies have incorporated functional store image, there has been lack of studies that examined social responsibility image of retail stores in the self-congruity studies. Findings indicate that self-congruity influence on store loyalty was mediated by both functional store image and social responsibility image. In influencing store loyalty, the findings have shown that social responsibility image has a stronger influence on store loyalty than functional store image. This study offers important findings and implications for future research as it presents a new framework on the importance of social responsibility image.

Keywords: Self-congruity, functional store image, social responsibility image, store loyalty

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Authors: Nebi Gedik

Abstract:

The goal of motor image brain computer interface research is to create a link between the central nervous system and a computer or device. The most important signal for brain-computer interface is the electroencephalogram. The aim of this research is to explore a set of effective features from EEG signals, separated into frequency bands, using wave atom sub-bands to discriminate right and left-hand motor imagery signals. Over the transform coefficients, feature vectors are constructed for each frequency range and each transform sub-band, and their classification performances are tested. The method is validated using EEG signals from the BCI competition III dataset IIIa and classifiers such as support vector machine and k-nearest neighbors.

Keywords: motor imagery, EEG, Wave atom transform sub-bands, SVM, k-NN

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Authors: Abraham Varghese, Kannan Balakrishnan, Reji R. Varghese, Joseph S. Paul

Abstract:

Magnetic Resonance Imaging play a vital role in the decision-diagnosis process of brain MR images. For an accurate diagnosis of brain related problems, the experts mostly compares both T1 and T2 weighted images as the information presented in these two images are complementary. In this paper, rotational and translational invariant form of Local binary Pattern (LBP) with additional gray scale information is used to retrieve similar slices of T1 weighted images from T2 weighted images or vice versa. The incorporation of additional gray scale information on LBP can extract more local texture information. The accuracy of retrieval can be improved by extracting moment features of LBP and reweighting the features based on users feedback. Here retrieval is done in a single subject scenario where similar images of a particular subject at a particular level are retrieved, and multiple subjects scenario where relevant images at a particular level across the subjects are retrieved.

Keywords: Local Binary pattern (LBP), Modified Local Binary pattern (MOD-LBP), T1 and T2 weighted images, Moment features.

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Authors: Vlado Kitanovski, Dimitar Taskovski, Sofija Bogdanova

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In this paper we present a combined hashing/watermarking method for image authentication. A robust image hash, invariant to legitimate modifications, but fragile to illegitimate modifications is generated from the local image characteristics. To increase security of the system the watermark is generated using the image hash as a key. Quantized Index Modulation of DCT coefficients is used for watermark embedding. Watermark detection is performed without use of the original image. Experimental results demonstrate the effectiveness of the presented method in terms of robustness and fragility.

Keywords: authentication, blind watermarking, image hash, semi-fragile watermarking

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Authors: A. Habibizad Navin, A. Sadighi, M. Naghian Fesharaki, M. Mirnia, M. Teshnelab, R. Keshmiri

Abstract:

This paper presents a new data oriented model of image. Then a representation of it, ADBT, is introduced. The ability of ADBT is clustering, segmentation, measuring similarity of images etc, with desired precision and corresponding speed.

Keywords: Data oriented modelling, image, clustering, segmentation, classification, ADBT and image processing.

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Authors: Qiu Chen, Feifei Lee, Koji Kotani, Tadahiro Ohmi

Abstract:

In this paper, we present local image descriptor using VQ-SIFT for more effective and efficient image retrieval. Instead of SIFT's weighted orientation histograms, we apply vector quantization (VQ) histogram as an alternate representation for SIFT features. Experimental results show that SIFT features using VQ-based local descriptors can achieve better image retrieval accuracy than the conventional algorithm while the computational cost is significantly reduced.

Keywords: SIFT feature, Vector quantization histogram, Localdescriptor, Image retrieval.

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Authors: H. Motameni, M. Norouzi, M. Jahandar, A. Hatami

Abstract:

In this paper a way of hiding text message (Steganography) in the gray image has been presented. In this method tried to find binary value of each character of text message and then in the next stage, tried to find dark places of gray image (black) by converting the original image to binary image for labeling each object of image by considering on 8 connectivity. Then these images have been converted to RGB image in order to find dark places. Because in this way each sequence of gray color turns into RGB color and dark level of grey image is found by this way if the Gary image is very light the histogram must be changed manually to find just dark places. In the final stage each 8 pixels of dark places has been considered as a byte and binary value of each character has been put in low bit of each byte that was created manually by dark places pixels for increasing security of the main way of steganography (LSB).

Keywords: Binary image, labeling, low bit, neighborhood, RGB image, steganography, threshold.

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Authors: Mounira Rouaïnia, Mohamed Salah Medjram, Noureddine Doghmane

Abstract:

In Multiple Sclerosis, pathological changes in the brain results in deviations in signal intensity on Magnetic Resonance Images (MRI). Quantitative analysis of these changes and their correlation with clinical finding provides important information for diagnosis. This constitutes the objective of our work. A new approach is developed. After the enhancement of images contrast and the brain extraction by mathematical morphology algorithm, we proceed to the brain segmentation. Our approach is based on building statistical model from data itself, for normal brain MRI and including clustering tissue type. Then we detect signal abnormalities (MS lesions) as a rejection class containing voxels that are not explained by the built model. We validate the method on MR images of Multiple Sclerosis patients by comparing its results with those of human expert segmentation.

Keywords: EM algorithm, Magnetic Resonance Imaging, Mathematical morphology, Markov random model.

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Authors: Pinaki Pratim Acharjya, Esha Dutta

Abstract:

In image segmentation contour detection is one of the important pre-processing steps in recent days. Contours characterize boundaries and contour detection is one of the most difficult tasks in image processing. Hence it is a problem of fundamental importance in image processing. Contour detection of an image decreases the volume of data considerably and useless information is removed, but the structural properties of the image remain same. In this research, a robust and effective contour detection technique has been proposed using mathematical morphology. Three different contour detection results are obtained by using morphological dilation and erosion. The comparative analyses of three different results also have been done.

Keywords: Image segmentation, contour detection, mathematical morphology.

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Authors: M. Kutila, J. Viitanen

Abstract:

This paper presents image compression with wavelet based method. The wavelet transformation divides image to low- and high pass filtered parts. The traditional JPEG compression technique requires lower computation power with feasible losses, when only compression is needed. However, there is obvious need for wavelet based methods in certain circumstances. The methods are intended to the applications in which the image analyzing is done parallel with compression. Furthermore, high frequency bands can be used to detect changes or edges. Wavelets enable hierarchical analysis for low pass filtered sub-images. The first analysis can be done for a small image, and only if any interesting is found, the whole image is processed or reconstructed.

Keywords: image compression, jpeg, wavelet, vlc

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Authors: Pinaki Pratim Acharjya, Dibyendu Ghoshal

Abstract:

Image matching methods play a key role in deciding correspondence between two image scenes. This paper presents a method for the matching of digital images using mathematical morphology. The proposed method has been applied to real life images. The matching process has shown successful and promising results.

Keywords: Digital image, gradients, image matching, mathematical morphology.

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