UDT:U-shaped deformable transformer for subarachnoid haemorrhage image segmentation

Subarachnoid haemorrhage(SAH),mostly caused by the rupture of intracranial aneu-rysm,is a common disease with a high fatality rate.SAH lesions are generally diffusely distributed,showing a variety of scales with irregular edges.The complex characteristics of lesions make SAH segmentation a challenging task.To cope with these difficulties,a u-shaped deformable transformer(UDT)is proposed for SAH segmentation.Specifically,first,a multi-scale deformable attention(MSDA)module is exploited to model the diffuseness and scale-variant characteristics of SAH lesions,where the MSDA module can fuse features in different scales and adjust the attention field of each element dynamically to generate discriminative multi-scale features.Second,the cross deformable attention-based skip connection(CDASC)module is designed to model the irregular edge char-acteristic of SAH lesions,where the CDASC module can utilise the spatial details from encoder features to refine the spatial information of decoder features.Third,the MSDA and CDASC modules are embedded into the backbone Res-UNet to construct the proposed UDT.Extensive experiments are conducted on the self-built SAH-CT dataset and two public medical datasets(GlaS and MoNuSeg).Experimental results show that the presented UDT achieves the state-of-the-art performance.

EFFECTIVE IMAGE SEGMENTATION FRAMEWORK FOR GAUSSIAN MIXTURE MODEL INCORPORATING LOCAL INFORMATION

A new two-step framework is proposed for image segmentation. In the first step, the gray-value distribution of the given image is reshaped to have larger inter-class variance and less intra-class variance. In the sec- ond step, the discriminant-based methods or clustering-based methods are performed on the reformed distribution. It is focused on the typical clustering methods-Gaussian mixture model （GMM） and its variant to demonstrate the feasibility of the framework. Due to the independence of the first step in its second step, it can be integrated into the pixel-based and the histogram-based methods to improve their segmentation quality. The experiments on artificial and real images show that the framework can achieve effective and robust segmentation results.

Dual-Branch-UNet: A Dual-Branch Convolutional Neural Network for Medical Image Segmentation

In intelligent perception and diagnosis of medical equipment,the visual and morphological changes in retinal vessels are closely related to the severity of cardiovascular diseases(e.g.,diabetes and hypertension).Intelligent auxiliary diagnosis of these diseases depends on the accuracy of the retinal vascular segmentation results.To address this challenge,we design a Dual-Branch-UNet framework,which comprises a Dual-Branch encoder structure for feature extraction based on the traditional U-Net model for medical image segmentation.To be more explicit,we utilize a novel parallel encoder made up of various convolutional modules to enhance the encoder portion of the original U-Net.Then,image features are combined at each layer to produce richer semantic data and the model’s capacity is adjusted to various input images.Meanwhile,in the lower sampling section,we give up pooling and conduct the lower sampling by convolution operation to control step size for information fusion.We also employ an attentionmodule in the decoder stage to filter the image noises so as to lessen the response of irrelevant features.Experiments are verified and compared on the DRIVE and ARIA datasets for retinal vessels segmentation.The proposed Dual-Branch-UNet has proved to be superior to other five typical state-of-the-art methods.

Development and Parallelization of an Improved 2D Moving Window Standard Deviation Python Routine for Image Segmentation Purposes

Two additional features are particularly useful in pixelwise satellite data segmentation using neural networks: one results from local window averaging around each pixel (MWA) and another uses a standard deviation estimator (MWSD) instead of the average. While the former’s complexity has already been solved to a satisfying minimum, the latter did not. This article proposes a new algorithm that can substitute a naive MWSD, by making the complexity of the computational process fall from O(N2n2) to O(N2n), where N is a square input array side, and n is the moving window’s side length. The Numba python compiler was used to make python a competitive high-performance computing language in our optimizations. Our results show efficiency benchmars

Recognition and Classification of Pomegranate Leaves Diseases by Image Processing and Machine Learning Techniques

Disease recognition in plants is one of the essential problems in agricultural image processing.This article focuses on designing a framework that can recognize and classify diseases on pomegranate plants exactly.The framework utilizes image processing techniques such as image acquisition,image resizing,image enhancement,image segmentation,ROI extraction(region of interest),and feature extraction.An image dataset related to pomegranate leaf disease is utilized to implement the framework,divided into a training set and a test set.In the implementation process,techniques such as image enhancement and image segmentation are primarily used for identifying ROI and features.An image classification will then be implemented by combining a supervised learning model with a support vector machine.The proposed framework is developed based on MATLAB with a graphical user interface.According to the experimental results,the proposed framework can achieve 98.39%accuracy for classifying diseased and healthy leaves.Moreover,the framework can achieve an accuracy of 98.07%for classifying diseases on pomegranate leaves.

