Cell identification using image analysis: A literature survey

Medical image processing and analysis techniques play a significant role in diagnosing diseases. Thus, during the last decade, several noteworthy improvements in medical diagnostics have been made based on medical image processing techniques. In this article, we reviewed articles published in the most important journals and conferences that used or proposed medical image analysis techniques to diagnose diseases. Starting from four scientific databases, we applied the PRISMA technique to efficiently process and refine articles until we obtained forty research articles published in the last five years (2017–2021) aimed at answering our research questions. The medical image processing and analysis approaches were identified, examined, and discussed, including preprocessing, segmentation, feature extraction, classification, evaluation metrics, and diagnosis techniques. This article also sheds light on machine learning and deep learning approaches. We also focused on the most important medical image processing techniques used in these articles to establish the best methodologies for future approaches, discussing the most efficient ones and proposing in this way a comprehensive reference source of methods of medical image processing and analysis that can be very useful in future medical diagnosis systems.

Techniques of medical image processing and analysis play a crucial role in many clinical scenarios, including in diagnosis and treatment planning. However, immense quantities of data and high complexity of the algorithms often used are computationally demanding. As a result, there now exists a wide range of techniques of medical image processing and analysis that require the application of high-performance computing solutions in order to reduce the required runtime. The main purpose of this review is to provide a comprehensive reference source of techniques of medical image processing and analysis that have been accelerated by high-performance computing solutions. With this in mind, the articles available in the Scopus and Web of Science electronic repositories were searched. Subsequently, the most relevant articles found were individually analyzed in order to identify: (a) the metrics used to evaluate computing performance, (b) the high-performance computing solution used, (c) the parallel design adopted, and (d) the task of medical image processing and analysis involved. Hence, the techniques of medical image processing and analysis found were identified, reviewed, and discussed, particularly in terms of computational performance. Consequently, the techniques reviewed herein present the progress made so far in reducing the computational runtime involved, and the difficulties and challenges that remain to be overcome.

Human kind is exposed to large amounts of data. According to statistics, more than 80% of information received by humans comes from the visual system. Therefore, image information processing is not only an important research topic but also a challenging task. The unique information processing mechanism of the human visual system provides it with fast, accurate, and efficient image processing capabilities. At present, many advanced image analysis and processing techniques have been widely used in image communication, geographic information systems, medical image analysis, and virtual reality. However, there is still a large gap between these technologies and the human visual system. Therefore, building an image system research mechanism based on the biological vision system is an attractive but difficult target. Although it is a challenge, it can also be considered as an opportunity which utilizes biologically inspired ideas. Meanwhile, through the integration of neural biology, biological perception mechanisms, and computer science and mathematical science, related research can bridge biological vision and computer vision. Finally, the biologically inspired image analysis and processing system is expected to be built on the basis of further consideration of the learning mechanism of the human brain.

Image processing is a method for enhancing unprocessed images from cameras on aircraft, spacecraft, and satellites as well as images taken regularly for a variety of uses. In general, the following strategies can be used to categorize all image processing operations: Images are represented in several ways, which are referred to as image representation, image preprocessing, image enhancement, image restoration, image analysis, picture reconstruction, and image data compression. The first radiometric normalization, geometric distortion correction, and noise removal of the raw image data been discussed in the past. The goal of the information extraction procedures is to automate the identification of tone in a scene by replacing the visual examination of image data with quantitative techniques. This entails analyzing multispectral image data and establishing the earth cover identification of each pixel in an image using statistically based decision procedures. The goal of the classification procedure is to sort all of the pixels in a digital image into one of several different earth cover classes or themes. The purpose of this study is to examine various image processing approaches and algorithms, many sorts of image processing algorithms: Optical Character Recognition (OCR) and Supporting Vector Machine (SVM) a feature extraction technique, on the vehicle classification dataset and had accurate results of 90% for SVM and 95% for OCR, to further improve the performance of machine algorithms in terms of accuracy for image processing technique using a vehicle. This study can be used for vehicle classification research, it also advances and improves the performance of the system in terms of accurate detection.

