An Improved Python-Based Image Processing Algorithm for Flotation Foam Analysis

The cell image segmentation has very high practical significance in medical diagnosis. But the cell image has the problems of accretive cells, incoherent cell boundary, and the internal cavity that make it difficult to image segmentation. In this paper, a watershed algorithm based on distance transform is proposed to solve images of cells adhesion. Firstly, image enhancement is carried out as the image preprocessing, then the OTSU threshold segmentation is used to rough segment the image, finally the watershed algorithm by optimizing the seed points is adopt for fine segmentation. Experiment results showed that the proposed algorithm is effectively solved the problems of cell adhesion and over-segmentation, the image segmentation accuracy is more than the traditional watershed algorithm, and remained the cell shape in maximum extent. Therefore, the watershed segmentation based on distance transformation transform is practical according to the accretive cell images.

An improved watershed algorithm based on fuzzy threshold was proposed for the over-segmentation of watershed images due to their sensitivity to detail and noise. Firstly, the noise is suppressed by median filtering, and the image is preprocessed with multi-fuzzy threshold segmentation technology to obtain the obvious gray image of the edge region. Then, the main contour of the image is extracted by the fuzzy threshold algorithm. Finally, the image is segmented again by the watershed algorithm. Experimental results show that the new algorithm is much better than the traditional watershed algorithm and can effectively solve the over-segmentation problem of watershed algorithm. The segmentation results are more consistent with the visual characteristics of human beings, and it is an effective, accurate and practical image segmentation method.

It is the key step of automatic apple picking and sorting to segment of the apple image in natural environment using image information processing technology. In order to improve the intellectualization of apple picking process and effectively extract the target of image segmentation, a novel image segmentation method of Huaniu apple based on pulse coupled neural network (PCNN) and watershed algorithm is proposed. Firstly, the PCNN model is improved and optimized on the basis of image RGB to L*A*B space conversion. Then, the improved PCNN combined with watershed algorithm was used to segment the apple image on considering the image texture and color feature information. The theoretical analysis and experimental results show that the novel method is effectively able to segment target image, and can preserve the image details and the edges as well. The proposed algorithm has better subjective vision effect and objective quality than the traditional watershed and other related algorithms.

Objective. Cardiovascular disease (CVD) is a group of diseases affecting cardiac and blood vessels, and short-axis cardiac magnetic resonance (CMR) images are considered the gold standard for the diagnosis and assessment of CVD. In CMR images, accurate segmentation of cardiac structures (e.g. left ventricle) assists in the parametric quantification of cardiac function. However, the dynamic beating of the heart renders the location of the heart with respect to other tissues difficult to resolve, and the myocardium and its surrounding tissues are similar in grayscale. This makes it challenging to accurately segment the cardiac images. Our goal is to develop a more accurate CMR image segmentation approach. Approach. In this study, we propose a regional perception and multi-scale feature fusion network (RMFNet) for CMR image segmentation. We design two regional perception modules, a window selection transformer (WST) module and a grid extraction transformer (GET) module. The WST module introduces a window selection block to adaptively select the window of interest to perceive information, and a windowed transformer block to enhance global information extraction within each feature window. The WST module enhances the network performance by improving the window of interest. The GET module grids the feature maps to decrease the redundant information in the feature maps and enhances the extraction of latent feature information of the network. The RMFNet further introduces a novel multi-scale feature extraction module to improve the ability to retain detailed information. Main results. The RMFNet is validated with experiments on three cardiac data sets. The results show that the RMFNet outperforms other advanced methods in overall performance. The RMFNet is further validated for generalizability on a multi-organ data set. The results also show that the RMFNet surpasses other comparison methods. Significance. Accurate medical image segmentation can reduce the stress of radiologists and play an important role in image-guided clinical procedures.

