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Infrared image processing is an effective method for diagnosing faults in electrical equipment, in which target device segmentation and temperature feature extraction are key steps. Target device segmentation separates the device to be diagnosed from the image, while temperature feature extraction analyzes whether the device is overheating and has potential faults. However, the segmentation of infrared images of electrical equipment is slow due to issues such as high computational complexity, and the temperature information extracted lacks accuracy due to the insufficient consideration of the non-linear relationship between the image grayscale and temperature. Therefore, in this study, we propose an optimized maximum between-class variance thresholding method (OTSU) segmentation algorithm based on the Gray Wolf Optimization (GWO) algorithm, which accelerates the segmentation speed by optimizing the threshold determination process using OTSU. The experimental results show that compared to the non-optimized method, the optimized segmentation method increases the threshold calculation time by more than 83.99% while maintaining similar segmentation results. Based on this, to address the issue of insufficient accuracy in temperature feature extraction, we propose a temperature value extraction method for infrared images based on the K-nearest neighbor (KNN) algorithm. The experimental results demonstrate that compared to traditional linear methods, this method achieves a 73.68% improvement in the maximum residual absolute value of the extracted temperature values and a 78.95% improvement in the average residual absolute value. [ABSTRACT FROM AUTHOR]

Unusual collection of mass tissue in human body is commonly refereed as tumor. The tumor when it is found in brain. These tumor cells have tendency to multiply and grow in rapid speed. The growth of tumors is generally uncontrollable in nature. Tumor in brain develops along the skull and it evolves to contact with the functioning of the brain. Brain tumor (BT) can be detected at the earlier stages with the help of MRI or CT scan techniques. These scanning techniques are proved to be efficient in detecting the tumor irrespective of its size. Brain tumor being a life‐threatening disease has to be diagnosed at the earliest before it turns to be malignant. The current research work focuses in proposing an efficient image processing techniques by processing the MRI images of human brain which is affected by brain tumor. This process is carried out in two stages as image denoising and image segmentation which are helpful in detecting and localizing the tumor affected region in human brain. Initially, the MRI image is read and preprocessed by converting the input image into a grayscale image and noise removed by involving the proposed method. Later, the proposed image is segmented using sobel edge detection method and the image is enhanced using the image enhancement techniques. It is achieved by using the proficient blended thresholding (PBT) Segmentation method. The performance of the proposed methods is evaluated using PSNR (peak signal noise ratio) and RMSE (root mean square error). The proposed conserve edge scrutinize (CES) filter achieved highest PSNR value. Then segmentation is evaluated by five metrices: Sensitivity, Specificity, Dice, Jaccard, and Accuracy. [ABSTRACT FROM AUTHOR]

Background: Breast fibroadenoma poses a significant health concern, particularly for young women. Computer-aided diagnosis has emerged as an effective and efficient method for the early and accurate detection of various solid tumors. Automatic segmentation of the breast fibroadenoma is important and potentially reduces unnecessary biopsies, but challenging due to the low image quality and presence of various artifacts in sonography. Methods: Human learning involves modularizing complete information and then integrating it through dense contextual connections in an intuitive and efficient way. Here, a human learning paradigm was introduced to guide the neural network by using two consecutive phases: the feature fragmentation stage and the information aggregation stage. To optimize this paradigm, three fragmentation attention mechanisms and information aggregation mechanisms were adapted according to the characteristics of sonography. The evaluation was conducted using a local dataset comprising 600 breast ultrasound images from 30 patients at Suining Central Hospital in China. Additionally, a public dataset consisting of 246 breast ultrasound images from Dataset_BUSI and DatasetB was used to further validate the robustness of the proposed network. Segmentation performance and inference speed were assessed by Dice similarity coefficient (DSC), Hausdorff distance (HD), and training time and then compared with those of the baseline model (TransUNet) and other state-of-the-art methods. Results: Most models guided by the human learning paradigm demonstrated improved segmentation on the local dataset with the best one (incorporating C3ECA and LogSparse Attention modules) outperforming the baseline model by 0.76% in DSC and 3.14 mm in HD and reducing the training time by 31.25%. Its robustness and efficiency on the public dataset are also confirmed, surpassing TransUNet by 0.42% in DSC and 5.13 mm in HD. Conclusions: Our proposed human learning paradigm has demonstrated the superiority and efficiency of ultrasound breast fibroadenoma segmentation across both public and local datasets. This intuitive and efficient learning paradigm as the core of neural networks holds immense potential in medical image processing. [ABSTRACT FROM AUTHOR]