Chapter 46 - Medical Image Processing Using GPU-Accelerated ITK Image Filters

Cancer has become very common in this evolving world. Technology advancements, increased radiations have made cancer a common syndrome. Various types of cancers like Skin Cancer, Breast Cancer, Prostate Cancer, Blood Cancer, Colorectal cancer, Kidney Cancer and Lung Cancer exits. Among these various types of cancers, the mortality rate is high in lung cancer which is tough to diagnose and can be diagnosed only in advanced stages. Small cell lung cancer and non-small cell lung cancer are the two types in which non-small cell lung cancer (NSCLC) is the most common type which makes up to 80 to 85 percent of all cases [1]. Digital Image Processing and Artificial Intelligence advancements has helped a lot in medical image analysis and Computer Aided Diagnosis(CAD). Numerous research is carried out in this field to improve the detection and prediction of the cancerous tissues. In current methods, traditional image processing techniques is applied for image processing, noise removal and feature extraction. There are few good approaches that applies Artificial Intelligence and produce better results. However, no research has achieved 100% accuracy in nodule detection, early detection of cancerous nodules nor faster processing methods. Application of Artificial Intelligence techniques like Machine Learning, Deep Learning is very minimal and limited. In this paper [Figure 1], we have applied Artificial intelligence techniques to process CT (Computed Tomography) Scan image for data collection and data model training. The DICOM image data is saved as numpy file with all medical information extracted from the files for training. With the trained data we apply deep learning for noise removal and feature extraction. We can process huge volume of medical images for data collection, image processing, detection and prediction of nodules. The patient is made well aware of the disease and enabled with their health tracking using various mobile applications made available in the online stores for iOS and Android mobile devices.

Chapter 11 - Use of artificial intelligence in Alzheimer’s disease detection

Plant leaf diseases pose significant challenges to agricultural productivity, necessitating early detection and intervention strategies. Recent research has increasingly turned to artificial intelligence (AI) techniques, particularly image processing and deep learning, to address these challenges. The paper provides a technical view of the application of Artificial Intelligence in plant leaf disease detection. Key aspects covered include datasets, image processing methodologies, deep learning algorithms, and evaluation metrics commonly utilized in this domain. An emphasis is placed on the important role of annotated datasets in the training of AI models, facilitating the development of accurate classification systems. Image processing techniques, encompassing tasks such as enhancement, segmentation, and feature extraction, are discussed for their role in preprocessing leaf images to extract salient features. Deep learning algorithms, notably convolutional neural networks (CNNs), are highlighted for their effectiveness in automatically learning discriminative features from input images and achieving high accuracy rates in disease classification tasks. The paper illuminates the significance of employing evaluation metrics such as accuracy, precision, recall, and F1 score as quantitative benchmarks to gauge the efficacy of model performance. It highlights the triumphs observed across diverse studies harnessing AI methodologies for the detection of plant leaf diseases, emphasizing their transformative capacity in reshaping agricultural management paradigms. Future research directions are outlined, including the exploration of transfer learning methods to capitalize on pre-trained models and the integration of additional sensing technologies like hyperspectral imaging and drones for enhanced disease detection capabilities. These advancements are poised to further refine the accuracy and efficiency of plant leaf disease detection systems, thereby offering substantial benefits to agriculture and food production sectors.

The aim of this study is to evaluate infected leaf disease images. Precision agriculture's automatic leaf disease detection system employs image acquisition, image processing, image segmentation, feature extraction, and machine learning techniques. An automated disease detection system offers the farmer with a fast and accurate diagnosis of the plant disease. Automation of plant leaf disease detection system is essential for accelerating crop diagnosis. Using machine learning and image processing, this paper describes a framework for detecting leaf illness. An image of a leaf can be used as an input for this framework. To begin, leaf photographs are preprocessed in order to remove noise from their images. The mean filter is used to filter out background noise. Histogram equalization is used to enhance the quality of the image. The division of a single image into multiple portions or segments is referred to as segmentation in photography. It assists in establishing the boundaries of the image. Segmenting the image is accomplished using the K-Means approach. Feature extraction is carried by using the principal component analysis. Following that, images are categorized using techniques such as RBF-SVM, SVM, random forest, and ID3.

A deep analysis of the image and video processing techniques using nanoscale quantum-dots cellular automata

Now a days, skin diseases are mostly found in animals, humans and plants. A skin disease is a particular kind of illness caused by bacteria or an infection. These diseases like alopecia, ringworm, yeast infection, brown spot, allergies, eczema etc. have various dangerous effects on the skin and keep on spreading over time. It becomes important to identify these diseases at their initial stage to control it from spreading. These diseases are identified by using many technologies such as image processing, data mining, artificial neural network (ANN) etc. Recently, image processing has played a major role in this area of research and has widely used for the detection of skin diseases. Techniques like filtering, segmentation, feature extraction, image pre-processing and edge detection etc. are part of image processing and are used to identify the part affected by disease, the form of affected area, its affected area color etc. This paper presents a survey of various skin disease diagnosis systems using image processing techniques in recent times. A comprehensive study of a number of skin disease diagnosis systems are done in this paper, with different methodologies and their performances.