THE APPLICATION OF MULTILAYER FEEDFORWARD NETWORK FOR IMAGE SEGMENTATION

The multilayer feedforward network is used for image segmentation. This paper deals with the procedure of achieving the learning patterns and the method of improving the learning rate. The experiment shows that the image segmentation can get better result from using the multilayer feedforward network.

Hierarchical Image Segmentation Using a Combined Geometrical and Feature Based Approach

This paper presents a fully automatic segmentation algorithm based on geometrical and local attributes of color images. This method incorporates a hierarchical assessment scheme into any general segmentation algorithm for which the segmentation sensitivity can be changed through parameters. The parameters are varied to create different segmentation levels in the hierarchy. The algorithm examines the consistency of segments based on local features and their relationships with each other, and selects segments at different levels to generate a final segmentation. This adaptive parameter variation scheme provides an automatic way to set segmentation sensitivity parameters locally according to each region's characteristics instead of the entire image. The algorithm does not require any training dataset. The geometrical attributes can be defined by a shape prior for specific applications, i.e. targeting objects of interest, or by one or more general constraint(s) such as boundaries between regions for non-specific applications. Using mean shift as the general segmentation algorithm, we show that our hierarchical approach generates segments that satisfy geometrical properties while conforming with local properties. In the case of using a shape prior, the algorithm can cope with partial occlusions. Evaluation is carried out on the Berkeley Segmentation Dataset and Benchmark (BSDS300) (general natural images) and on geo-spatial images (with specific shapes of interest). The F-measure for our proposed algorithm, i.e. the harmonic mean between precision and recall rates, is 64.2% on BSDS300, outperforming the same segmentation algorithm in its standard non-hierarchical variant.

Medical ultrasound image segmentation by modified local histogram range image method

Fast and satisfied medical ultrasound segmentation is known to be difficult due to speckle noises and other artificial effects. Since speckle noise is formed from random signals which are emitted by an ultrasound system, we can’t encounter the same way as other image noises. Lack of information in ultrasound images is another problem. Thus, segmentation results may not be accurate enough by means of customary image segmentation methods. Those methods that can specify undesirable effects and segment them by eliminating artificial effects, should be chosen. It seems to be a complicated work with high computational load. The current study presents a different approach to ultrasound image segmentation that relies mainly on local evaluation, named as local histogram range image method which is modified by means of discrete wavelet transform. Thus, a significant decrease in computational load is then achieved. The results show that it is possible for tissues to be segmented correctly.

A Hierarchical Grab Cut Image Segmentation Algorithm

This paper aims to speed up a segmentation algorithm “Grab Cut” by separating the process of segmentation into hierarchical steps. The Grab Cut algorithm segments images by means of the color clustering concept and the process requires a lot of iteration for it to get converged. Therefore, it is a time-consuming process which we are interested in improving this process. In this study, we adopt the idea of hierarchical processing. The first step is to compute at low resolution to make the iteration much faster, and the second step use the result of the first step to carry on iteration at original resolution so that the total execution time can be reduced. Specifically speaking, segmentation of a low resolution image will lead to high-speed and similar-segmentation result to the segmentation at original resolution. Hence, once the iterations at low resolution have converged, we can utilize the parameters of segmentation result to initialize the next segmentation on original resolution. This way, the number of iteration of segmentation at original resolution will be reduced through the initialization of those parameters. Since the execution time of low resolution images is relatively short, the total hierarchical execution time will be reduced consequently. Also, we made a comparison among the four methods of reduction on image resolution. Finally, we found that reducing the number of basins by “Median Filter” resulted in best segmentation speed.

A novel stepwise thresholding for fuzzy image segmentation

A novel stepwise thresholding method for fuzzy image segmentation is proposed. Unlike the published iterative or recursive thresholding mehtods, this method segments regions into sub-regions iteratively by increasing threshold value in a stepwise manner, based on a preset intensity homogeneity criteria. The method is particularly suited to segmentation of the laser scanning confocal microscopy (LSCM) images, computerised tomography (CT) images, magnetic resonance (MR) images, fingerprint images, etc. The method has been tested on some typical fuzzy image data sets. In this paper, the novel stepwise thresholding is first addressed. Next a new method of region labelling for region extraction is introduced. Then the design of intensity homogeneity segmentation criteria is presented. Some examples of the experiment results of fuzzy image segmentation by the method are given at the end.

