Search results for: satellite images

Authors: Arturo Aquino, Manuel Emilio Gegundez, Diego Marin

Abstract:

In this paper, a new automated methodology to detect the optic disc (OD) automatically in retinal images from patients with risk of being affected by Diabetic Retinopathy (DR) and Macular Edema (ME) is presented. The detection procedure comprises two independent methodologies. On one hand, a location methodology obtains a pixel that belongs to the OD using image contrast analysis and structure filtering techniques and, on the other hand, a boundary segmentation methodology estimates a circular approximation of the OD boundary by applying mathematical morphology, edge detection techniques and the Circular Hough Transform. The methodologies were tested on a set of 1200 images composed of 229 retinographies from patients affected by DR with risk of ME, 431 with DR and no risk of ME and 540 images of healthy retinas. The location methodology obtained 98.83% success rate, whereas the OD boundary segmentation methodology obtained good circular OD boundary approximation in 94.58% of cases. The average computational time measured over the total set was 1.67 seconds for OD location and 5.78 seconds for OD boundary segmentation.

Keywords: Diabetic retinopathy, macular edema, optic disc, automated detection, automated segmentation.

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Authors: Lv Meibo, Naqvi Najam Abbas, Hina Arshad, Li YanJun

Abstract:

This research contribution is drafted to present the orbit design, orbit propagator and geomagnetic field estimator for the nanosatellites specifically for the upcoming CUBESAT, ICUBE-1 of the Institute of Space Technology (IST), Islamabad, Pakistan. The ICUBE mission is designed for the low earth orbit at the approximate height of 700KM. The presented research endeavor designs the Keplarian elements for ICUBE-1 orbit while incorporating the mission requirements and propagates the orbit using J2 perturbations, The attitude determination system of the ICUBE-1 consists of attitude determination sensors like magnetometer and sun sensor. The Geomagnetic field estimator is developed according to the model of International Geomagnetic Reference Field (IGRF) for comparing the magnetic field measurements by the magnetometer for attitude determination. The output of the propagator namely the Keplarians position and velocity vectors and the magnetic field vectors are compared and verified with the same scenario generated in the  Satellite Tool Kit (STK).

Keywords: CUBESAT, Geomagnetic Field, ICUBE-1, Orbit Propagator, Satellite.

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Authors: Carlos Vera, Tetyana Baydyk, Ernst Kussul, Graciela Velasco, Miguel Aparicio

Abstract:

This article describes the Random Subspace Neural Classifier (RSC) for the recognition of meteors in the night sky. We used images of meteors entering the atmosphere at night between 8:00 p.m.-5: 00 a.m. The objective of this project is to classify meteor and star images (with stars as the image background). The monitoring of the sky and the classification of meteors are made for future applications by scientists. The image database was collected from different websites. We worked with RGB-type images with dimensions of 220x220 pixels stored in the BitMap Protocol (BMP) format. Subsequent window scanning and processing were carried out for each image. The scan window where the characteristics were extracted had the size of 20x20 pixels with a scanning step size of 10 pixels. Brightness, contrast and contour orientation histograms were used as inputs for the RSC. The RSC worked with two classes and classified into: 1) with meteors and 2) without meteors. Different tests were carried out by varying the number of training cycles and the number of images for training and recognition. The percentage error for the neural classifier was calculated. The results show a good RSC classifier response with 89% correct recognition. The results of these experiments are presented and discussed.

Keywords: Contour orientation histogram, meteors, night sky, RSC neural classifier, stars.

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Authors: Mustafa Kisa, Fatih Mehmet Botsali

Abstract:

Recent developments in automotive technology are focused on economy, comfort and safety. Vehicle tracking and collision detection systems are attracting attention of many investigators focused on safety of driving in the field of automotive mechatronics. In this paper, a vision-based vehicle detection system is presented. Developed system is intended to be used in collision detection and driver alert. The system uses RGB images captured by a camera in a car driven in the highway. Images captured by the moving camera are used to detect the moving vehicles in the image. A vehicle ahead of the camera is detected in daylight conditions. The proposed method detects moving vehicles by subtracting successive images. Plate height of the vehicle is determined by using a plate recognition algorithm. Distance of the moving object is calculated by using the plate height. After determination of the distance of the moving vehicle relative speed of the vehicle and Time-to-Collision are calculated by using distances measured in successive images. Results obtained in road tests are discussed in order to validate the use of the proposed method.

Keywords: Image possessing, vehicle tracking, license plate detection, computer vision.

