Mini Kirsch Edge Detection and Its Sharpening Effect

In computer vision, edge detection is a crucial step in identifying the objects’ boundaries in an image. The existing edge detection methods function in either spatial domain or frequency domain, fail to outline the high continuity boundaries of the objects. In this work, we modified four-directional mini Kirsch edge detection kernels which enable full directional edge detection. We also introduced the novel involvement of the proposed method in image sharpening by adding the resulting edge map onto the original input image to enhance the edge details in the image. From the edge detection performance tests, our proposed method acquired the highest true edge pixels and true non-edge pixels detection, yielding the highest accuracy among all the comparing methods. Moreover, the sharpening effect offered by our proposed framework could achieve a more favorable visual appearance with a competitive score of peak signal-to-noise ratio and structural similarity index value compared to the most widely used unsharp masking and Laplacian of Gaussian sharpening methods.  The edges of the sharpened image are further enhanced could potentially contribute to better boundary tracking and higher segmentation accuracy

K-means Clustering In Knee Cartilage Classification: Data from the OAI

Knee osteoarthritis is a degenerative joint disease which affects people mostly from elderly population. Knee cartilage segmentation is still a driving force in managing early symptoms of knee pain and its consequences of physical disability. However, manual delineation of the tissue of interest by single trained operator is very time consuming. This project utilized a fully-automated segmentation that combined a series of image processing methods to process sagittal knee images. MRI scans undergo Bi-Bezier curve contrast enhancement which increase the distinctiveness of cartilage tissue. Bone-cartilage complex is extracted with dilation of mask resulted from region growing at distal femoral bone. Later, the processed image is clustered with k = 2, into two groups, including coarse cartilage group and background. The thin layer of cartilage is successfully clustered with satisfactory accuracy of 0.987±0.004, sensitivity 0.685±0.065 of and specificity of 0.994±0.004. The results obtained are promising and potentially replace the manual labelling process of training set in convolutional neural network model

Brain-computer interface algorithm based on wavelet-phase stability analysis in motor imagery experiment

Severe movement or motor disability diseases such as amyotrophic lateral sclerosis (ALS), cerebral palsy (CB), and muscular dystrophy (MD) are types of diseases which lead to the total of function loss of body parts, usually limbs. Patient with an extreme motor impairment might suffers a lockedin state, resulting in the difficulty to perform any physical movements. These diseases are commonly being treated by a specific rehabilitation procedure with prescribed medication. However, the recovery process is time-consuming through such treatments. To overcome these issues, Brain- Computer Interface system is introduced in which one of its modalities is to translate thought via electroencephalography (EEG) signals by the user and generating desired output directly to an external artificial control device or human augmentation. Here, phase synchronization is implemented to complement the BCI system by analyzing the phase stability between two input signals. The motor imagery-based experiment involved ten healthy subjects aged from 24 to 30 years old with balanced numbers between male and female. Two aforementioned input signals are the respective reference data and the real time data were measured by using phase stability technique by indicating values range from 0 (least stable) to 1 (most stable). Prior to that, feature extraction was utilized by applying continuous wavelet transform (CWT) to quantify significant features on the basis of motor imagery experiment which are right and left imaginations. The technique was able to segregate different classes of motor imagery task based on classification accuracy. This study affirmed the approach’s ability to achieve high accuracy output measurements

Formulation of a novel HRV classification model as a surrogate fraudulence detection schema

Lie detection has been studied since a few decades ago, usually for the purpose of producing a scheme to assist in the investigation of identifying the culprit from a list of suspects. Heart Rate Variability (HRV) may be used as a method in lie detection due to its versatility and suitability. However, since its analysis is not instantaneous, a new experiment is described in this paper to overcome the problem. Additionally, a preliminary HRV classification model is designed to further enhance the classification model which is able to distinguish the lie from the truth for up to 80%

Prominent region of interest contrast enhancement for knee MR images: data from the OAI

