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Glaucoma is a neurodegenerative disease presents with retinal nerve fiber layer (RNFL) defects. We apply machine learning classifiers on the color information of the RNFL to differentiate between intact RNFL (i-RNFL) and RNFL defect (d-RNFL) on optic disc photographs (DPs). DPs from individuals with and without glaucoma were collected. Then, a semi-circle was automatically marked on the DPs, to label i-RNFL versus d-RNFL. RGB intensities and other color spaces of two profiles were collected. Five-fold cross validation is used to compare classification efficiency of five classifiers. A total of 2,051 profiles from 89, 32 and 15 DPs from patients with glaucoma, glaucoma suspects and control subjects were collected. There were 702 and 175 points of d-RNFL and 940 and 234 of i-RNFL in the training and test sets. In the training set, the 3 best classifiers using RGB intensities were fine Gaussian support vector machine (SVM), medium k-Nearest Neighbor and ensemble RUSBoosted Trees, with accuracies of 81.8%, 79.4% and 79.2%. The performance of the fine Gaussian SVM was similar between RGB and other color spaces. In the test set, the highest sensitivity (71.4%) and specificity (88.5%) were archived using RGB and the combination of RGB and Cb and Cr.

Speckle noise is one of the major artefacts in ultrasound images. The denoising faces the trade-off between noise suppression and structural preservation. In this study, multiscale adaptive regularisation Savitzky-Golay (MARSG) method, the new filter for removing speckle noise, is proposed. The proposed method combines the benefit of the multiscale analysis and the outstanding noise removing capability of Savitzky-Golay (SG) filter. The Laplacian pyramid is employed to separate an image into the noise, texture and object layers. Adaptive regularisation Savitzky-Golay (ARSG) filter is developed as the denoising filter in the noise and the texture layers. The denoising of the ARSG filter is adaptively adjusted in order to preserve the edges of objects in the image. The experiments on the synthetic and ultrasound images demonstrated that MARSG method offered better balance between noise removal and structural preservation than non-linear multiscale wavelet diffusion, feature-enhanced speckle reduction and regularised SG filter.

Background. In Thailand, Japanese encephalitis appears with a higher incidence of the Northern region (0.0/100,000 cases/year) as compared to the central southern region of the country (0.01/100,000/year). Also, all Japanese encephalitis virus (JEV) recently isolated strains in country belong to the genotype I and III.Methods. In order to evaluate the long-term efficacy of the newly developed live attenuated SA 14-14-2 Japanese encephalitis vaccine in Thailand, the immune response in children was studied after a second immunization campaign. Following 2012 immunization campaign, a second JEV immunization vaccine was delivered to children among four provinces of the Northern Region of Thailand. For each province, the recipients were identified accordingly to the two vaccine campaigns time including the children who received the second dose of vaccine: 1/ less than one year before the present study (N=30 by province); 2/ more than one year before the present study (N=70 by province). Altogether, a total of 400 children were enrolled in this project. After receiving the second vaccine dose, blood samples were collected and tested for JEV (Genotypes I and III) neutralizing antibodies following a standard procedure of LLC-MK2 infected cells.Results. All recipient presented neutralizing antibodies cross reacting against the prevalent JEV genotypes I (SM1 JEV strain) and genotype III (SA 14-14-2 and Beijing JEV strains). Most of the children from the first group had a seroconversion rate of 94.2% against homologous (i.e. GIII). Among them, an optimal rate of 100% seroconversion was find at the Phayao Province against both heterologous (GI) and homologous (GIII) JEV strains. Geometric Mean Titer (GMT) of neutralizing antibody against SM1, SA 14-14-2 JEV, and Beijing JEV strains were consistently high, respectively as 239±50, 323±201.2 and 177±36.7. For the second group, seroconversion rate was lower against SM1, SA 14-14-2 and Beijing JEV strains with a rate of 94.3, 90% and 86.8% with respectively a GMT of 154±32, 90±23 and 82±13.6.Conclusion. Although, the immunity appears to decrease within the period of 2.5 years, neutralizing antibody rate are consistent to protect against the JEV infection. Nevertheless, a long-term follow up is suitable to evaluate an eventual third dose opportunity.