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Authors: Farshideh Einsele, Hassan Foroosh

Abstract:

In this paper, we present a robust algorithm to recognize extracted text from grocery product images captured by mobile phone cameras. Recognition of such text is challenging since text in grocery product images varies in its size, orientation, style, illumination, and can suffer from perspective distortion. Pre-processing is performed to make the characters scale and rotation invariant. Since text degradations can not be appropriately defined using well-known geometric transformations such as translation, rotation, affine transformation and shearing, we use the whole character black pixels as our feature vector. Classification is performed with minimum distance classifier using the maximum likelihood criterion, which delivers very promising Character Recognition Rate (CRR) of 89%. We achieve considerably higher Word Recognition Rate (WRR) of 99% when using lower level linguistic knowledge about product words during the recognition process.

Keywords: Camera-based OCR, Feature extraction, Document and image processing.

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Authors: A. Nachour, L. Ouzizi, Y. Aoura

Abstract:

Recent developments on multi-agent system have brought a new research field on image processing. Several algorithms are used simultaneously and improved in deferent applications while new methods are investigated. This paper presents a new automatic method for edge detection using several agents and many different actions. The proposed multi-agent system is based on parallel agents that locally perceive their environment, that is to say, pixels and additional environmental information. This environment is built using Vector Field Convolution that attract free agent to the edges. Problems of partial, hidden or edges linking are solved with the cooperation between agents. The presented method was implemented and evaluated using several examples on different synthetic and medical images. The obtained experimental results suggest that this approach confirm the efficiency and accuracy of detected edge.

Keywords: Edge detection, medical MR images, multi-agent systems, vector field convolution.

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Authors: Ferreira F. C. L., Souza D. N., Rodrigues T. M. A., Cunha C. J., Dullius M. A., Andrade J. E., Sousa A. H., Vieira J. P. C., Carvalho Júnior A. B., Santos L. P. B., Passos R. O.

Abstract:

The objective this study was to characterize and develop anthropomorphic liver phantoms in tomography hepatic procedures for quality control and improvement professionals in nuclear medicine. For the conformation of the anthropomorphic phantom was used in plaster and acrylic. We constructed three phantoms representing processes with liver cirrhosis. The phantoms were filled with 99mTc diluted with water to obtain the scintigraphic images. Tomography images were analyzed anterior and posterior phantom representing a body with a greater degree cirrhotic. It was noted that the phantoms allow the acquisition of images similar to real liver with cirrhosis. Simulations of hemangiomas may contribute to continued professional education of nuclear medicine, on the question of image acquisition, allowing of the study parameters such of the matrix, energy window and count statistics.

Keywords: Nuclear medicine, liver phantom, control quality

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Authors: Zonglin Yang, Tatsuya Akiyama, Kerry S. Williamson, Michael J. Franklin, Thiruvarangan Ramaraj

Abstract:

Pseudomonas aeruginosa is an opportunistic pathogen that forms surface-associated microbial communities (biofilms) on artificial implant devices and on human tissue. Biofilm infections are difficult to treat with antibiotics, in part, because the bacteria in biofilms are physiologically heterogeneous. One measure of biological heterogeneity in a population of cells is to quantify the cellular concentrations of ribosomes, which can be probed with fluorescently labeled nucleic acids. The fluorescent signal intensity following fluorescence in situ hybridization (FISH) analysis correlates to the cellular level of ribosomes. The goals here are to provide computationally and statistically robust approaches to automatically quantify cellular heterogeneity in biofilms from a large library of epifluorescent microscopy FISH images. In this work, the initial steps were developed toward these goals by developing an automated biofilm detection approach for use with FISH images. The approach allows rapid identification of biofilm regions from FISH images that are counterstained with fluorescent dyes. This methodology provides advances over other computational methods, allowing subtraction of spurious signals and non-biological fluorescent substrata. This method will be a robust and user-friendly approach which will enable users to semi-automatically detect biofilm boundaries and extract intensity values from fluorescent images for quantitative analysis of biofilm heterogeneity.

Keywords: Image informatics, Pseudomonas aeruginosa, biofilm, FISH, computer vision, data visualization.