Accurate image segmentation is crucial in medical imaging for quantifying diseases, assessing prognosis, and evaluating treatment outcomes. However, existing methods often fall short in integrating global and local features in a meaningful way, failing to give sufficient attention to abnormal regions and boundary details in medical images. These limitations hinder the effectiveness of segmentation techniques in clinical settings. To address these issues, we propose a novel deep learning-based approach, MIPC-Net, designed for precise boundary segmentation in medical images. Our approach, inspired by radiologists' working patterns, introduces two distinct modules: 1. Mutual Inclusion of Position and Channel Attention (MIPC) Module: To improve boundary segmentation precision, we present the MIPC module. This module enhances the focus on channel information while extracting position features and vice versa, effectively enhancing the segmentation of boundaries in medical images. 2. Skip-Residue Module: To optimize the restoration of medical images, we introduce Skip-Residue, a global residual connection. This module improves the integration of the encoder and decoder by filtering out irrelevant information and recovering the most crucial information lost during the feature extraction process. We evaluate the performance of MIPC-Net on three publicly accessible datasets: Synapse, ISIC2018-Task, and Segpc. The evaluation uses metrics such as the Dice coefficient (DSC) and Hausdorff Distance (HD). Our ablation study confirms that each module contributes to the overall improvement of segmentation quality. Notably, with the integration of both modules, our model outperforms state-of-the-art methods across all metrics. Specifically, MIPC-Net achieves a 2.23 mm reduction in Hausdorff Distance on the Synapse dataset, highlighting the model's enhanced capability for precise image boundary segmentation. The introduction of the novel MIPC and Skip-Residue modules significantly improves feature extraction accuracy, leading to better boundary recognition in medical image segmentation tasks. Our approach demonstrates substantial improvements over existing methods, as evidenced by the results on benchmark datasets.

In the past decade, people's interest in computer vision has grown. Stable multiplication rate boost with powerful computing power every 13 months, face detection and recognition has changed from esoteric concept to popular One of the better and successful applications of computer vision research and image analysis and image analysis Algorithm-based understanding. In our work, we will be detecting contours (shapes) of various geometrical figures in the sample given binary mask extracted from HSV (Hue Saturation Value) range using Python 3.6, Open-Source Computer Vision Library (OpenCV 3x) and Numpy. The very fundamental functions are used for processing the frames, that involves getting live video feed, loading them frame-by-frame, and detecting various shape of red colors inside the live feed frames and segment them with previously saved image with the exact same location. In this paper we discuss three primary problems of computer vision: Finding contours, detection of specified color and segment with another image. We tried state-of-the-art algorithms for these three tasks. HSV color extraction is used to get the mask of desire colored object. In order to get high quality image segmentations results, we applied Morphological operations to refine the mask such as, Opening which removes unnecessary details which in our case are the white region on the boundary of the cloth that is not required, Erosion, Dilatation operation and Gaussian Blurring to smoothen the result. We link this contour detector with Convex Hull, this strategy performs better than our existing image segmentation algorithms on measures of both boundary and segmentation quality. Finally, we replaced pixel value of red color region detected by HSV mask with the pixel value of our background image with the help of bitwise operation to get the augmented result of invisible cloak.

Image segmentation of brain low-grade glioma (LGG) magnetic resonance imaging (MRI) contributes tremendously to diagnosis, classification and treatment of the disease. A tangible, accurate, reliable and fast image segmentation technique is demanded in clinical diagnosis and research. The emerging machine learning technique has been demonstrated its unique capability in the field of medical image processing, including medical image segmentation. Support vector machine (SVM) and convolutional neural network (CNN) are two widely used machine learning methods. In this work, image segmentation tools based on SVM and CNN are developed and evaluated for brain LGG MR image segmentation studies. The segmentation performance in terms of accuracy and cost is quantitatively analyzed and compared between the SVM and CNN techniques developed. Computed on the Google CoLab, each of the 109 SVM models represents an individual patient, is trained using a single image of that patient and takes a few seconds to complete. The CNN model is trained on a drastically larger dataset of 19,760 data augmented images and takes approximately 2 hours to obtain the most optimal result. The SVM models achieved an average and median accuracy of 0.937 and 0.976 respectively, precision of 0.456 and 0.535 respectively, recall of 0.878 and 0.906 respectively, and F1 score of 0.546 and 0.662 respectively. Although the CNN model required a significantly longer calculation time, it surpassed the SVM models in performance in LGG MR image segmentation, achieving an accuracy of 0.998, a precision of 0.999, a recall of 0.999 and an F1 score of 0.999. This study shows that SVM with appropriate filtering techniques is capable of obtaining reliable and fast segmentation of brain LGG MR images with sufficient accuracy and limited image data. CNN technique outperforms SVM in the accuracy of segmentation with requirements of significantly enlarged data set, long computation time and high-performance computer.