An important concern with regard to the ensembles of algorithms is that using the individually optimal parameter settings of the members does not necessarily maximize the performance of the ensemble itself. In this paper, we propose a novel evaluation method for simulated annealing that combines dataset sampling and image downscaling to accelerate the parameter optimization of medical image segmentation ensembles. The scaling levels and sample sizes required to maintain the convergence of the search are theoretically determined by adapting previous results for simulated annealing with imprecise energy measurements. To demonstrate the efficiency of the proposed method, we optimize the parameters of an ensemble for lung segmentation in CT scans. Our experimental results show that the proposed method can maintain the solution quality of the base method with significantly lower runtime. In our problem, optimization with simulated annealing yielded an F 1 score of 0.9397 and an associated M C C of 0.7757. Our proposed method maintained the solution quality with an F 1 score of 0.9395 and M C C of 0.7755 while exhibiting a 42.01% reduction in runtime. It was also shown that the proposed method is more efficient than simulated annealing with only sampling-based evaluation when the dataset size is below a problem-specific threshold. [ABSTRACT FROM AUTHOR]

We investigated various methods for image segmentation and image processing for the segmentation of MRI of human medical data, as well as bioinformatics for the segmentation of brain cell details, in this work. The goal is to demonstrate and bring various mathematical analyses for medical and biological image analysis. We proposed new software and methods for improving the segmentation of biological and medical data. This way, we can fi nd new ways to improve the diagnostic process in medical data and improve results in cell and iron diagnostics. We present the GrabCut algorithm as well as new, improved software for this part, a fuzzy approach and fuzzy processing of tissues, and fi nally machine-learning techniques with neural networks. We implemented the new software in the C++ programming language for the Grab cut algorithm. Consequently, we present a fuzzy approach to the diagnosis of image data in Matlab. Finally, a deep learning-based approach is used, with a U-Net-based segmentation architecture proposed to measure the various brain cell parameters. We will be able to proceed with data that we were unable to proceed when using other methods. As a result, we improved biological and medical data segmentation to obtain better boundaries and sharper edges on the objects. There is still space to extend these methods to other medical and biological applications (Tab. 1, Fig. 34, Ref. 46). Text in PDF www.elis.sk. [ABSTRACT FROM AUTHOR]

Generation of random fields (GRF) for image segmentation represents partitioning an image into different regions that are homogeneous or have similar facets of the image. It is one of the most challenging tasks in image processing and a very important pre-processing step in the fields of computer vision, image analysis, medical image processing, pattern recognition, remote sensing, and geographical information system. Many researchers have presented numerous image segmentation approaches, but still, there are challenges like segmentation of low contrast images, removal of shadow in the images, reduction of high dimensional images, and computational complexity of segmentation techniques. In this review paper, the authors address these issues. The experiments are conducted and tested on the Berkely dataset (BSD500), Semantic dataset, and our own dataset, and the results are shown in the form of tables and graphs. [ABSTRACT FROM AUTHOR]

Deep neural networks have been tremendously successful at segmenting objects in images. However, it has been shown they still have limitations on challenging problems such as the segmentation of medical images. The main reason behind this lower success resides in the reduced size of the object in the image. In this paper we overcome this limitation through a cyclic collaborative framework, CyCoSeg. The proposed framework is based on a deep active shape model (D-ASM), which provides prior information about the shape of the object, and a semantic segmentation network (SSN). These two models collaborate to reach the desired segmentation by influencing each other: SSN helps D-ASM identify relevant keypoints in the image through an Expectation Maximization formulation, while D-ASM provides a segmentation proposal that guides the SSN. This cycle is repeated until both models converge. Extensive experimental evaluation shows CyCoSeg boosts the performance of the baseline models, including several popular SSNs, while avoiding major architectural modifications. The effectiveness of our method is demonstrated on the left ventricle segmentation on two benchmark datasets, where our approach achieves one of the most competitive results in segmentation accuracy. Furthermore, its generalization is demonstrated for lungs and kidneys segmentation in CT scans. [ABSTRACT FROM AUTHOR]

The variation in the thermal state of electrical energy substation equipment is normally associated with natural wear or equipment failure. This can be detected by infrared thermography, but technically it demands a long time to analyze these images. Computational analysis can allow an automated, more agile, and more efficient analysis to detect overheated regions in thermographic images. Therefore, it is necessary to segment the region of interest in the images; however, the results may diverge depending on the technique used. Thus, this article presents the improvement of four different techniques implemented in Python and applied in a substation under real operating conditions for a period of eleven months. The performance of the four methods was compared using eight statistical performance measures, and the efficiency was measured by the runtime. The segmentation results showed that the methods based on a threshold (Otsu and Histogram-Based Threshold) were fast, with processing times of 0.11 to 0.24 s, but caused excessive segmentation, presenting the lowest accuracy (0.160 and 0.444) and precision (0.004 and 0.049, respectively). The clustering-based methods (Cluster K-means and Fuzzy C-means) showed similar results to each other but were more accurate (0.936 to 1.000), more precise (0.965 to 1.000), and slower, with 2.55 and 38.8 s, respectively, compared to the threshold methods. The Fuzzy C-means method obtained the highest values of specificity, accuracy, and precision among the methods under analysis, followed by the Cluster K-means method. [ABSTRACT FROM AUTHOR]

