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Amplified spontaneous emission (ASE) noise, induced by an erbium-doped fiber amplifier (EDFA), is always a key problem for the measurement accuracy in a cavity ring-down (CRD) fiber amplified loop gas sensing system. A digital least-mean-square (LMS) adaptive filter is proposed to reduce the ASE noise in a CRD acetylene-sensing fiber loop for improving the measurement accuracy in terms of concentration. The simulation and experimental results show that the measurement accuracy in terms of concentration of acetylene could achieve about 15 ppm.

BACKGROUND Presence of microvascular invasion (MVI) indicates poorer prognosis post-curative resection of hepatocellular carcinoma (HCC), with an increased chance of tumour recurrence. By present standards, MVI can only be diagnosed post-operatively on histopathology. Texture analysis potentially allows identification of patients who are considered ‘high risk’ through analysis of pre-operative magnetic resonance imaging (MRI) studies. This will allow for better patient selection, improved individualised therapy (such as extended surgical margins or adjuvant therapy) and pre-operative prognostication. AIM This study aims to evaluate the accuracy of texture analysis on pre-operative MRI in predicting MVI in HCC. METHODS Retrospective review of patients with new cases of HCC who underwent hepatectomy between 2007 and 2015 was performed. Exclusion criteria: No pre-operative MRI, significant movement artefacts, loss-to-follow-up, ruptured HCCs, previous hepatectomy and adjuvant therapy. Fifty patients were divided into MVI ( n = 15) and non-MVI ( n = 35) groups based on tumour histology. Selected images of the tumour on post-contrast-enhanced T1-weighted MRI were analysed. Both qualitative (performed by radiologists) and quantitative data (performed by software) were obtained. Radiomics texture parameters were extracted based on the largest cross-sectional area of each tumor and analysed using MaZda software. Five separate methods were performed. Methods 1, 2 and 3 exclusively made use of features derived from arterial, portovenous and equilibrium phases respectively. Methods 4 and 5 made use of the comparatively significant features to attain optimal performance. RESULTS Method 5 achieved the highest accuracy of 87.8% with sensitivity of 73% and specificity of 94%. CONCLUSION Texture analysis of tumours on pre-operative MRI can predict presence of MVI in HCC with accuracies of up to 87.8% and can potentially impact clinical management.

This paper presents a termination criterion for active contour that does not involve alteration of the energy functional. The criterion is based on the area difference of the contour during evolution. In this criterion, the evolution of the contour terminates when the area difference fluctuates around a constant. The termination criterion is tested using parametric gradient vector flow active contour with contour resampling and normal force selection. The usefulness of the criterion is shown through its trend, speed, accuracy, shape insensitivity, and insensitivity to contour resampling. The metric used in the proposed criterion demonstrated a steadily decreasing trend. For automatic implementation in which different shapes need to be segmented, the proposed criterion demonstrated almost 50% and 60% total time reduction while achieving similar accuracy as compared with the pixel movement-based method in the segmentation of synthetic and real medical images, respectively. Our results also show that the proposed termination criterion is insensitive to shape variation and contour resampling. The criterion also possesses potential to be used for other kinds of snakes.

This paper presents a segmentation technique which utilizes atlas based centroid forces coupled with GradientVector Flow (GVF) parametric active contour for the segmentation of femoral cancellous bone. The atlas used in our study provides prior information to constraint contours at regions where edge based forces are missing and to initialize the active contours. GVF external force field is padded with the centroid force derived from the atlas. In our implementation, once the atlas is registered with the target image to be segmented, the segmentation process is fully automatic. Analysis of segmentation accuracy of twenty one slices at the intercondylar location of sagittal slices provides sensitivity of 97.4±1.9%; specificity of 99.6±0.1%; Dice similarity coefficient of 96.7±1.1%. From the inspection of external force fields and the accuracy results, the study suggests that the centroid force formulation is effective in approximating missing boundaries in GVF and in facilitating automatic initialization.

This paper presents a study that investigated the potential of texture analysis using Fluid Sensitive Fat Suppressed MRI images for the use in detection of bone marrow edema. A total of 168 slices of knee MRI from 10 subjects were involved. Six histogram-based textures (mean intensity, standard deviation, smoothness, third moment, uniformity and entropy) were calculated in both 2D and 3D, and were compared between healthy group and group affected by bone marrow edema. Two-sample t-tests were performed to assess the difference between healthy group and group affected by edema. The intensity third moment in 2D showed significant difference between the slices of healthy subjects and the slices affected by edema (p<0.05). Smoothness and standard deviation in 2D showed a modest significance between healthy and affected groups. No significant difference was found in the 3D textures of healthy group and group affected by edema.