A Multi-Classifier Based Prediction Model for Phishing Emails Detection Using Topic Modelling, Named Entity Recognition and Image Processing

Phishing is the act of attempting to steal a user’s financial and personal information, such as credit card numbers and passwords by pretending to be a trustworthy participant, during online communication. Attackers may direct the users to a fake website that could seem legitimate, and then gather useful and confidential information using that site. In order to protect users from Social Engineering techniques such as phishing, various measures have been developed, including improvement of Technical Security. In this paper, we propose a new technique, namely, “A Prediction Model for the Detection of Phishing e-mails using Topic Modelling, Named Entity Recognition and Image Processing”. The features extracted are Topic Modelling features, Named Entity features and Structural features. A multi-classifier prediction model is used to detect the phishing mails. Experimental results show that the multi-classification technique outperforms the single-classifier-based prediction techniques. The resultant accuracy of the detection of phishing e-mail is 99% with the highest False Positive Rate being 2.1%.

Development and implementation of an automated system to aid laboratory diagnosis using image processing

The objective of this work is to provide an automatic system to count white blood cells in a blood smear. To do so an experiment was assembled, composed by a standard microscope with two step motors coupled to its knobs in order to move the microscope in x and y directions and a web cam which was mounted in the top of the microscope responsible for to acquire images from the smear. The step motors and the web cam are controlled by a microcomputer PC standard via software developed inDelphi. The motors use the parallel port to communicate with the PC and the camera use the USB port. The main idea is to set an initial point into the smear and the automated system will carry over the smear acquiring images (frames with 640 × 480 pixels) and counting the white blood cells encountered. The double histogram threshold technique is implemented to initially exclude the red cells from the image leaving only the white ones. Preliminaries results are obtained and show that the system is quite fast and has a good capacity of selection, even when different kinds of smear are used.

The Watershed Algorithm for Image Segmentation

This article introduced the watershed algorithm for the segmentation, illustrated the segmation process by implementing this algo-rithm. By comparing with another three related algorithm, this article revealed both the advantages and drawbacks of the watershed algorithm.

ED-Ged:Nighttime Image Semantic Segmentation Based on Enhanced Detail and Bidirectional Guidance

Semantic segmentation of driving scene images is crucial for autonomous driving.While deep learning technology has significantly improved daytime image semantic segmentation,nighttime images pose challenges due to factors like poor lighting and overexposure,making it difficult to recognize small objects.To address this,we propose an Image Adaptive Enhancement(IAEN)module comprising a parameter predictor(Edip),multiple image processing filters(Mdif),and a Detail Processing Module(DPM).Edip combines image processing filters to predict parameters like exposure and hue,optimizing image quality.We adopt a novel image encoder to enhance parameter prediction accuracy by enabling Edip to handle features at different scales.DPM strengthens overlooked image details,extending the IAEN module’s functionality.After the segmentation network,we integrate a Depth Guided Filter(DGF)to refine segmentation outputs.The entire network is trained end-to-end,with segmentation results guiding parameter prediction optimization,promoting self-learning and network improvement.This lightweight and efficient network architecture is particularly suitable for addressing challenges in nighttime image segmentation.Extensive experiments validate significant performance improvements of our approach on the ACDC-night and Nightcity datasets.

A Practical Study of Intelligent Image-Based Mobile Robot for Tracking Colored Objects

Object tracking is one of the major tasks for mobile robots in many real-world applications.Also,artificial intelligence and automatic control techniques play an important role in enhancing the performance of mobile robot navigation.In contrast to previous simulation studies,this paper presents a new intelligent mobile robot for accomplishing multi-tasks by tracking red-green-blue(RGB)colored objects in a real experimental field.Moreover,a practical smart controller is developed based on adaptive fuzzy logic and custom proportional-integral-derivative(PID)schemes to achieve accurate tracking results,considering robot command delay and tolerance errors.The design of developed controllers implies some motion rules to mimic the knowledge of experienced operators.Twelve scenarios of three colored object combinations have been successfully tested and evaluated by using the developed controlled image-based robot tracker.Classical PID control failed to handle some tracking scenarios in this study.The proposed adaptive fuzzy PID control achieved the best accurate results with the minimum average final error of 13.8 cm to reach the colored targets,while our designed custom PID control is efficient in saving both average time and traveling distance of 6.6 s and 14.3 cm,respectively.These promising results demonstrate the feasibility of applying our developed image-based robotic system in a colored object-tracking environment to reduce human workloads.

