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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.

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.

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.

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.

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.