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Authors: Zhen-Hui Hu, Jyh-Shong Ju, Ming-Hwei Perng

Abstract:

This paper presents a useful sub-pixel image registration method using line segments and a sub-pixel edge detector. In this approach, straight line segments are first extracted from gray images at the pixel level before applying the sub-pixel edge detector. Next, all sub-pixel line edges are mapped onto the orientation-distance parameter space to solve for line correspondence between images. Finally, the registration parameters with sub-pixel accuracy are analytically solved via two linear least-square problems. The present approach can be applied to various fields where fast registration with sub-pixel accuracy is required. To illustrate, the present approach is applied to the inspection of printed circuits on a flat panel. Numerical example shows that the present approach is effective and accurate when target images contain a sufficient number of line segments, which is true in many industrial problems.

Keywords: Defect detection, Image registration, Straight line segment, Sub-pixel.

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Authors: Yanhua Jin, Won Suk Lee

Abstract:

The two-dimensional gel electrophoresis method (2-DE) is widely used in Proteomics to separate thousands of proteins in a sample. By comparing the protein expression levels of proteins in a normal sample with those in a diseased one, it is possible to identify a meaningful set of marker proteins for the targeted disease. The major shortcomings of this approach involve inherent noises and irregular geometric distortions of spots observed in 2-DE images. Various experimental conditions can be the major causes of these problems. In the protein analysis of samples, these problems eventually lead to incorrect conclusions. In order to minimize the influence of these problems, this paper proposes a partition based pair extension method that performs spot-matching on a set of gel images multiple times and segregates more reliable mapping results which can improve the accuracy of gel image analysis. The improved accuracy of the proposed method is analyzed through various experiments on real 2-DE images of human liver tissues.

Keywords: Proteomics, spot-matching, two-dimensionalelectrophoresis.

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Authors: Mehrnoosh Omati, Mahmod Reza Sahebi

Abstract:

The information on land use/land cover changing plays an essential role for environmental assessment, planning and management in regional development. Remotely sensed imagery is widely used for providing information in many change detection applications. Polarimetric Synthetic aperture radar (PolSAR) image, with the discrimination capability between different scattering mechanisms, is a powerful tool for environmental monitoring applications. This paper proposes a new boundary-based segmentation algorithm as a fundamental step for land cover change detection. In this method, first, two PolSAR images are segmented using integration of marker-controlled watershed algorithm and coupled Markov random field (MRF). Then, object-based classification is performed to determine changed/no changed image objects. Compared with pixel-based support vector machine (SVM) classifier, this novel segmentation algorithm significantly reduces the speckle effect in PolSAR images and improves the accuracy of binary classification in object-based level. The experimental results on Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) polarimetric images show a 3% and 6% improvement in overall accuracy and kappa coefficient, respectively. Also, the proposed method can correctly distinguish homogeneous image parcels.

Keywords: Coupled Markov random field, environment, object-based analysis, Polarimetric SAR images.

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Authors: A. Tellaeche, R. Arana, I.Maurtua

Abstract:

The exhaustive quality control is becoming more and more important when commercializing competitive products in the world's globalized market. Taken this affirmation as an undeniable truth, it becomes critical in certain sector markets that need to offer the highest restrictions in quality terms. One of these examples is the percussion cap mass production, a critical element assembled in firearm ammunition. These elements, built in great quantities at a very high speed, must achieve a minimum tolerance deviation in their fabrication, due to their vital importance in firing the piece of ammunition where they are built in. This paper outlines a machine vision development for the 100% inspection of percussion caps obtaining data from 2D and 3D simultaneous images. The acquisition speed and precision of these images from a metallic reflective piece as a percussion cap, the accuracy of the measures taken from these images and the multiple fabrication errors detected make the main findings of this work.

Keywords: critical tolerance, high speed decision makingsimultaneous 2D/3D machine vision.

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Authors: Ayman A. AbuBaker, Rami S.Qahwaji, Musbah J. Aqel, Mohmmad H. Saleh

Abstract:

Two algorithms are proposed to reduce the storage requirements for mammogram images. The input image goes through a shrinking process that converts the 16-bit images to 8-bits by using pixel-depth conversion algorithm followed by enhancement process. The performance of the algorithms is evaluated objectively and subjectively. A 50% reduction in size is obtained with no loss of significant data at the breast region.

Keywords: Breast cancer, Image processing, Image reduction, Mammograms, Image enhancement

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Authors: Samy Sadek, Ayoub Al-Hamadi, Bernd Michaelis, Usama Sayed

Abstract:

In this paper, we propose a supervised method for color image classification based on a multilevel sigmoidal neural network (MSNN) model. In this method, images are classified into five categories, i.e., “Car", “Building", “Mountain", “Farm" and “Coast". This classification is performed without any segmentation processes. To verify the learning capabilities of the proposed method, we compare our MSNN model with the traditional Sigmoidal Neural Network (SNN) model. Results of comparison have shown that the MSNN model performs better than the traditional SNN model in the context of training run time and classification rate. Both color moments and multi-level wavelets decomposition technique are used to extract features from images. The proposed method has been tested on a variety of real and synthetic images.