Enhanced Chimp Optimization Algorithm Using Attack Defense Strategy and Golden Update Mechanism for Robust COVID-19 Medical Image Segmentation

Medical image segmentation is a powerful and evolving technology in medical diagnosis.In fact,it has been identified as a very effective tool to support and accompany doctors in their fight against the spread of the coronavirus(COVID-19).Various techniques have been utilized for COVID-19 image segmentation,including Multilevel Thresholding(MLT)-based meta-heuristics,which are considered crucial in addressing this issue.However,despite their importance,meta-heuristics have significant limitations.Specifically,the imbalance between exploration and exploitation,as well as premature convergence,can cause the optimization process to become stuck in local optima,resulting in unsatisfactory segmentation results.In this paper,an enhanced War Strategy Chimp Optimization Algorithm(WSChOA)is proposed to address MLT problems.Two strategies are incorporated into the traditional Chimp Optimization Algorithm.Golden update mechanism that provides diversity in the population.Additionally,the attack and defense strategies are incorporated to improve the search space leading to avoiding local optima.The experimental results were conducted by comparing WSChoA with recent and well-known algorithms using various evaluation metrics such as Feature Similarity Index(FSIM),Structural Similarity Index(SSIM),Peak signal-to-Noise Ratio(PSNR),Standard deviation(STD),Freidman Test(FT),and Wilcoxon Sign Rank Test(WSRT).The results obtained by WSChoA surpassed those of other optimization techniques in terms of robustness and accuracy,indicating that it is a powerful tool for image segmentation.

Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation:A review

Recently,the development and application of lane line departure warning systems have been in the market.For any of the systems,the key part of lane line tracking,lane line identification,or lane line departure warning is whether it can accurately and quickly detect lane lines.Since 1990 s,they have been studied and implemented for the situations defined by the good viewing conditions and the clear lane markings on road.After then,the accuracy for particular situations,the robustness for a wide range of scenarios,time efficiency and integration into higher-order tasks define visual lane line detection and tracking as a continuing research subject.At present,these kinds of lane marking line detection methods based on machine vision and image processing can be divided into two categories:the traditional image processing and semantic segmentation(includes deep learning)methods.The former mainly involves feature-based and model-based steps,and which can be classified into similarity-and discontinuity-based ones;and the model-based step includes different parametric straight line,curve or pattern models.The semantic segmentation includes different machine learning,neural network and deep learning methods,which is the new trend for the research and application of lane line departure warning systems.This paper describes and analyzes the lane line departure warning systems,image processing algorithms and semantic segmentation methods for lane line detection.

Adaptive Boundary and Semantic Composite Segmentation Method for Individual Objects in Aerial Images

There are two types of methods for image segmentation.One is traditional image processing methods,which are sensitive to details and boundaries,yet fail to recognize semantic information.The other is deep learning methods,which can locate and identify different objects,but boundary identifications are not accurate enough.Both of them cannot generate entire segmentation information.In order to obtain accurate edge detection and semantic information,an Adaptive Boundary and Semantic Composite Segmentation method(ABSCS)is proposed.This method can precisely semantic segment individual objects in large-size aerial images with limited GPU performances.It includes adaptively dividing and modifying the aerial images with the proposed principles and methods,using the deep learning method to semantic segment and preprocess the small divided pieces,using three traditional methods to segment and preprocess original-size aerial images,adaptively selecting traditional results tomodify the boundaries of individual objects in deep learning results,and combining the results of different objects.Individual object semantic segmentation experiments are conducted by using the AeroScapes dataset,and their results are analyzed qualitatively and quantitatively.The experimental results demonstrate that the proposed method can achieve more promising object boundaries than the original deep learning method.This work also demonstrates the advantages of the proposed method in applications of point cloud semantic segmentation and image inpainting.

基于SAM&ImageJ图像处理的堆石混凝土坝层面露石率研究

堆石混凝土坝层面的外露块石为上下层提供了重要的啮合作用,其投影面积比例是科学评价层间抗剪性能的重要指标。采用国际最新Meta AI模型segment anything model(SAM)对层面外露堆石进行自动图像分割,并基于ImageJ软件对SAM识别后的图片进行再加工与图像计算,利用平滑、差分算法、中值滤波等方法精准标定外露堆石,二值化后计算得到层面露石率。结果表明:SAM图像预分割可识别约90%的外露堆石,经过ImageJ二次图像处理后可有效提高小粒径堆石的识别精度,对比手动标注结果误差在±3%以内。以贵州省两座水库的工程应用为例,对浇筑仓面进行分区预处理,结果发现靠近上游、中部、下游不同区域的露石率差别较大,计算得到的层面露石率以10%~30%居多,其中堆石入仓运输通道区域的露石率较低。研究内容与结论可为堆石混凝土结构层间界面抗剪力学性能和大坝蓄水安全稳定的研究提供参考与借鉴。