Background: Superpixel segmentation is a powerful preprocessing tool to reduce the complexity of image processing. Traditionally, size uniformity is one of the significant features of superpixels. However, in medical images, in which subjects scale varies greatly and background areas are often flat, size uniformity rarely conforms to the varying content. To obtain the fewest superpixels with retaining important details, the size of superpixel should be chosen carefully. Methods: We propose a scale-adaptive superpixel algorithm relaxing the size-uniformity criterion for medical images, especially pathological images. A new path-based distance measure and superpixel region growing schema allow our algorithm to generate superpixels with different scales according to the complexity of image content, that is smaller (larger) superpixels in color-riching areas (flat areas). Results: The proposed superpixel algorithm can generate superpixels with boundary adherence, insensitive to noise, and with extremely big sizes and extremely small sizes on one image. The number of superpixels is much smaller than size-uniformly superpixel algorithms while retaining more details of images. Conclusion: With the proposed algorithm, the choice of superpixel size is automatic, which frees the user from the predicament of setting suitable superpixel size for a given application. The results on the nuclear dataset show that the proposed superpixel algorithm superior to the respective state-of-the-art algorithms on both quantitative and quantitative comparisons. [ABSTRACT FROM AUTHOR]

Cloud and cloud shadow segmentation is one of the most important issues in remote sensing image processing. Most of the remote sensing images are very complicated. In this work, a dual-branch model composed of transformer and convolution network is proposed to extract semantic and spatial detail information of the image, respectively, to solve the problems of false detection and missed detection. To improve the model’s feature extraction, a mutual guidance module (MGM) is introduced, so that the transformer branch and the convolution branch can guide each other for feature mining. Finally, in view of the problem of rough segmentation boundary, this work uses different features extracted by the transformer branch and the convolution branch for decoding and repairs the rough segmentation boundary in the decoding part to make the segmentation boundary clearer. Experimental results on the Landsat-8, Sentinel-2 data, the public dataset high-resolution cloud cover validation dataset created by researchers at Wuhan University (HRC_WHU), and the public dataset Spatial Procedures for Automated Removal of Cloud and Shadow (SPARCS) demonstrate the effectiveness of our method and its superiority to the existing state-of-the-art cloud and cloud shadow segmentation approaches. [ABSTRACT FROM AUTHOR]

The segmentation methods for image processing are studied in the presented work. Image segmentation can be defined as a vital step in digital image processing. Also, it is used in various applications including object co-segmentation, recognition tasks, medical imaging, content based image retrieval, object detection, machine vision and video surveillance. A lot of approaches were created for image segmentation. In addition, the main goal of segmentation is to facilitate and alter the image representation into something which is more important and simply to be analyzed. The approaches of image segmentation are splitting the images into a few parts on the basis of image's features including texture, color, pixel intensity value and so on. With regard to the presented study, many approaches of image segmentation are reviewed and discussed. The techniques of segmentation might be categorized into six classes: First, thresholding segmentation techniques such as global thresholding (iterative thresholding, minimum error thresholding, otsu's, optimal thresholding, histogram concave analysis and entropy based thresholding), local thresholding (Sauvola's approach, T.R Singh's approach, Niblack's approaches, Bernsen's approach Bruckstein's and Yanowitz method and Local Adaptive Automatic Binarization) and dynamic thresholding. Second, edge-based segmentation techniques such as gray-histogram technique, gradient based approach (laplacian of gaussian, differential coefficient approach, canny approach, prewitt approach, Roberts approach and sobel approach). Thirdly, region based segmentation approaches including Region growing techniques (seeded region growing (SRG), statistical region growing, unseeded region growing (UsRG)), also merging and region splitting approaches. Fourthly, clustering approaches, including soft clustering (fuzzy C-means clustering (FCM)) and hard clustering (K-means clustering). Fifth, deep neural network techniques such as convolution neural network, recurrent neural networks (RNNs), encoder-decoder and Auto encoder models and support vector machine. Finally, hybrid techniques such as evolutionary approaches, fuzzy logic and swarm intelligent (PSO and ABC techniques) and discusses the pros and cons of each method. [ABSTRACT FROM AUTHOR]

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]

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]