The project "Transforming Expression into Language: American Sign Language Recognition using Image Processing" intends to employ image processing techniques to identify gestures in American Sign Language (ASL). The goal of this project is to develop an efficient system for translating ASL signs using visual cues generated from hand motions and gestures. The program intends to use image processing technology to bridge the communication gap between ASL users and nonusers. The use of image processing for ASL identification may improve communication in a range of situations while also increasing accessibility for the deaf community. The paper provides a realistic approach to real-time ASL motion interpretation using image processing techniques, thereby advancing the field of sign language recognition technology. KEYWORDS ASL Recognition, Image Processing Techniques, Convolutional Neural Networks (CNNs), Feature Extraction in Image Processing, Transforming Expressions into Text

The increased availability of smartphones has made it easier for taking technology to every individual. Technology can play important role in the field of agriculture to improve outcomes. Plant disease is one of the important reasons for the decrement of yield. Bridging technology with agriculture can give revolutionary change. It is challenging to monitor the disease of plants manually. It consumes important time, resources, and need efforts. Hence faster image processing technique is used as a solution for it. Disease detection using image processing involves multiple steps like the acquisition of images, pre-processing, segmentation, feature extraction, and classification. This paper discusses a faster image processing technique using the KNN method. This algorithm gives an asymptotically faster method for image processing.

This thesis aims to develop an embedded image processing board, which can be used to realize various image feature extraction and processing algorithms for robotic applications. The circuitry of this image board can be divided into two parts, one is for a digital signal processor (DSP) and the other for a field programmable gate array (FPGA). A low power, small size CMOS image sensor with YUV4:2:2 image format and 512x480 pixel size is adopted for image acquisition. The DSP module is mainly used for implementing image processing algorithms, while the FPGA module for lower level image processing and sensor control functions. In this work, the TMS320C6414TGLZ7 DSP from Texas Instruments Corporation and the EP2C35F672C6 FPGA from Altera Corporation were selected for the image board. In this work, speeded-up robust features (SURF) algorithm has been successfully realized on the developed image board. An USB interface with average throughput of 22MB/sec works to transfer acquired image frames and extracted SURF descriptors to a personal computer, where feature matching is executed. Experimental results show that the refresh period of frame grabbing is about 33.5ms. When the number of SURF descriptor is equal to 64, the computing time is about 417.5ms; when the number of SURF descriptor is equal to 199, the computing time is about 985ms. The typical matching rate varies from 70.2% to 20%, depending on the threshold of the patterns used in feature extraction.

The present report is devoted to the study of distinct ionization waves in terms of temporally-, spatially-, and wavelength-resolved analyses. The study is based on the technique of two-dimension fast imaging. However, appropriately selected ultraviolet to near infrared optical filters are employed to capture the propagation of specific species and digital image processing techniques are applied to explore the recorded snapshots. N2(SPS), (FNS), He I, OH(A–X), and O I, i.e., emissive neutral and ionic species, are investigated. On the other hand, the propagation of the NO γ species is studied by means of laser-induced fluorescence spectroscopy, since the light intensity due to spontaneous emission of this species was not readily detectable. Digital image processing techniques are also applied for the NO γ case. The crucial role of the above species in fields like plasma biomedicine and material processing is extensively recognized, and the present work: (i) provides gathered information on the propagation of these species within the atmospheric air; and (ii) introduces image processing algorithms to extract information which otherwise would remain hidden or uncorrelated with other plasma parameters. The present results unveil specific emission patterns due to the propagation of the N2(SPS), (FNS), He I, OH(A–X), and O I species. The intensity patterns consist of a first peak located in the vicinity of the reactor orifice, a second peak moving away from the reactor orifice, and a continuum which couples the two peaks. However, the detailed features of each pattern depend on the type of species considered. The fluorescence of the NO γ species also suggests the division of the area downstream of the reactor orifice into two regions where distinct ionization-excitation effects take place. Propagation speeds of the species up to about 2 × 105 m s−1 were measured. Finally, qualitative correlation between the species propagation (as it is pronounced by the emission patterns) and the local electric field (as it was numerically calculated in a similar setup) is demonstrated.