Osteoarthritis is the most commonly seen arthritis, where there are 30.8 million adults affected in 2015. Magnetic resonance imaging (MRI) plays a key role to provide direct visualization and quantitative measurement on knee cartilage to monitor the osteoarthritis progression. However, the visual quality of MRI data can be influenced by poor background luminance, complex human knee anatomy, and indistinctive tissue contrast. Typical histogram equalisation methods are proven to be irrelevant in processing the biomedical images due to their steep cumulative density function (CDF) mapping curve which could result in severe washout and distortion on subject details. In this paper, the prominent region of interest contrast enhancement method (PROICE) is proposed to separate the original histogram of a 16-bit biomedical image into two Gaussians that cover dark pixels region and bright pixels region respectively. After obtaining the mean of the brighter region, where our ROI – knee cartilage falls, the mean becomes a break point to process two Bezier transform curves separately. The Bezier curves are then combined to replace the typical CDF curve to equalize the original histogram. The enhanced image preserves knee feature as well as region of interest (ROI) mean brightness. The image enhancement performance tests show that PROICE has achieved the highest peak signal-to-noise ratio (PSNR=24.747±1.315dB), lowest absolute mean brightness error (AMBE=0.020±0.007) and notably structural similarity index (SSIM=0.935±0.019). In other words, PROICE has considerably outperformed the other approaches in terms of its noise reduction, perceived image quality, its precision and has shown great potential to visually assist physicians in their diagnosis and decision-making process

Enhanced human knee cartilage evaluation using reduced interactive segmentation model

The purpose of this research is to design an enhanced human knee cartilage evaluation framework to detect cartilage thinning in the early Osteoarthritis (OA) disease. The existing research drawbacks include the absence of contrast enhancement model merely on region of interest, the low efficiency and tedious labelling processes in interactive segmentation model, and the lacking of a quantitative assessment in the segmentation model. In this research, we propose a quantitative assessment framework which consists of three phases: Phase 1 focuses on developing an explicit contrast enhancement model for knee images; Phase 2 focuses on developing a reduced interactive cartilage segmentation tool; Phase 3 focuses on formulating a cartilage quantitative measurement. The knee images tested in this research are provided by Osteoarthritis Initiative, given that the sample sizes used were 120, 30 and 20 slices in Phase 1, Phase 2 and Phase 3, respectively. The proposed Prominent Region of Interest Contrast Enhancement (PROICE) method outperformed in diverging the dynamic range of intensity distributed by the region of interest, resulting in noticeable distinctiveness between cartilages and unwanted background tissues. Compared with other existing enhancement methods, PROICE achieved the highest peak signal-to-noise ratio score of 23.80±1.16dB, structural similarity index of 0.86±0.02, low absolute mean error score of 3.88±2.92, and adequate enhancement measure of 17.47±0.74. It was then extended to Enhanced Approximate Non-Cartilage Labels (EANCAL) for the extraction of portions that contained critical information through an entropy filter. This research contributed to reduce human attention level in manual annotations, eventually increased the segmentation efficiency. The modified segmentation framework showed a significant reduction in the mean processing time to 45±4s, which was averaged of 80.25% and 82.25% shorter than manual segmentation for healthy knee cartilage segmentation and diseased knee cartilage segmentation respectively, that performed by two trained operators. In addition, EANCAL obtained an adequate inter-operator reliability score in healthy femoral cartilage (FC) and tibial cartilage (TC) (FC: 0.920±0.046;TC:0.912±0.044). Meanwhile, EANCAL remained competitive compared to the ANCAL method yet with fewer human attention level required, recorded with the highest intra-operator reproducibility score of 0.820±0.074 for operator 1; and 0.833±0.056 for operator 2. The cartilage segmentations were then evaluated with Regional Cartilage Normal thickness approximation (RCN-ta). The quantitative assessment model was validated with FDA-cleared DICOM software, revealed an acceptable error range of 0.135-0.214 mm. The inter-class correlation score and Pearson correlation obtained were ICC>0.94 and r>0.90, respectively. In a nutshell, the PROICE-enhanced images successfully overcome the background seed allocation issue and improved the segmentation model efficiency and segmentation reproducibility, thus yielding a promising cartilage quantitative assessment framework, which potentially assist the clinicians in diagnosis and treatment decision-making process