Broiler is among Thai major industries. Red blood cell (RBC) count is one of the basic health screening protocols. The counting is often done manually. The mammalian blood analyzer cannot be used, because the shapes of mammalia and avian RBCs are different. RBC's color also varies among staining methods. Furthermore, RBCs can be grouped together. In this paper, we propose the automatic method for counting avian RBC. Otsu's multiple thresholding is used to extract the nucleus of RBC. The problems of the varying color of and the conglomerated RBC are tackled by applying iterative process. Only one parameter needs to be set manually. The experiment on five different staining types demonstrated that our proposed method provided less than 5% counting error for the staining that did not have the dark RBC's boundary.

The chicken industry ranks tenth in the world in term of output in the world meat market. Red blood cell (RBC) count is one of the basic health screening protocol required in an exported meat industry. However, the human’s automated blood analyzer cannot be applied to avian RBC, because the shape of avian blood (ellipse) is different from the mammal one (circle). In this paper, we propose 2-stage thresholding technique for automatic avian RBC counting. First, the blood smear slide is binarized into RBC and non-RBC area by applying Otsu thresholding. Then, image morphology and Otsu thresholding are reapplied to detect the blood nucleus. After that, the connected component analysis is applied to count the number of RBC. The experiment demonstrated that the proposed technique was simple and provided the count with error rate (2.23%) much less than the clinically acceptable value (5%).

Red blood cell counting is one of the most requested blood tests and can be effectively done by the dedicated equipment. In this paper, we proposed the two-stage circular Hough transform as the alternative automatic method for canine red blood cell counting. The possible blood cell area in the input slide image is first extracted. In the first stage, the Circular Hough transform with low sensitivity is applied to detect the non-overlapped cells. The detected cells are removed. The Circular Hough transform with high sensitivity is re-applied to the remaining region in order to detect the overlapped cells. The proposed method can be easily implemented into personal computers. The proposed algorithm was tested with many slides of canine red blood cell. It provided the counting with the average error of less than 5% with the best result of 1.439% error.

The demand of the fast acquisition in 3D ultrasound (3D-US) imaging leads to the dense population of speckle noise which causes the difficulty in medical diagnosis. In this Letter, 3Dadaptive regularisation Savitzky–Golay (3D ARSG) filter is proposed as the real-time filter for removing speckle noise in 3D-US imaging. The 3D ARSG filter uses the local intensity homogeneity to classify between noisy and texture regions. The intensity in the noisy regions is heavily smoothed, while the resolvable edges remain sharp. The experiment on the denoising of the 3D simulated models and the 3D-US images indicated that 3D ARSG filter provided the image with far less noise and crisper objects’ boundary than conventional methods. In addition, the proposed filter preserves the structural details with regard to the dimension and can denoise images in real time.

One of the fundamental noise in Computed Tomography (CT) imaging is metal artifact. The artifact leads to the information loss. Magnetic Resonance (MR) imaging is not affected by this artifact. In this paper, we propose using the information from MR images to reproduces the information lost due to dental filling in CT images. First, we register the CT and MR images of the same patient. Then, we classify the skull into 3 regions: teeth, tissue near the teeth and others. For teeth and tissue near the teeth, we develop the mapping function between the intensity in the MR image to the mean intensity in the CT image. The pixels corrupted by the metal artifact are detected and their intensity are changed according to the mapping function with its MR intensity as the input. The proposed method significantly removed metal artifacts and could be used to improve the diagnosis and planning accuracy.

