Search

The image processing of muscular fibers has seen significant improvements over the last decades, from light microscopy acquisition to virtual microscopy and from manual segmentation and fiber typing to automatic. The present study aims to discuss the main tools employed in different histo-enzymological image processing phases applied to muscle fibers, which are respectively: the acquisition, segmentation and fiber typing as well as their efficiency in the determination of morphometric parameters. First, the acquisition: to count the cell numbers within the muscle fiber sections, optical microscopic images with different magnifications (x100, x200, x250, and x400) were compared with virtual slides digitized by Slide Scanner. Second, the segmentation: Three softwares (Fiji "Digitizing pen, Mouse", Image Pro Plus 10 [semi-automatic], and Cytoinformatics LLC [automatic]) were compared for image segmentation quality and the determining of fiber numbers. Third, manual fiber typing: employing Fiji of segmented images using the 3 previously mentioned softwares were performed to calculate the accuracy of the morphometric parameter (Cross sectional Area [CSA], perimeter and Minimal Feret's Diameter [MFD]). The results of the acquisition showed that the scanner slides have a better resolution and detected a higher number of cells than when optical microscopes with different magnifications were used; with the latter, the processing can be performed only with an acceptable resolution, where the number of cells is lesser, which unfortunately requires several repetitions and exhausting work. Our findings regarding segmentation indicate that the Cytoinformatics LLC showed the best processing time and the highest quality followed by IP and Fiji. Finally and for the ultimate step, the morphometric parameter calculation: showed that the best accuracy was attained by using Fiji followed by Cytoinformatics and finally by IP. The findings of this study suggest that Fiji (semi-automatic) showed the best quality/price ratio (open access software) for segmentation and fiber typing, but it proved to be time consuming when compared to Image Pro Plus 10 (semi-automatic) and Cytoinformatics LLC (automatic). [ABSTRACT FROM AUTHOR]

The corneal endothelium state is verified on the basis of an in vivo specular microscope image from which the shape and density of cells are exploited for data description. Due to the relatively low image quality resulting from a high magnification of the living, non-stained tissue, both manual and automatic analysis of the data is a challenging task. Although, many automatic or semi-automatic solutions have already been introduced, all of them are prone to inaccuracy. This work presents a comparison of four methods (fully-automated or semi-automated) for endothelial cell segmentation, all of which represent a different approach to cell segmentation; fast robust stochastic watershed (FRSW), KH method, active contours solution (SNAKE), and TOPCON ImageNET. Moreover, an improvement framework is introduced which aims to unify precise cell border location in images pre-processed with differing techniques. Finally, the influence of the selected methods on clinical parameters is examined, both with and without the improvement framework application. The experiments revealed that although the image segmentation approaches differ, the measures calculated for clinical parameters are in high accordance when CV (coefficient of variation), and CVSL (coefficient of variation of cell sides length) are considered. Higher variation was noticed for the H (hexagonality) metric. Utilisation of the improvement framework assured better repeatability of precise endothelial cell border location between the methods while diminishing the dispersion of clinical parameter values calculated for such images. Finally, it was proven statistically that the image processing method applied for endothelial cell analysis does not influence the ability to differentiate between the images using medical parameters. [ABSTRACT FROM AUTHOR]

This paper aims to improve automation in brick segmentation and crack detection of masonry walls through image-based techniques and machine learning. Initially, a large dataset of hand-labelled images of different in colour, texture, and size of brickwork masonry walls has been developed. Then, different deep learning networks (U-Net, DeepLabV3+, U-Net (SM), LinkNet (SM), and FPN (SM)) were utilised and their quality was assessed. Furthermore, the ability to generate geometric models of masonry structures and the evaluation of the geometric properties of detected cracks was also investigated. Additional metrics were also developed to compare the CNN output with other image-processing algorithms. From the analysis of results it was shown that the use of machine learning, for brick segmentation, provides better outcome than typical image-processing applications. This implementation of deep-learning for crack detection and localisation of bricks in masonry walls highlights the great potential of new technologies for documentation of masonry fabric. • Automatic brick segmentation from images using machine learning. • Automatic crack quantification from images using machine learning. • The brick detection model obtained a validation-accuracy of 96.86%. • The proposed procedure paves the way for automated documentation and inspection of full-scale masonry structures. [ABSTRACT FROM AUTHOR]