Mini Kirsch edge detection and its sharpening effect

In computer vision, edge detection is a crucial step in identifying the objects’ boundaries in an image. The existing edge detection methods function in either spatial domain or frequency domain, fail to outline the high continuity boundaries of the objects. In this work, we modified four-directional mini Kirsch edge detection kernels which enable full directional edge detection. We also introduced the novel involvement of the proposed method in image sharpening by adding the resulting edge map onto the original input image to enhance the edge details in the image. From the edge detection performance tests, our proposed method acquired the highest true edge pixels and true non-edge pixels detection, yielding the highest accuracy among all the comparing methods. Moreover, the sharpening effect offered by our proposed framework could achieve a more favorable visual appearance with a competitive score of peak signal-to-noise ratio and structural similarity index value compared to the most widely used unsharp masking and Laplacian of Gaussian sharpening methods. The edges of the sharpened image are further enhanced could potentially contribute to better boundary tracking and higher segmentation accuracy

K-means clustering in knee cartilage classification: Data from the OAI

Knee osteoarthritis is a degenerative joint disease which affects people mostly from elderly population. Knee cartilage segmentation is still a driving force in managing early symptoms of knee pain and its consequences of physical disability. However, manual delineation of the tissue of interest by single trained operator is very time consuming. This project utilized a fully-automated segmentation that combined a series of image processing methods to process sagittal knee images. MRI scans undergo Bi-Bezier curve contrast enhancement which increase the distinctiveness of cartilage tissue. Bone-cartilage complex is extracted with dilation of mask resulted from region growing at distal femoral bone. Later, the processed image is clustered with k = 2, into two groups, including coarse cartilage group and background. The thin layer of cartilage is successfully clustered with satisfactory accuracy of 0.987±0.004, sensitivity 0.685±0.065 of and specificity of 0.994±0.004. The results obtained are promising and potentially replace the manual labelling process of training set in convolutional neural network model

Bacterial disinfection and cell assessment post ultraviolet-C LED exposure for wound treatment

Ultraviolet-C sourced LED (UVC-LED) has been widely used for disinfection purposes due to its germicidal spectrum. In this study, the efficiencies of UVC-LED for Pseudomonas aeruginosa (P. aeruginosa) and Staphylococcus aureus (S. aureus) disinfections were investigated at three exposure distances (1, 1.5, and 2 cm) and two exposure times (30 and 60 s). The respective bacterial inhibition zones were measured, followed by a morphological analysis under SEM. The viabilities of human skin fibroblast cells were further evaluated under the treatment of UVC-LED with the adoption of aforesaid exposure parameters. The inhibition zones were increased with the increment of exposure distances and times. The highest records of 5.40 ± 0.10 cm P. aeruginosa inhibition and 5.43 ± 0.11 cm S. aureus inhibition were observed at the UVC-LED distance of 2 cm and 60-s exposure. Bacterial physical damage with debris formation and reduction in size were visualized following the UVC-LED exposures. The cell viability percentages were in a range of 75.20–99.00% and 82–100.00% for the 30- and 60-s exposures, respectively. Thus, UVC-LED with 275-nm wavelength is capable in providing bacterial disinfection while maintaining accountable cell viability which is suitable to be adopted in wound treatment.

Magnetic resonance imaging-based estimation of knee cartilage thickness with MATLAB

Detection of early knee osteoarthritis remains a driving force in the search for more promising quantitative assessment approaches. Apart from other conventional methods such as radiography, computed tomography, and sonography, magnetic resonance imaging has become more widely available and has made it essential to visualize the knee's entire anatomy. Biomarkers such as joint space narrowing, articular cartilage thickness, cartilage volume, cartilage surface curvature, lesion depth, and others are used to determine disease progression in non-invasive manner. In this research, a regional cartilage normal thickness approximation (RCN-ta) model was developed with MATLAB to enable rapid cartilage thickness assessment with a simple click. The model formulated was compared to the FDA-cleared software measurements. A reasonable range of 0.135-0.214 mm of root-mean-square error may be predicted from the model. With a high ICC > 0.975, the model was highly accurate and reproducible. A good agreement between the proposed model and the medically used software can be found with a high Pearson correlation of r > 0.90