The purpose of the study was to design a hybrid decision support system (HDSS) that could simulate the embolized coil selection pattern of the radiologists in aneurysms treatment. As the longest available length of the coils should be used in most cases, therefore only the shape diameter (SD) selection was modeled and varied. Ninety-eight aneurysms successfully treated by a radiologist with coil embolization were divided into two groups (86 for training and 12 randomly selected for validating). Eight aneurysms treated by another radiologist were also used to cross validate the proposed HDSS. The HDSS was developed using the classification and the linear regression methods (LRM). The dome and the width of an aneurysm were used as the system inputs. The system outputs were the SDs of the first three coils indexed according to the insertion order. The HDSS that consisted of Bagging classification and LRM achieved the highest accuracy for all cases. The errors were within 1 mm for the SD selection of the first two coils. For the third coil, the SD selection within 1 mm bound had 80 % accuracy. The experimental results indicated the feasibility of using the HDSS as the guidance for selecting the SDs of the first two coils. The selection of the third coil required more training data for the rarely used SD. Moreover, the cross validation with another radiologist showed the feasibility of using the proposed HDSS as the guidance, however further validation with more data is recommended.

Directional Local Mean Difference Level Set method (DLMD-LS) is the segmentation method for a urinary bladder in an MR sequence used for planning the treatment of a cervical cancer by radiation. The blurred boundary of a bladder is segmented based on the judgment of a radiologist and can be differed among radiologists. In this paper, DLMD-LS with Reinforcement Learning (RL) is proposed. It is an interactive system, where the parameter is adjusted to reflect the individual judgment. The weighted average method is used to update the distance that the boundary will be expanded, after the level set contour finishes evolving. The experiment on 30 MR slices demonstrated that DLMD-LS with RL had high segmentation accuracy and was adaptable to the new radiologist. It was also robust to outliers.

Japanese archaeologists have paid special attention to ancient Chinese bronze mirrors because the mirrors may provide a key for the exact location of Yamatai State, which is one of the major archaeological controversies. Currently, archaeologists visually analyse ancient Chinese bronze mirrors for their shape difference. The practice requires a huge amount of time and effort. In this paper, we propose an automatic method for detecting the shape difference between a pair of ancient mirrors. The 3D data of the mirrors are obtained using a laser range scanner. Our algorithm then aligns them into the same coordinate and visualizes their shape differences. Our proposed algorithm provides fast and non-damaging analysis for shape difference. Further analysis can be evaluated on our data instead of the actual mirror, so it can be performed by more than one group of archaeologists. Copyright © 2003 John Wiley & Sons, Ltd.

Region growing is a widely used image-segmentation techniques. However, oversegmentation and dependency on the starting point (seed) pose difficulties. We propose au algorithm using the robust homogeneous criteria consisting of (1) intensity and (2) no edge in-between criterion. The region is grown into pixels that satisfy both criteria. The intensity criterion ensures that no abrupt. change occurs in the image intensity inside a region. It is calculated based on the local average image intensity after rejecting outliers. The no edge in-between criterion prevents the growing across the boundaries. The segmentation algorithm consists of a (1) coarse and a (2) fine scaled segmentation. In the first stage, large regions are segmented, which include both uniform regions and regions containing a small area of intensity fluctuation. In the second stage, the remaining regions are segmented. Our experiments showed good segmentation results for both gray-scale and color images.

In this paper, we propose the directional local mean difference level set method (DLMD-LS). Our work is focused on the segmentation for a urinary bladder lumen in a T2 weighted image taken during brachytherapy. The boundary is detected as the region where the intensity means of the areas inside and outside the zero-level contour (edge) are high. The contour is controlled such that it stops evolving only at the boundary of the bright object inside the dark surrounding. Since the intensity mean is used, the DLMD-LS is more tolerant to noise than the conventional level set methods which detect the boundary as the part with large intensity gradient. Furthermore, the mean is calculated locally; therefore, the function is less affected to the different intensity distribution inside the lumen. The experiment demonstrates the superior noise tolerance and better capability to capture the blurred boundary of our proposed DLMD-LS to the directional level set method and the level set which detects the boundary according to the intensity distribution of the entire lumen.

