Integrating image processing in PACS

We present MOPEX - a software package for astronomical image processing and display. The package is a combination of command-line driven image processing software written in C/C++ with a Java-based GUI. The main image processing capabilities include creating mosaic images, image registration, background matching, point source extraction, as well as a number of minor image processing tasks. The combination of the image processing and display capabilities allows for much more intuitive and efficient way of performing image processing. The GUI allows for the control over the image processing and display to be closely intertwined. Parameter setting, validation, and specific processing options are entered by the user through a set of intuitive dialog boxes. Visualization feeds back into further processing by providing a prompt feedback of the processing results. The GUI also allows for further analysis by accessing and displaying data from existing image and catalog servers using a virtual observatory approach. Even though originally designed for the Spitzer Space Telescope mission, a lot of functionalities are of general usefulness and can be used for working with existing astronomical data and for new missions. The software used in the package has undergone intensive testing and benefited greatly from effective software reuse. The visualization part has been used for observation planning for both the Spitzer and Herschel Space Telescopes as part the tool Spot. The visualization capabilities of Spot have been enhanced and integrated with the image processing functionality of the command-line driven MOPEX. The image processing software is used in the Spitzer automated pipeline processing, which has been in operation for nearly 3 years. The image processing capabilities have also been tested in off-line processing by numerous astronomers at various institutions around the world. The package is multi-platform and includes automatic update capabilities. The software package has been developed by a small group of software developers and scientists at the Spitzer Science Center. It is available for distribution at the Spitzer Science Center web page.

At the nexus of artificial intelligence, robotics, image processing, and natural language processing (NLP), quadruped AI assistants provide a revolutionary method of interacting with machines. Intending to give quadruped AI assistants emotional intelligence, this literature review methodically looks over and compiles the body of research, concentrating on how to use natural language processing and image processing together. The review seeks to offer a comprehensive grasp of the developments, challenges, and possible uses in this diverse subject. The initial section of the survey provides an overview of quadruped robots and highlights how they are integrated with image processing technology to provide visual perception. It explores the various locomotion techniques, demonstrating how these robots use picture data to improve their capacity to navigate and adapt to different situations. The conversation also touches on sensor technologies, highlighting their function in obtaining and deciphering visual data for intelligent interaction. In addition, the research delves into how quadruped AI assistants include natural language processing and examines how these robots interpret and react to instructions in human language. One of the main topics of debate is how sentiment analysis and emotional recognition methods might help these assistants become more emotionally intelligent. Finally, this review of the literature offers a comprehensive viewpoint on how natural language processing and image processing are integrated in quadruped AI assistants, providing academics and practitioners with an outline for developing emotional intelligence in these robots. Compiling knowledge from robotics, artificial intelligence, image processing, and natural language processing is essential to creating emotionally competent quadruped AI assistants and ensuring their smooth incorporation into human-centered settings.This research also investigates the integration of Natural Language Processing and Image Processing for Emotional Intelligence in Quadruped AI Assistants and thoroughly assesses the gyroscope functioning on these robotic systems

The recent advancements in integrating image processing with audio extraction have provided a new dimension to emotion and fluency assessment. This paper proposes a new system based on advanced image processing algorithms and audio extraction methods to perform states of emotion and fluent speech analysis. The designed system utilizes Gabor filters – an efficient texture representation and feature extraction method for facial expressions-based systems – to analyze facial movements that comprise particular emotions. It applies the Haar cascade classifier for practical yet straightforward facial detection from the system’s target image. As for the sound characterization, MFCC is employed to extract the emotional content of the voice and its effectively connected speech. The prepared information is processed further through a set of machine-learning techniques. Logistic regression offers a classic classifier for the first emotion categorization. Convolutional neural networks are utilized for one of the DNN sections because of their ability to recognize and learn complicated patterns in image and sound. Using random forest algorithms in the system improves the accuracy and robustness of the model by combining many decision trees, improving the predictive performance. The results indicate that the system efficiently recognizes different emotional states and changes in fluency levels. Hence, it is helpful in mental health, education, etc. In the coming years, the research development is focused on improving the system’s precision by additional models alongside increasing the scope of the system to ordinary day situations that require multilingual and multimodal analysis.

This paper describes the design and implementation of an OMAP3530-based networked control system which has real-time image processing capability. It integrates an ARM core and a DSP core in the controller. The ARM core with Linux operating system is responsible for applying functional services and the DSP core is responsible for executing image processing algorithms. The due-core structure makes the novel controller available for advanced control and image processing so that the designed control system expands the application of the existed networked controllers to machine vision. Finally, a reliable application for vision-based mobile robot localization demonstrates the effectiveness of the designed system.