Keywords: Image classification, multi-level neural networks, feature extraction, wavelets decomposition.

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Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

Abstract:

Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: Artificial neural networks, digital image processing, pattern recognition.

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Authors: Sangbum Park, Namki Lee, Youngjoon Han, Hernsoo Hahn

Abstract:

In this paper, we propose the robust water level detection method based on the accumulated histogram under small changed image which is acquired from water level surveillance camera. In general surveillance system, this is detecting and recognizing invasion from searching area which is in big change on the sequential images. However, in case of a water level detection system, these general surveillance techniques are not suitable due to small change on the water surface. Therefore the algorithm introduces the accumulated histogram which is emphasizing change of water surface in sequential images. Accumulated histogram is based on the current image frame. The histogram is cumulating differences between previous images and current image. But, these differences are also appeared in the land region. The band pass filter is able to remove noises in the accumulated histogram Finally, this algorithm clearly separates water and land regions. After these works, the algorithm converts from the water level value on the image space to the real water level on the real space using calibration table. The detected water level is sent to the host computer with current image. To evaluate the proposed algorithm, we use test images from various situations.

Keywords: accumulated histogram, water level detection, band pass filter.

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Authors: Yanli Qi, Ning Lv, Jing Li

Abstract:

Sub-Nyquist sampling jamming method (SNSJ) is a well known deception jamming method for inverse synthetic aperture radar (ISAR). However, the anti-decoy of the SNSJ method performs easier since the amplitude of the false-target images are weaker than the real-target image; the false-target images always lag behind the real-target image, and all targets are located in the same cross-range. In order to overcome the drawbacks mentioned above, a simple modulation based on SNSJ (M-SNSJ) is presented in this paper. The method first uses amplitude modulation factor to make the amplitude of the false-target images consistent with the real-target image, then uses the down-range modulation factor and cross-range modulation factor to make the false-target images move freely in down-range and cross-range, respectively, thus the capacity of deception is improved. Finally, the simulation results on the six available combinations of three modulation factors are given to illustrate our conclusion.

Keywords: Inverse synthetic aperture radar, ISAR, deceptive jamming, Sub-Nyquist sampling jamming method, SNSJ, modulation based on Sub-Nyquist sampling jamming method, M-SNSJ.

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Authors: Pradeep Singh, Sukhwinder Singh, Gurjinder Kaur

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In recent years, rapid advances in software and hardware in the field of information technology along with a digital imaging revolution in the medical domain facilitate the generation and storage of large collections of images by hospitals and clinics. To search these large image collections effectively and efficiently poses significant technical challenges, and it raises the necessity of constructing intelligent retrieval systems. Content-based Image Retrieval (CBIR) consists of retrieving the most visually similar images to a given query image from a database of images[5]. Medical CBIR (content-based image retrieval) applications pose unique challenges but at the same time offer many new opportunities. On one hand, while one can easily understand news or sports videos, a medical image is often completely incomprehensible to untrained eyes.

Keywords: Classification, clustering, content-based image retrieval (CBIR), relevance feedback (RF), statistical similarity matching, support vector machine (SVM).

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Authors: Jandot Aurélia

Abstract:

As the nuclear power is a sensitive field leading to controversy, the quality of the communication about it is important. Between 1965 and 1981, in France, this one had gradually changed. This change is studied here in the main French news magazine L’Express, in connection with several parameters. As this represents a huge number of copies and occurrences, thus a considerable amount of information; this paper is focused on the main articles as well as the main “mental images”. These ones are important, as their aim is to direct the thought of the readers, and as they have led the public awareness to evolve. Over this 17 years, two trends are in confrontation: The first one is promoting the perception of the nuclear power, while the second one is discrediting it. These trends are organized in two axes: the evolution of engineering, and the risks. In both cases, the changes in the language allow discerning the deepest intentions of the magazine editing, over a period when the nuclear technology, to there a laboratory object accompanied with mystery and secret, has become a social issue seemingly open to all.

Keywords: French news magazine, mental images, nuclear power, public awareness.