Impulsive noise in a compressed measurement signal can be detected by the energy distribution of the reconstructed sparse signal. In this paper, the fast preprocessing for impulsive noise rejection for a compressed measurement image signal is proposed. An image is sparsified by octave-tree wavelet transform with db8 as the mother wavelet. The impulsive noise is detected via the change in the ratio of the energy leaking out of the third level subband to the total energy of the sparse signal. Since the information in the image is redundant, the number of the noisy element is estimated within +g of the actual value. g is defined in the unit of the percent of the size of the compressed measurement signal. The experiment shows that the proposed method effectively rejected noise with large magnitude. It provided the comparable performance to the method that rejected the exact number of noisy elements but required less computational time.

This paper proposes a formulation of estimating a sparse image signal where the sparsity only occurs on the high frequency components. The formulation is for compressed sensing reconstruction when a compressed measurement signal is contaminated by impulsive noise. The approach is based on two prior well-known assumptions. First, adding an L 1 -norm penalty on a compressible signal to the cost objective of the estimation problem will promote a sparsity in the signal. Secondly, an image signal should be sparse in high frequency domain, while it is dense in low frequency domain where most of its important information lies. Our approach is therefore to impose an L 1 -norm penalty only on the signal components that need to be sparse while we allow the important low frequency part to be dense. The obtained formulation is a convex optimization which can be effectively solved by many available algorithms. The experiment demonstrated a superior impulsive noise tolerance of the proposed penalty function to a conventional scheme where all signal components are penalized by L 1 -norm. At the noise probability of less than 0.1, the reconstruction was better both quantitatively and qualitatively.

Impulsive noise in a compressed measurement signal, y, has a significant effect on the reconstruction in Compressed Sensing (CS). In this paper, Greedy gap's Boundary Finder (GBF), a fast preprocessing for impulsive noise rejection for the CS reconstruction of image signal, is proposed. An image is sparsified by the octave-tree wavelet transform. GBF adopts an idea that the leakage energy out of the third-level (L 3 ) subband in the reconstructed sparse signal is the result of either the impulsive noise in y or the insufficient information (of y) for reconstruction. The leakage energy is measured as the ratio to the total energy. In a graph of relationship between the energy ratio and the number of the removed elements, there is a gap of low energy ratio in the middle. A binary search is adapted in GBF to find the lower gap's boundary which is the minimum number of removed elements with low energy ratio. As the image signal is highly redundant, the search in GBF can be non-exact and will be stopped after the number is within +g of the lower boundary, where g is the gap's resolution and defined as the percent of the size of y. GBF was evaluated on 100 16×16 image blocks and 10 256×256 standard test images. The evaluation shows that at the optimal g of 5%, GBF provided the comparable performance to the exact boundary's search, but consumed less computational time.

A reconstruction method using the ensemble of compressed measurement signals is proposed for reconstructing the image from the signal corrupted by Gaussian noise. The ensemble is created from one signal under the assumption that an image is highly redundant; hence, it is approximated as the mixture of a number of signals. The proposed method adopted the sampling with replacement in bootstrapping to extract L signals from the mixture. The extracted L signals from the ensemble of signals corrupted by Gaussian noise with the same mean and variance. The signals have different bases; thus, they he in different space. Orthogonal Matching Pursuit with Partially Known Support (OMP-PKS) is applied to reconstruct the L signals to the same sparse space. Gaussian noise is reduced by averaging the reconstructed signals. The performance of the proposed method was compared with Basis Pursuit Denoising (BPDN), OMP-PKS and Distributed Compressed Sensing using Simultaneously Orthogonal Matching Pursuit (DCS-SOMP). The experimental results of 10 standard test images showed that our method yielded higher Peak Signal-to-Noise Ratio (PSNR) and better visual quality at a high level of noise.