The primary purpose of this paper is to elaborate upon and to take a step ahead on the research done in the field of Image Processing with a focus on Early Tumor Detection through the use of Magnetic Resonance Imaging (MRI) and Image Processing Tool Box of MATLAB.The technique which the author proposed is Morphological Reconstruction Based Segmentation, used to segment the solid cum cystic tumor of brain and is suggested after testing and observing various available methods and algorithms.The proposed method shows more precision amongst others and the processing time is also fast.

Today, agriculture faces many challenges, such as the use of inefficient methods that affect crop quality. Precision agriculture (PA), combined with advanced technologies, improves monitoring, while the integration of wireless communication optimizes processes and resources. This work presents the design of a communication prototype applied in precision agriculture, which allows the acquisition, processing, and wireless transmission of information extracted from the Cotonet pest to The Things Network (TTN) cloud server. This prototype integrates technologies and protocols such as LoRaWAN, Message Queuing Telemetry Transport (MQTT), Internet of Things (IoT) sensors, and Computer Vision. This prototype employs a robust processing and segmentation algorithm, which allows the recognition of pests in citrus plants based on color. The results show that lighting conditions, weather, and time of day influence the quality of the captured images. The relationship between image resolution, brightness, and processing time shows that higher-resolution images (1920 × 1080 pixels per image) provide better detection of pest pixels (greater than 50% of the pest index) but require longer processing time (28.415 ms on average). Furthermore, the developed system effectively detects an index of affection of Planococcus citri (Cotonet) in agricultural plantations through an end-to-end technological implementation that integrates image processing, wireless communication, and IoT technologies.

Describes a study of two fuzzy based segmentation algorithms to model successfully ambiguities and uncertainties inherent in assigning an image pixel to an object of interest and to help choosing the threshold for binarizing images using the index of fuzziness and fuzzy compactness. The methods detect object contours to different degrees of certainty on the basis of the information provided by the user, who define a reference point inside the object around which is construct the contours. First, we obtain a connectedness or a similarity map related to the indicated reference point and then for the pixels surrounding that point, if their membership-values are high they are regarded as being inside the object. We also present the connectedness and similarity maps as a useful tool in image processing, so as to obtain better gradient images.

Directional filtering is commonly known as a technique for selective linear enhancement. In addition to this traditional utility, we present in this paper some examples demonstrating that it can be used as a multi-purpose tool in image processing, by adjusting its parameters. These examples show that: (1) applying directional filtering in conjugate directions simultaneously can lead to the enhancement of specific conjugate linear features on an image, which is useful in interpretation of geophysical and Landsat images, satellite and aerial photographs that contain many conjugate linear features; (2) directional filtering can be used as illuminating sources to produce shaded relief images that show 3D effect; by combining the directional filtered result with its original image, this 3D effect becomes much more profound; (3) the directional filtered result can be weighted by different factors, which allows some subtle features on an image to be identified.

Image processing plays an important role in computer science, making complex image manipulation more feasible. It can be used either in a general manner or in specific domains such as for medical purposes. With the availability of the internet, image processing applications can be distributed to be available for different people, regardless of their geographical location. Although there are a handful of professional image processing solutions available, there is no well-designed framework that can be simply distributed and customized to be used in specific areas. Other than image processing tools, image storage and retrieval is another issue that needs to be addressed. This research proposes a general platform that can be used for implementing a distributed image processing framework. It provides solutions for storing and retrieving images, particularly large-size ones as well as an approach for executing image processing functions that are physically located anywhere within the distributed system.

Artificial intelligence for medical imaging: U-Net technology for anatomical feature analysis

The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing, as well as robotic and biological visions. The designs for CNN templates are one of the important i ssues for the practical applications of CNNs. This paper combines two CNN to implement the D ilation CNNs and Erosion CNN for gray scale images and proposes two theorems of robustness designs. The parameters of the templates can range between a hyper plane and a hyper surface in the first quartile. The simulations have been given. The results show the effectiveness of the theoretical results to be implemented in computer simulations. Keywords: Cellular neural network, Robust Design, Dilation, Erosion. 1. INTRODUCTION The CNN was firstly introduced by Chua & Yang [1,2] in 1988. Its original intention is to find out a structure of neural network easier to implement than the Hopfield neural networks[3], which requires fully-connected and grows exponentially with the size of the array. Now CNN has played important roles in many fields such as image and video signal processing, robotic and biological visions, and data prediction[3-8]. In an analog cellular neural network, the parameter levers usually have 5% ý 10% of perturbation [9]. So the robustness designs for CNN template parameters are important for the practical applications of CNN. Chua and Dogaru [3,5] have studied the robust designs of a large kind of CNN-uncoupled Boolean CNNs, which provides optimal design schemes for CNNs with prescribed tasks. Since then, some robust designs for uncoupled and coupled CNNs have been studied (for example see [10-20]), which have been used in image processing and pattern recognitions. Dilation CNN and Erosion CNN for binary image is proposed in Chua and Roska’s book[6]. Then Shu Jian[21] et al. designed the robust of them and proposed two theorems to guarantee the parameters of the templates can perform prescribed dilation or erosion CNN. The pa rameters of templates in [21] will range according the image. In this paper, we separate the original local rules of dilation or erosion CNN into two local rules, then we set up two parameters inequalities of templates to perform each local rules. The parameters of our templates need not range according the image, and parameters values of each template range between one hyper plane and hyper surface. The rest of this paper has organized as follows. Section 2.1 introduces the robust designs of the CNN. Section 2.2 describes and proves two local rules of the Combination C NN which implements the dilati on CNN for gray scale image. Section 2.3 describes and proves two local rules of the Combination CNN which implements erosion CNN for gray scale image. Section 3 gives the examples of the combination CNN. Conclusions have been addressed in Section 4.