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Authors: Ghazy M.R. Assassa, Mona F. M. Mursi, Hatim A. Aboalsamh

Abstract:

Traditional principal components analysis (PCA) techniques for face recognition are based on batch-mode training using a pre-available image set. Real world applications require that the training set be dynamic of evolving nature where within the framework of continuous learning, new training images are continuously added to the original set; this would trigger a costly continuous re-computation of the eigen space representation via repeating an entire batch-based training that includes the old and new images. Incremental PCA methods allow adding new images and updating the PCA representation. In this paper, two incremental PCA approaches, CCIPCA and IPCA, are examined and compared. Besides, different learning and testing strategies are proposed and applied to the two algorithms. The results suggest that batch PCA is inferior to both incremental approaches, and that all CCIPCAs are practically equivalent.

Keywords: Candid covariance-free incremental principal components analysis (CCIPCA), face recognition, incremental principal components analysis (IPCA).

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Authors: Shujun Fu, Qiuqi Ruan, Wenqia Wang

Abstract:

Image interpolation is a common problem in imaging applications. However, most interpolation algorithms in existence suffer visually the effects of blurred edges and jagged artifacts in the image to some extent. This paper presents an adaptive feature preserving bidirectional flow process, where an inverse diffusion is performed to sharpen edges along the normal directions to the isophote lines (edges), while a normal diffusion is done to remove artifacts (“jaggies") along the tangent directions. In order to preserve image features such as edges, corners and textures, the nonlinear diffusion coefficients are locally adjusted according to the directional derivatives of the image. Experimental results on synthetic images and nature images demonstrate that our interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional interpolations.

Keywords: anisotropic diffusion, bidirectional flow, directional derivatives, edge enhancement, image interpolation, inverse flow, shock filter.

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Authors: Low Khong Teck, Hasan S. M. Al-Khaffaf, Abdullah Zawawi Talib, Tan Kian Lam

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In this paper, an extended study is performed on the effect of different factors on the quality of vector data based on a previous study. In the noise factor, one kind of noise that appears in document images namely Gaussian noise is studied while the previous study involved only salt-and-pepper noise. High and low levels of noise are studied. For the noise cleaning methods, algorithms that were not covered in the previous study are used namely Median filters and its variants. For the vectorization factor, one of the best available commercial raster to vector software namely VPstudio is used to convert raster images into vector format. The performance of line detection will be judged based on objective performance evaluation method. The output of the performance evaluation is then analyzed statistically to highlight the factors that affect vector quality.

Keywords: Performance Evaluation, Vectorization, Median Filter, Gaussian Noise.

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Authors: Ashish Thakur, Radhey Shyam Anand

Abstract:

This paper presents the region based segmentation method for ultrasound images using local statistics. In this segmentation approach the homogeneous regions depends on the image granularity features, where the interested structures with dimensions comparable to the speckle size are to be extracted. This method uses a look up table comprising of the local statistics of every pixel, which are consisting of the homogeneity and similarity bounds according to the kernel size. The shape and size of the growing regions depend on this look up table entries. The algorithms are implemented by using connected seeded region growing procedure where each pixel is taken as seed point. The region merging after the region growing also suppresses the high frequency artifacts. The updated merged regions produce the output in formed of segmented image. This algorithm produces the results that are less sensitive to the pixel location and it also allows a segmentation of the accurate homogeneous regions.

Keywords: Local statistics, region growing, segmentation, ultrasound images.

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Authors: Karim Sobh, Khaled El-Ayat, Fady Morcos, Amr El-Kadi

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Distributed applications deployed on LEO satellites and ground stations require substantial communication between different members in a constellation to overcome the earth coverage barriers imposed by GEOs. Applications running on LEO constellations suffer the earth line-of-sight blockage effect. They need adequate lab testing before launching to space. We propose a scalable cloud-based network simulation framework to simulate problems created by the earth line-of-sight blockage. The framework utilized cloud IaaS virtual machines to simulate LEO satellites and ground stations distributed software. A factorial ANOVA statistical analysis is conducted to measure simulator overhead on overall communication performance. The results showed a very low simulator communication overhead. Consequently, the simulation framework is proposed as a candidate for testing LEO constellations with distributed software in the lab before space launch.

Keywords: LEO, Cloud Computing, Constellation, Satellite, Network Simulation, Netfilter.

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Authors: Normi Abdul Hadi, Arsmah Ibrahim, Fatimah Yahya, Jamaludin Md. Ali

Abstract:

Beta-spline is built on G2 continuity which guarantees smoothness of generated curves and surfaces using it. This curve is preferred to be used in object design rather than reconstruction. This study however, employs the Beta-spline in reconstructing a 3- dimensional G2 image of the Stanford Rabbit. The original data consists of multi-slice binary images of the rabbit. The result is then compared with related works using other techniques.

