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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.

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.

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.