Image Segments Research Articles

Development of an automated combined positive score prediction pipeline using artificial intelligence on multiplexed immunofluorescence images

Immunotherapy targeting immune checkpoint proteins, such as programmed cell death ligand 1 (PD-L1), has shown impressive outcomes in many clinical trials but only 20%–40% of patients benefit from it. Utilizing Combined Positive Score (CPS) to evaluate PD-L1 expression in tumour biopsies to identify patients with the highest likelihood of responsiveness to anti-PD-1/PD-L1 therapy has been approved by the Food and Drug Administration for several solid tumour types. Current CPS workflow requires a pathologist to manually score the two-colour PD-L1 chromogenic immunohistochemistry image. Multiplex immunofluorescence (mIF) imaging reveals the expression of an increased number of immune markers in tumour biopsies and has been used extensively in immunotherapy research. Recent rapid progress of Artificial Intelligence (AI)-based imaging analysis, particularly Deep Learning, provides cost effective and high-quality solutions to healthcare. In this article, we propose an imaging pipeline that utilizes three-colour mIF images (DAPI, PD-L1, and Pan-cytokeratin) as input and predicts the CPS using AI techniques. Our novel pipeline is composed of three modules employing algorithms of image processing, machine learning, and deep learning techniques. The first module of quality check (QC) detects and removes the image regions contaminated with sectioning and staining artefacts. The QC module ensures that only image regions free of the three common artefacts are used for downstream analysis. The second module of nuclear segmentation uses deep learning to segment and count nuclei in the DAPI images wherein our specialized method can accurately separate touching nuclei. The third module of cell phenotyping calculates CPS by identifying and counting PD-L1 positive cells and tumour cells. These modules are data-efficient and require only few manual annotations for training purposes. Using tumour biopsies from a clinical trial, we found that the CPS from the AI-based models shows a high Spearman correlation (78%, p = 0.003) to the pathologist-scored CPS.

Review on Region-Based Segmentation Using Watershed and Region Growing Techniques and their Applications in Different Fields

In digital image processing and computer vision, segmentation operation for an image refers to dividing an image into multiple image segments, and the significant purpose of segmentation operation is to depict an image in a way so that the analysis process of the objects of interest is easier and more accurate. The region-based segmentation scheme act for finding similarities between adjacent pixels to detect each region that constructs the image. Similarity scales have based on different features, in a grayscale image, the scale may be referred to as textures and other spatial appearances, and also the variance in intensity of a region and so on. Significantly, many applications in different fields involved region-based segmentation for instance remote sensing, medical application, and others for recognizing interesting objects in an image. In this paper, two techniques for segmentation operation in region-based which are region growing and watershed are reviewed.

Machine learning based skin lesion segmentation method with novel borders and hair removal techniques.

The effective segmentation of lesion(s) from dermoscopic skin images assists the Computer-Aided Diagnosis (CAD) systems in improving the diagnosing rate of skin cancer. The results of the existing skin lesion segmentation techniques are not up to the mark for dermoscopic images with artifacts like varying size corner borders with color similar to lesion(s) and/or hairs having low contrast with surrounding background. To improve the results of the existing skin lesion segmentation techniques for such kinds of dermoscopic images, an effective skin lesion segmentation method is proposed in this research work. The proposed method searches for the presence of corner borders in the given dermoscopc image and removes them if found otherwise it starts searching for the presence of hairs on it and eliminate them if present. Next, it enhances the resultant image using state-of-the-art image enhancement method and segments lesion from it using machine learning technique namely, GrabCut method. The proposed method was tested on PH2 and ISIC 2018 datasets containing 200 images each and its accuracy was measured with two evaluation metrics, i.e., Jaccard index, and Dice index. The evaluation results show that our proposed skin lesion segmentation method obtained Jaccard Index of 0.77, 0.80 and Dice index of 0.87, 0.82 values on PH2, and ISIC2018 datasets, respectively, which are better than state-of-the-art skin lesion segmentation techniques.