Keywords: Beta-spline, multi-slice image, rectangular surface, 3D reconstruction

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Authors: Shujun Fu, Qiuqi Ruan, Wenqia Wang

Abstract:

Image interpolation is a common problem in imaging applications. However, most interpolation algorithms in existence suffer visually to some extent the effects of blurred edges and jagged artifacts in the image. This paper presents an adaptive feature preserving bidirectional flow process, where an inverse diffusion is performed to enhance edges along the normal directions to the isophote lines (edges), while a normal diffusion is done to remove artifacts (''jaggies'') along the tangent directions. In order to preserve image features such as edges, angles and textures, the nonlinear diffusion coefficients are locally adjusted according to the first and second order directional derivatives of the image. Experimental results on synthetic images and nature images demonstrate that our interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional interpolations.

Keywords: anisotropic diffusion, bidirectional flow, directionalderivatives, edge enhancement, image interpolation, inverse flow, shock filter.

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Authors: Swetha Kurup, Sridhar G., Sridhar V.

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In this paper we present a novel technique for data hiding in binary document images. We use the concept of entropy in order to identify document specific least distortive areas throughout the binary document image. The document image is treated as any other image and the proposed method utilizes the standard document characteristics for the embedding process. Proposed method minimizes perceptual distortion due to embedding and allows watermark extraction without the requirement of any side information at the decoder end.

Keywords: Entropy, Steganography, Watermarking.

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Authors: Sumathi Poobal, G. Ravindran

Abstract:

Image compression is one of the most important applications Digital Image Processing. Advanced medical imaging requires storage of large quantities of digitized clinical data. Due to the constrained bandwidth and storage capacity, however, a medical image must be compressed before transmission and storage. There are two types of compression methods, lossless and lossy. In Lossless compression method the original image is retrieved without any distortion. In lossy compression method, the reconstructed images contain some distortion. Direct Cosine Transform (DCT) and Fractal Image Compression (FIC) are types of lossy compression methods. This work shows that lossy compression methods can be chosen for medical image compression without significant degradation of the image quality. In this work DCT and Fractal Compression using Partitioned Iterated Function Systems (PIFS) are applied on different modalities of images like CT Scan, Ultrasound, Angiogram, X-ray and mammogram. Approximately 20 images are considered in each modality and the average values of compression ratio and Peak Signal to Noise Ratio (PSNR) are computed and studied. The quality of the reconstructed image is arrived by the PSNR values. Based on the results it can be concluded that the DCT has higher PSNR values and FIC has higher compression ratio. Hence in medical image compression, DCT can be used wherever picture quality is preferred and FIC is used wherever compression of images for storage and transmission is the priority, without loosing picture quality diagnostically.

Keywords: DCT, FIC, PIFS, PSNR.

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Authors: Alessandro Bevilacqua, Alessandro Gherardi, Ludovico Carozza, Filippo Piccinini

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The last years have seen an increasing use of image analysis techniques in the field of biomedical imaging, in particular in microscopic imaging. The basic step for most of the image analysis techniques relies on a background image free of objects of interest, whether they are cells or histological samples, to perform further analysis, such as segmentation or mosaicing. Commonly, this image consists of an empty field acquired in advance. However, many times achieving an empty field could not be feasible. Or else, this could be different from the background region of the sample really being studied, because of the interaction with the organic matter. At last, it could be expensive, for instance in case of live cell analyses. We propose a non parametric and general purpose approach where the background is built automatically stemming from a sequence of images containing even objects of interest. The amount of area, in each image, free of objects just affects the overall speed to obtain the background. Experiments with different kinds of microscopic images prove the effectiveness of our approach.

Keywords: Microscopy, flat field correction, background estimation, image segmentation.

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Authors: Mario Mastriani, Alberto E. Giraldez

Abstract:

In this paper, a new probability density function (pdf) is proposed to model the statistics of wavelet coefficients, and a simple Kalman-s filter is derived from the new pdf using Bayesian estimation theory. Specifically, we decompose the speckled image into wavelet subbands, we apply the Kalman-s filter to the high subbands, and reconstruct a despeckled image from the modified detail coefficients. Experimental results demonstrate that our method compares favorably to several other despeckling methods on test synthetic aperture radar (SAR) images.

Keywords: Kalman's filter, shrinkage, speckle, wavelets.

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