In recent studies, drowsiness of drivers while driving is seen as one of the most important causes of deaths due to traffic accidents. Therefore, in this study, an algorithm that determines driver fatigue by examining the driver's eye conditions in real-time is proposed. Python versions of OpenCV (Open Source Computer Vision Library) and machine learning libraries were used to create this proposed algorithm. In the study, the face and eyes were detected with the Cascade classifier using Haar features. Detected eyes were classified as “open” “closed” or “half open” with the model trained by the SVM (Support Vector Machine) method according to the HOG (Histogram of Oriented Gradient) feature descriptor. Driver fatigue was decided according to the PERCLOS (Percentage of Eyelid Closure) method, which examines the percentage of eyelid closure over time. In the proposed system, fatigue was detected according to the time the driver's eyes were in the “half-open” or “closed” state, and a related user interface was designed and audio and visual warnings were given to the driver.

This article presents the application of a new approach to segmentation of images of the iris and pupil of the human eye using the pretrained neural network model YoLoV5. Despite the use of modern neural network technologies and computer vision methods for image processing and eye segmentation, not all of them guarantee obtaining a highly accurate result of the desired circles. Examples of incorrect segmentation of the iris and pupil obtained using the Mediapipe framework and the FloodFill algorithm are given. Smoothing filters were previously used to eliminate artifacts, noise and outliers in the data. At the same time, the use of smoothing filters contributes to the distortion of the data they process, especially when working in real-time systems with the property of observation duration. Since the diagnosis of the functional state of a person by the pupillometry method is sensitive to the quality of the original data, the use of smoothing filters in a number of diagnostic cases is not acceptable. To improve the accuracy of iris and eye image segmentation and avoid data distortion by smoothing filters, the authors developed a computer vision module based on the eye circumference segmentation mechanism using the pre-trained YoLoV5 neural network model. Examples of eye circumference segmentation by the YoLoV5 neural network are given. The accuracy of iris and pupil image segmentation was assessed by comparing the number of outliers and omissions in time series values obtained using the approaches discussed in the article. The approach to using the YoLoV5 neural network model proposed in the article is implemented in solving the problem of driver fatigue monitoring and is used in a prototype of a specialized device developed by the authors.

In recent years, the use of computer technology in medical image processing and analysis has been a hot issue of research and application at home and abroad, and the application of the PACS system has created a new era of digital medical treatment. Based on the technology of PACS system for post-processing segmentation, an improved watershed algorithm has been brought up to the medical image segmentation. Compared with the original algorithm, the improved algorithm greatly improves the accuracy of image segmentation; and the problem of over-segmentation of the original algorithm has been solved better. Dynamic merge rule is added in process of implementing watershed algorithm and the formation of over-segmentation regions due to the algorithm itself are restrained directly. Applying the improved algorithm to the PACS system, it has been proved that the algorithm can effectively extract the medical image lesions.KeywordsPACS systemMedical image segmentationWatershed algorithmDynamic merge

This paper presents the mathematical modeling, design, and implementation of a ball balancer. The aim is to balance the ball at a desired position on the base plate. Ball balancer with two degrees of freedom uses machine vision as feedback and DC servomotors as actuators. Mathematical modeling of ball balancer system is done based on Lagrange–Euler equation, processing of the real-time image, and finding the co-ordinates of the ball is done using Open Source Computer Vision Library software. The co-ordinates of the ball are sent to Arduino through universal serial bus (USB) to transistor—transistor logic (TTL) converter. Perfect balancing of the ball is achieved using a proportional—derivative (PD) controller and pole placement technique. Stability of the designed ball balancer system is verified by its time response analysis using MATLAB/Simulink.KeywordsArduino controllerBall balancer systemImage processingMachine visionMATLAB/SimulinkOpen CV software