Abstract 14241: Feasibility and Safety of Non-Contrast Optical Coherence Tomography Imaging Using Hydroxyethyl Starch in Coronary Arteries

Background/Objectives: Contemporary high-resolution intracoronary optical coherence tomography (OCT) requires injection of flushing media for image acquisition. There is a growing need for an alternative flushing media other than iodine contrast to reduce the risk of acute kidney injury. To address this issue, we investigated the safety and feasibility of hydroxyethyl starch (pentastarch) as a flushing media for OCT image acquisition and compared with the contrast media flushed images. Methods: We prospectively enrolled 43 patients with 70 coronary lesions (46 stented and 24 pre-stented or non-culprit lesions). Paired OCT images were obtained by manual injection of iodine contrast (average 12 mL), followed by pentastarch (average 15 mL). A total of 162 runs (81 pairs) were analyzed and each OCT pullback was assessed on a frame-by-frame basis by automated customized lumen contour and stent strut segmentation algorithm. Results: Image quality as assessed by proportion of clear image segments were comparable between the contrast media and pentastarch flushed images (93.3% vs. 93.7%; p=0.501). Pixel-based flushing capability showed no significant difference between the paired image segments (median(IQR), 0.951(0.947-0.953) vs. 0.951(0.949-0.952), p=0.264; Figure). Quantitative measurements of minimal stent area (R 2 =0.977, p<0.001), minimal lumen area (R 2 =0.983, p<0.001) correlated well between the paired OCT runs. There were no clinically relevant complications associated with OCT imaging with pentastarch such as anaphylaxis, life-threatening arrhythmia, or acute renal deterioration. Conclusions: Intracoronary OCT using pentastarch is safe and technically feasible without affecting the optical signal intensity and overall image quality. Non-contrast imaging with pentastarch significantly reduces the obligatory iodine contrast load, therefore, could be considered as a promising strategy in high-risk patients for renal impairment.

Manipulation and generation of synthetic satellite images using deep learning models

Generation and manipulation of digital images based on deep learning (DL) are receiving increasing attention for both benign and malevolent uses. As the importance of satellite imagery is increasing, DL has started being used also for the generation of synthetic satellite images. However, the direct use of techniques developed for computer vision applications is not possible, due to the different nature of satellite images. The goal of our work is to describe a number of methods to generate manipulated and synthetic satellite images. To be specific, we focus on two different types of manipulations: full image modification and local splicing. In the former case, we rely on generative adversarial networks commonly used for style transfer applications, adapting them to implement two different kinds of transfer: (i) land cover transfer, aiming at modifying the image content from vegetation to barren and vice versa and (ii) season transfer, aiming at modifying the image content from winter to summer and vice versa. With regard to local splicing, we present two different architectures. The first one uses image generative pretrained transformer and is trained on pixel sequences in order to predict pixels in semantically consistent regions identified using watershed segmentation. The second technique uses a vision transformer operating on image patches rather than on a pixel by pixel basis. We use the trained vision transformer to generate synthetic image segments and splice them into a selected region of the to-be-manipulated image. All the proposed methods generate highly realistic, synthetic, and satellite images. Among the possible applications of the proposed techniques, we mention the generation of proper datasets for the evaluation and training of tools for the analysis of satellite images.

Correction method for 3D non-linear drift distortions in atomic force microscopy raster measurements

A method to correct non-linear drift distortions in all three coordinate axes of atomic force microscope (AFM) images is presented. The method uses two measurements of the sample with two fast scan axes orthogonal to each other. Both AFM images are divided into segments and the shifts of the surface features of the segments of both images are determined. From these shifts subsequently the drift of both measurements is calculated. Depending on the segments used, significant non-linearities of the drift can be corrected. The two required measurements for this method do not have to be carried out in direct succession. With this method it is therefore possible to correct drift in an existing AFM image by measuring the sample again later. Although the method has been developed for AFM, it can also be used for other scanning probe microscopes.

Serum myostatin level is associated with myocardial scar burden by SPECT myocardial perfusion imaging