The Lorentzian norm of robust statistics is often applied in the reconstruction of the sparse signal from a compressed measurement signal in an impulsive noise environment. The optimization of the robust statistic function is iterative and usually requires complex parameter adjustments. In this article, the impulsive noise rejection for the compressed measurement signal with the design for image reconstruction is proposed. It is used as the preprocessing for any compressed sensing reconstruction given that the sparsified version of the signal is obtained by utilizing octave-tree discrete wavelet transform with db8 as the mother wavelet. The presence of impulsive noise is detected from the energy distribution of the reconstructed sparse signal. After the noise removal, the noise corrupted coefficients are estimated. The proposed method requires neither complex optimization nor complex parameter adjustments. The performance of the proposed method was evaluated on 60 images. The experimental results indicated that the proposed method effectively rejected the impulsive noise. Furthermore, at the same impulsive noise corruption level, the reconstruction with the proposed method as the preprocessing required much lower measurement rate than the model-based Lorentzian iterative hard thresholding.

The compressed signal in compressed sensing (CS) may be corrupted by noise during transmission. The effect of Gaussian noise can be reduced by averaging, hence a robust reconstruction method using compressed signal ensemble from one compressed signal is proposed. The compressed signal is subsampled for L times to create the ensemble of L compressed signals. Orthogonal matching pursuit with partially known support (OMP-PKS) is applied to each signal in the ensemble to reconstruct L noisy outputs. The L noisy outputs are then averaged for denoising. The proposed method in this article is designed for CS reconstruction of image signal. The performance of our proposed method was compared with basis pursuit denoising, Lorentzian-based iterative hard thresholding, OMP-PKS and distributed compressed sensing using simultaneously orthogonal matching pursuit. The experimental results of 42 standard test images showed that our proposed method yielded higher peak signal-to-noise ratio at low measurement rate and better visual quality in all cases.

In image stitching, parallax is key challenge that leads to inaccurate registration and ghosting effect of objects in the result. A novel foreground rejection method is proposed in this paper to remove parallax in video sequence. Firstly, the global motion is estimated between two frames using SIFT feature matching. The logical OR operators are then applied to the pixels that have high displaced frame difference for foreground extraction. There are two sets of foreground calculated from the near frames and far frames. Voting scheme is applied in the way that only the near-frame foreground appears inside the area of far-frame foreground that is considered as the actual foreground. The extracted foreground at this stage is mostly the edges of objects. Therefore, the foreground rejection method proposed in this paper aims to provide better motion vector estimation and ghosting effect reduction. The removal of the major parts of the high difference foreground is sufficient; hence the simple two-stage rendering is applied. The implemented results indicate that more accurate registration is achieved by including our proposed foreground rejection method. Moreover, the background of the stitched video sequence does not have the visible ghosting effect.

In video frame rate conversion, unavailable frames are reconstructed from existing frames based on interpolated motion vectors. Due to an error in estimating motion vectors and an occlusion effect, it is possible that intensity information is not provided to every pixel in the reconstructed frame. Pixels whose intensity is missing become holes in the reconstructed frame. This paper proposes the reconstruction without distinct holes by double motion vector estimation. Motion is described by affine model instead of the conventional 2D translation model. The proposed method applies block based motion estimation. It is divided into 2 stages: frame reconstruction and hole filling. In the reconstruction stage, a new frame is reconstructed by moving blocks in previous frames according to their interpolated motion vectors. Holes in the newly reconstructed frame are then filled with the corresponding pixels in the subsequent frame. The proposed method searches for the corresponding pixels by looking for the region in the subsequent frames that are similar to the surrounding region of each hole. The computation cost in the proposed method is low because the motion estimation is simple and applied only twice. It does not use any complex processes to fill holes. Experiments on three standard video sequences showed that our proposed method reconstructed frames in complex video sequence without noticeable holes.

Due to large data set, block processing is usually applied for fast compressive sensing (CS) reconstruction; however, it gives the undesired blocking artifact in reconstructed data. In order to reduce blocking artifact and preserve high frequency, this paper proposes a novel block processing on wavelet domain instead of spatial domain. No post-processing nor special mapping is included. CS is applied only on blocks where the data are really sparse. An enhancing process, often included for artifact reduction, is no longer necessary. Our algorithm was evaluated by reconstructing three standard images (Lena, Mandrill and Peppers) and then compared with scrambled block hadamard ensemble (SBHE) and block-based CS sampling with a smoothed PL variant using directional discrete wavelet transform (BCS-SPL-DDWT). In the experiment, it provided better reconstruction both objectively (PSNR) and subjectively at low measurement rate. It gave the sharpest image in all cases. Details were preserved and blocking artifact was not detectable.