Image segmentation plays an important role in image processing and computer vision. It is often used to classify an image into separate regions, which ideally correspond to different real-world objects. Several segmentation methods have been proposed in the literature, being thresholding techniques the most popular. In such techniques, it is selected a set of proper threshold values that optimize a determined functional criterion, so that each pixel is assigned to a determined class according to its corresponding threshold points. One interesting functional criterion is the Tsallis entropy, which gives excellent results in bi-level thresholding. However, when it is applied to multilevel thresholding, its evaluation becomes computationally expensive, since each threshold point adds restrictions, multimodality and complexity to its functional formulation. Therefore, in the process of finding the appropriate threshold values, it is desired to limit the number of evaluations of the objective function (Tsallis entropy). Under such circumstances, most of the optimization algorithms do not seem to be suited to face such problems as they usually require many evaluations before delivering an acceptable result. On the other hand, the Electromagnetism-Like algorithm is an evolutionary optimization approach which emulates the attraction–repulsion mechanism among charges for evolving the individuals of a population. This technique exhibits interesting search capabilities whereas maintains a low number of function evaluations. In this paper, a new algorithm for multilevel segmentation based on the Electromagnetism-Like algorithm is proposed. In the approach, the optimization algorithm based on the electromagnetism theory is used to find the optimal threshold values by maximizing the Tsallis entropy. Experimental results over several images demonstrate that the proposed approach is able to improve the convergence velocity, compared with similar methods such as Cuckoo search, and Particle Swarm Optimization. [ABSTRACT FROM AUTHOR]

Cardiac magnetic resonance imaging (CMR) is a widely used non-invasive imaging modality for evaluating cardiovascular diseases. CMR is the gold standard method for left and right ventricular functional assessment due to its ability to characterize myocardial structure and function and low intra- and inter-observer variability. However the post-processing segmentation during the functional evaluation is time-consuming and challenging. A fully automated segmentation method can assist the experts; therefore, they can do more efficient work. In this paper, a regression-based fully automated method is presented for the right- and left ventricle segmentation. For training and evaluation, our dataset contained MRI short-axis scans of 5570 patients, who underwent CMR examinations at Heart and Vascular Center, Semmelweis University Budapest. Our approach is novel and after training the state-of-the-art algorithm on our dataset, our algorithm proved to be superior on both of the ventricles. The evaluation metrics were the Dice index, Hausdorff distance and volume related parameters. We have achieved average Dice index for the left endocardium: 0.927, left epicardium: 0.940 and right endocardium: 0.873 on our dataset. We have also compared the performance of the algorithm to the human-level segmentation on both ventricles and it is similar to experienced readers for the left, and comparable for the right ventricle. We also evaluated the proposed algorithm on the ACDC dataset, which is publicly available, with and without transfer learning. The results on ACDC were also satisfying and similar to human observers. Our method is lightweight, fast to train and does not require more than 2 GB GPU memory for execution and training. • A novel regression-based method for the fully-automatic segmentation of CMR short-axis images on both ventricles. • The algorithm performance is similar to an experienced radiologist on the left and right ventricle. • Our algorithm is more general because our dataset was more comprehensive and diverse compared to the other datasets used for developing segmentation algorithms. It does not contain only healthy subjects but a wide scale of cardiovascular diseases. • More accurate results than the baselines, we trained Bai's method from scratch on our dataset. • Lightweight model, requires fewer memory and computation to train and use the model. [ABSTRACT FROM AUTHOR]