We present a data mining process for discovering associations between structures and functions of the human brain. Our approach is through the study of lesioned (abnormal) structures and associated functional deficits (disorders). For this purpose we have developed a BRAinImage Database (BRAID) that integrates image processing and visualization capabilities with statistical analysis of spatial and clinical data, providing access via extended SQL through a web interface. We present visualization and statistical methods for mining lesion-deficit associations. We consider issues of scalability and morphological variability. We demonstrate the use of the proposed mining methods by applying them to epidemiological data finding clinically meaningful associ-

Pores are inherent to additively manufactured components and critical especially in technical components. Since they reduce the component's fatigue life, a reliable identification and description of pores is vital to ensure the component's performance. X‐ray computed tomography (CT) is an established and non‐destructive testing method to investigate internal defects. The CT scan process can induce noise and artefacts in the resulting images which afterwards have to be reduced through image processing. To reconstruct the internal defects of a component, the images need to be segmented in defect region and bulk material by applying a threshold. The application of the threshold as well as the previous image processing alter the geometry and size of the identified defects. This contribution aims to quantify the influence of selected commercial image processing and segmentation methods on identified pores in several additively manufactured components made of AlSi10Mg and Ti6Al4V as well as in an artificial CT scan. To that aim, gray value histograms and characteristic parameters thereof are compared for different image processing tools. After the segmentation of the processed images, particle characteristics are compared. The influence of image processing and segmentation on the predicted fatigue life of the material is evaluated through the change of the largest pore in each set of data applying Murakami's empirical ‐parameter model.

BackgroundIn vivo multiphoton imaging and automatic 3D image processing tools provide quantitative information on human skin constituents. These multiphoton‐based tools allowed evidencing retinoids epidermal effects in the occlusive patch test protocol developed for antiaging products screening. This study aimed at investigating their relevance for non‐invasive, time course assessment of retinoids cutaneous effects under real‐life conditions for one year.Materials and MethodsThirty women, 55‐65 y, applied either retinol (RO 0.3%) or retinoic acid (RA 0.025%) on one forearm dorsal side versus a control product on the other forearm once a day for 1 year. In vivo multiphoton imaging was performed every three months, and biopsies were taken after 1 year. Epidermal thickness and dermal‐epidermal junction undulation were estimated in 3D with multiphoton and in 2D with histology, whereas global melanin density and its z‐epidermal distribution were estimated using 3D multiphoton image processing tools.ResultsMain results after one year were as follows: a) epidermal thickening with RO (+30%); b) slight increase in dermal‐epidermal junction undulation with RO; c) slight decrease in 3D melanin density with RA; d) limitation of the melanin ascent observed with seasonality and time within supra‐basal layers with both retinoids, using multiphoton 3D‐melanin z‐epidermal profile.ConclusionsWith a novel 3D descriptor of melanin z‐epidermal distribution, in vivo multiphoton imaging allows demonstrating that daily usage of retinoids counteracts aging by acting not only on epidermal morphology, but also on melanin that is shown to accumulate in the supra‐basal layers with time.

Abstract. As remote sensing technology advances, imagery becomes one of the most important data sources for GIS applications –as it can quickly provide the most current and most detailed information with high resolution image data. Traditional image processing workflow becomes inefficient in GIS applications especially in the case of disaster analysis. This paper introduces two ArcGIS data models that are designed for on-demand image processing in managing imagery: raster product and mosaic dataset. A raster product allows you to access single scene remote sensing image products dynamically such as a pan-sharpened and orthorectified GeoEye image or multi-spectral Landsat scene with a single drag and drop to the map display. Mosaic datasets allow you to catalog a large collection of images from many sensor platforms, process them on-the-fly and dynamically mosaic the images together. The key concept – the raster function that is used to implement on-the-fly image processing capabilities – will also be discussed.