Traditionally, the problems of applying Orthogonal Matching Pursuit (OMP) to large images are its high computing time and its requirement for a large matrix. In this paper, we propose a fast image recovery algorithm by dividing the image into block of n×n pixels and applying OMP to each n×n block instead of the entire image. The key idea is that small matrix requires less computing time and less memory. In the experiment, the block based OMP was applied to three standard test images: Lena, Mandrill and Pirate. Compared to standard OMP, block based OMP required less computing time while giving comparable PSNR.

The quality of the frame rate up-conversion depends on the accuracy of the motion parameters. 2D affine model describes the motion better than the popular 2D translation model; however, its parameters are difficult to interpolate. In this paper, the approximation method for interpolating the mid-point of 2D affine parameters is proposed. The affine transform is approximated as the 2D rigid transformation where the target x and y axes are not orthogonal. Trigonometry relationship is applied to interpolate rotational parameters. Conventional linear interpolation is applied to interpolate translational parameters. For video sequences containing complex motion, the reconstruction from the approximated affine parameters gave higher PSNR and visually better result than the one from the interpolated 2D translation parameters.

Most of segmentation algorithms are based on the assumption of intensity homogeneity within an object. However, in many applications, the object of interest contains more than one homogenous-intensity region. Even when the object's shape is known, such object is not effectively extracted. In this paper, we propose a segmentation process for the objects containing 2 homogenous regions. Our method is based on level set method. This method is divided into 2 parts. In the first part, we estimate the pose parameters of the mean shape of the object. In the latter part, the region inside the curve is adjusted to form a homogenous region. Then we segment the adjusted image for the object by level set method. Our experiment on medical images indicated that our method effectively segmented object with one and two homogenous regions.

Most segmentation algorithms are based on the assumption of intensity homogeneity within an object. However, in many applications, the object of interest contains more than one homogenous region. Even when the object’s shape is known, such object is not effectively extracted. In this paper, we propose a segmentation process for the objects containing 2 homogenous regions. Our method is based on the level set method. We construct the shape model from the set of manually extracted objects. The parameters that represent the shape model are coefficients of PCA basis. Instead of defining a new cost-function based on heterogeneity assumption, we repeatedly form a homogenous region inside the evolving curve and evolve the curve by the level set method. Our experiment on medical images indicated that our method effectively segmented object with one and two homogenous regions.

In this paper, we propose a home video summarization by representing a group of Representative frames (R-frames). The number of R-frames depends on shot characteristics which is shot duration and shot motion activity. For each shot, we apply an adaptive sub-sampling algorithm to extract the R-frames that contain only the high frame difference. When events of shots are not related to each other (One tape contains many events), it is desired to retrieve more information from more shots. Our algorithm allows users to select the number of shots appearing in the summarized video which give an optional way to understand the original sequence. In our experiments, we summarize video into variable number of shot appearing in the summary and evaluate the summary by users's subjective evaluations. We also summarize the home video by user's feedback. The user is asked to select the extracted R-frames as a training data. We apply the Support Vector Machine algorithm (SVM) to train and classify. The result from user's feedback shows that SVM retrieves the frames according to the user's desired.

A novel image improving algorithm for compressed image sequence by blending with reference images is presented. The reference image is defined as the high quality still image of the same scene. Compressed images are improved by blending the data directly with the reference image in both DCT and spatial domain. Amount of blending is controlled by the resemblance between the reference and the compressed image. Experiments conducted on the sequence of H.263 compressed image is presented. Prior information about compression technique is not required so this technique should be able to apply to other techniques as well.