Proposed Image Analysis System Research Articles

Automatic Fetal Middle Sagittal Plane Detection in Ultrasound Using Generative Adversarial Network.

Background and Objective: In the first trimester of pregnancy, fetal growth, and abnormalities can be assessed using the exact middle sagittal plane (MSP) of the fetus. However, the ultrasound (US) image quality and operator experience affect the accuracy. We present an automatic system that enables precise fetal MSP detection from three-dimensional (3D) US and provides an evaluation of its performance using a generative adversarial network (GAN) framework. Method: The neural network is designed as a filter and generates masks to obtain the MSP, learning the features and MSP location in 3D space. Using the proposed image analysis system, a seed point was obtained from 218 first-trimester fetal 3D US volumes using deep learning and the MSP was automatically extracted. Results: The experimental results reveal the feasibility and excellent performance of the proposed approach between the automatically and manually detected MSPs. There was no significant difference between the semi-automatic and automatic systems. Further, the inference time in the automatic system was up to two times faster than the semi-automatic approach. Conclusion: The proposed system offers precise fetal MSP measurements. Therefore, this automatic fetal MSP detection and measurement approach is anticipated to be useful clinically. The proposed system can also be applied to other relevant clinical fields in the future.

Automated image analysis system for renal filtration barrier integrity of potassium bromate treated adult male albino rat

Potassium bromate (KBrO3) is a potent nephrotoxic agent that leads to a significant decrease in the activities of renal antioxidant capacity, antioxidant loss and restoration of the renal dysfunction. Several measurements are used to examine the kidney status, including the base width of the foot, the slit pore diameter, and the glomerular basement membrane thickness of the kidney. In this work, morphometric analysis based on image processing is carried out to assess the filtration barrier integrity parameters, which indicates the degree of recovery against the nephrotoxic effect of the KBrO3 on the renal cortex of adult male albino rat and assesses the capability of the renal cortex to recover after its cessation. The morphometric methods based proposed image analysis system enabled the identification of the renal status of different groups, namely the control, potassium bromate affected, and the recovered groups, according to the variation of the measured parameters is a powerful tool. The proposed image analysis system provided a radical geometric morphometrics, which includes morphological operations and structuring element processes in order to identify the glomerular filtration barrier and the feet for further measurements in each case study. The results established that the average lengths of the feet in the histological microscopic images are 465.2397 nm, 278.189 nm, and 393.2347 nm for the control, KBrO3 affected rats and the recovered rats; respectively.

A new automatic algorithm to extract craniofacial measurements from fetal three‐dimensional volumes

Three-dimensional (3D) ultrasound is useful in the prenatal evaluation of fetal craniofacial structures, particularly as it provides a multiplanar view. However, an expert must designate the area of interest and the appropriate view, making measurement of fetal structures using 3D ultrasound both time-consuming and subjective. In this study we propose an image analysis system that measures automatically and precisely the fetal craniofacial structures and evaluate its performance in the second trimester of pregnancy using a new 3D volume analysis algorithm. A universal facial surface template model containing the geometric shape information of a fetal craniofacial structure was constructed from a fetal phantom. Using the proposed image analysis system we fitted this stored template model using a model deformation approach to individual fetal 3D facial volumes from 11 mid-trimester fetuses, and extracted automatically the following standard measurements: biparietal diameter (BPD), occipitofrontal diameter (OFD), interorbital diameter (IOD), bilateral orbital diameter (BOD) and distance between vertex and nasion (VN). The same five parameters were measured manually by an expert and the results compared. Comparison of the algorithm-based automatic measurements with manual measurements made by an expert gave correlation coefficients of 0.99 for BPD, 0.98 for OFD, 0.80 for BOD, 0.83 for IOD and 0.99 for VN. There were no significant differences between automatic and manual measurements. Our proposed system measures precisely the fetal craniofacial structures using 3D ultrasound, making it potentially useful for clinical service. This system could also be applied to other clinical fields in future testing.

High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles

BackgroundHigh content screening (HCS)-based image analysis is becoming an important and widely used research tool. Capitalizing this technology, ample cellular information can be extracted from the high content cellular images. In this study, an automated, reliable and quantitative cellular image analysis system developed in house has been employed to quantify the toxic responses of human H4 neuroglioma cells exposed to metal oxide nanoparticles. This system has been proved to be an essential tool in our study.ResultsThe cellular images of H4 neuroglioma cells exposed to different concentrations of CuO nanoparticles were sampled using IN Cell Analyzer 1000. A fully automated cellular image analysis system has been developed to perform the image analysis for cell viability. A multiple adaptive thresholding method was used to classify the pixels of the nuclei image into three classes: bright nuclei, dark nuclei, and background. During the development of our image analysis methodology, we have achieved the followings: (1) The Gaussian filtering with proper scale has been applied to the cellular images for generation of a local intensity maximum inside each nucleus; (2) a novel local intensity maxima detection method based on the gradient vector field has been established; and (3) a statistical model based splitting method was proposed to overcome the under segmentation problem. Computational results indicate that 95.9% nuclei can be detected and segmented correctly by the proposed image analysis system.ConclusionThe proposed automated image analysis system can effectively segment the images of human H4 neuroglioma cells exposed to CuO nanoparticles. The computational results confirmed our biological finding that human H4 neuroglioma cells had a dose-dependent toxic response to the insult of CuO nanoparticles.

Computer-based grading of haematoxylin-eosin stained tissue sections of urinary bladder carcinomas.

A computer-based image analysis system was developed for assessing the malignancy of urinary bladder carcinomas in a more objective manner. Tumours characterized in accordance with the WHO grading system were classified into low-risk (grades I and II) and high-risk (grades III and IV). Images from 92 haematoxylin-eosin stained sections of urinary bladder carcinomas were digitized and analysed. An adequate number of nuclei were segmented from each image for morphologic and textural analysis. Image segmentation was performed by an efficient algorithm, which used pattern recognition methods to automatically characterize image pixels as nucleus or background. Image classification into low-risk or high-risk tumours was performed by means of the quadratic non-linear Bayesian classifier, which was designed employing 36 textural and morphological features of the nucleus. Automatic segmentation of nuclei on all images was about 90% on average. Overall system accuracy in correctly classifying tumours into low-risk or high-risk was 88%, employing the leave-one-out method and the best combination of three textural and one morphological feature. Classification accuracy for low-risk tumours was 88.8% and for high-risk tumours 86.2%. The proposed image analysis system may be of value to the objective assessment of the malignancy of urine bladder carcinomas, since it relies on nuclear parameters that are employed in visual grading and their prognostic value has been proved.

Application of image analysis techniques to two-dimensional crossed immunoelectrophoresis study

Two-dimensional crossed immunoelectrophoresis (2D-CIEP) is a technique widely used for studying composition of protein mixtures in biological and clinical studies. A low cost image analysis system with the use of an optical flatbed scanner and a IBM-compatible PC was set up in this work for capturing 2D-CIEP patterns. A computer package CIEPEASY was developed for modification and analysis of the image acquired to determine various peak parameters such as the migration distance, peak height and peak area of the constituting components for both qualitative and quantitative studies. In this approach, more composition information of the standard and sample gel patterns could be extracted from the proposed image analysis system. The time required for data collection and interpretation of 2D-CIEP images was shortened significantly and the results obtained have a higher accuracy than those obtained by using conventional methods. Moreover, a linear relationship between the peak area and the amount of antigen present in a sample was confirmed accurately and reported for the first time in the literature.