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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.

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.

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.

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 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.

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.

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.