The automatic segmentation of medical images has widespread applications in modern clinical workflows. The Segment Anything Model (SAM), a recent development of foundational models in computer vision, has become a universal tool for image segmentation without the need for specific domain training. However, SAM's reliance on prompts necessitates human-computer interaction during the inference process. Its performance on specific domains can also be limited without additional adaptation. In contrast, traditional models like nnUNet are designed to perform segmentation tasks automatically during inference and can work well for each specific domain, but they require extensive training on domain-specificdatasets. To leverage the advantages of both foundational and domain-specific models and achieve fully automated segmentation with limited training samples, we propose nnSAM, which combines the robust feature extraction capabilities of SAM with the automatic configuration abilities of nnUNet to enhance the accuracy and robustness of medical image segmentation on smalldatasets. We propose the nnSAM model for small sample medical image segmentation. We made optimizations for this goal via two main approaches: first, we integrated the feature extraction capabilities of SAM with the automatic configuration advantages of nnUNet, which enables robust feature extraction and domain-specific adaptation on small datasets. Second, during the training process, we designed a boundary shape supervision loss based on level set functions and curvature calculations, enabling the model to learn anatomical shape priors from limited annotationdata. We conducted quantitative and qualitative assessments on the performance of our proposed method on four segmentation tasks: brain white matter, liver, lung, and heart segmentation. Our method achieved the best performance across all tasks. Specifically, in brain white matter segmentation using 20 training samples, nnSAM achieved the highest DICE score of 82.77 ( 10.12) % and the lowest average surface distance (ASD) of 1.14 ( 1.03) mm, compared to nnUNet, which had a DICE score of 79.25 ( 17.24) % and an ASD of 1.36 ( 1.63) mm. A sample size study shows that the advantage of nnSAM becomes more prominent under fewer trainingsamples. A comprehensive evaluation of multiple small-sample segmentation tasks demonstrates significant improvements in segmentation performance by nnSAM, highlighting the vast potential of small-samplelearning.

Aiming at the problems of high labor cost, low detection efficiency, and insufficient detection accuracy of traditional pipe gallery disease detection methods, this paper proposes a pipe gallery disease segmentation model, PipeU-NetX, based on deep learning technology. By introducing the innovative down-sampling module MD-U, up-sampling module SC-U, and feature fusion module FFM, the model optimizes the feature extraction and fusion process, reduces the loss of feature information, and realizes the accurate segmentation of the pipe gallery disease image. In comparison with U-Net, FCN, and Deeplabv3+ models, PipeU-NetX achieved the best PA, MPA, FWIoU, and MIoU, which were 99.15%, 92.66%, 98.34%, and 87.63%, respectively. Compared with the benchmark model U-Net, the MIoU and MPA of the PipeU-NetX model increased by 4.64% and 3.92%, respectively, and the number of parameters decreased by 23.71%. The detection speed increased by 22.1%. The PipeU-NetX model proposed in this paper shows the powerful ability of multi-scale feature extraction and defect area adaptive recognition and provides an effective solution for the intelligent monitoring of the pipe gallery environment and accurate disease segmentation.

In recent years, accurate segmentation of images is a very challenging task for image processing applications. Image segmentation can also be stated as clustering problem in which image pixels are clustered according to the homogeneity of their feature values. Crisp K-means clustering algorithm can achieve the solution of this problem. But it is not suitable for coinciding partition and it is unable to handle noisy data. Fuzzy form of C-means clustering can manage overlapping partition problem, but traditional FCM is also sensitive to noise pixels. In this paper, neighborhood-influenced Fuzzy C-means (NFCM) algorithm is proposed where spatial neighborhood information of pixels is incorporated with the traditional fuzzy c-means algorithm. NFCM is giving more accurate segmentation result compared to hard c-means and fuzzy c-means based segmentation techniques.

Image processing is an important way to obtain information and is widely used in important fields such as military, medical and transportation. Image segmentation plays an important role in image processing. In view of the inherent complexity and correlation of the image itself, how to deal with the uncertainty in the image segmentation process is the main work to obtain a more accurate image segmentation result. We propose an image single-threshold segmentation algorithm using the maximum dependency of variable precision rough set (VPRS). The algorithm uses VPRS to represent the image, and uses the maximum dependency and particle swarm optimization to solve the optimal image segmentation threshold, which effectively handles the uncertainty in image segmentation. The experiments show that the single-threshold segmentation algorithm has certain practicability and flexibility, the segmentation effect is better than the maximum average information entropy method, and the segmentation efficiency is significantly higher than the ordinary iterative method.