BackgroundMyostatin is a negative regulator of skeletal muscle and myocardial mass. The relationship between myocardial ischemia and scar burden and levels of myostatin has not been established. MethodsWe enrolled symptomatic patients with heart failure (HF) with a left ventricular ejection fraction (LVEF) < 45 % and controls (LVEF ≥ 45 %). Serum levels of myostatin were measured. Single-photon emission computed tomography (SPECT) imaging was interpreted, and summed scores of 17 stress and rest image segments in 5-point scale produced the summed stress score (SSS) and summed rest score (SRS), respectively. The summed difference score (SDS) was calculated as the difference between the SSS and SRS. ResultsNinety-six patients with HF and 103 controls were enrolled. The levels of myostatin were higher in the HF group. In linear regression analysis, myostatin level was positively correlated with SSS (β coefficient = 6.840, p = 0.013) and SRS (β coefficient = 5.557, p = 0.026), but not correlated with SDS. The areas under the receiver operating characteristic curves of myostatin levels for SSS ≥ 8 and SSS ≥ 13 were 0.6813 ± 0.04854 (p < 0.001) and 0.7498 ± 0.04402 (p < 0.001), respectively. ConclusionSerum levels of myostatin were correlated with the extent of myocardial scarring as defined by SPECT imaging. Serum levels of myostatin may be a predictor of myocardial scar burden.

A goal-driven unsupervised image segmentation method combining graph-based processing and Markov random fields

Image segmentation is the process of partitioning a digital image into a set of homogeneous regions (according to some homogeneity criterion) to facilitate a subsequent higher-level analysis. In this context, the present paper proposes an unsupervised and graph-based method of image segmentation, which is driven by an application goal, namely, the generation of image segments associated with a user-defined and application-specific goal. A graph, together with a random grid of source elements, is defined on top of the input image. From each source satisfying a goal-driven predicate, called seed, a propagation algorithm assigns a cost to each pixel on the basis of similarity and topological connectivity, measuring the degree of association with the reference seed. Then, the set of most significant regions is automatically extracted and used to estimate a statistical model for each region. Finally, the segmentation problem is expressed in a Bayesian framework in terms of probabilistic Markov random field (MRF) graphical modeling. An ad hoc energy function is defined based on parametric models, a seed-specific spatial feature, a background-specific potential, and local-contextual information. This energy function is minimized through graph cuts and, more specifically, the alpha-beta swap algorithm, yielding the final goal-driven segmentation based on the maximum a posteriori (MAP) decision rule. The proposed method does not require deep a priori knowledge (e.g., labelled datasets), as it only requires the choice of a goal-driven predicate and a suited parametric model for the data. In the experimental validation with both magnetic resonance (MR) and synthetic aperture radar (SAR) images, the method demonstrates robustness, versatility, and applicability to different domains, thus allowing for further analyses guided by the generated products.

Biologically inspired image classifier based on saccadic eye movement design for convolutional neural networks

We propose a model for image classification by attentional search. Analogous to how humans scan an image by a sequence of saccades, in this model, an attentional window of size much smaller than the target size scans the target by a sequence of “saccades”, integrates the information acquired, and makes a classification decision. In order to process a sequence of attended image segments, the network must have memory, which is incorporated through 3 kinds of recurrent elements in the network architecture: Elman connections, Jordan connections, and Flip-flop neurons. The architecture of the model is designed as three separate channels labeled as – classifier network, eye-position network, and saccade network. Multiple attentional windows with different resolutions and a common center are given as input to the classifier network and the saccade network. The heat-map representation of the location of the attentional windows is given as input to the eye-position network. The saccade network predicts the next jump of the attention windows with the help of reward signals received by the classifier network. The output features of all the three channels are concatenated, before finally terminating in two output layers representing class prediction and next saccade prediction. The model is trained using deep Q-learning algorithm. Attentional search model is evaluated on MNIST handwritten digit, Kannada MNIST, Medical-MNIST, OCTMNIST, and QuickDraw datasets. Translated and Cluttered Translated versions of each dataset are generated to perform the task of classification based on local target search. Original datasets are used to show the task of classification based on search with global target integration. We also evaluate the saccade performance on Extended Yale Face B database. In various problem cases, the model exhibits comparable or superior performance to a state-of-the-art recurrent attention model. Demo code is available in this link.

Extracting Landmark and Trait Information from Segmented Digital Specimen Images Generated by Artificial Neural Networks

We have been successfully developing Artificial Intelligence (AI) models for automatically classifying fish species using neural networks over the last three years during the “Biology Guided Neural Network” (BGNN) project*1. We continue our efforts in another broader project, “Imageomics: A New Frontier of Biological Information Powered by Knowledge-Guided Machine Learning”*2. One of the main topics in the Imageomics Project is “Morphological Barcoding”. Within the Morphological Barcoding study, we are trying to build a gold standard method to identify species in different taxonomic groups based on their external morphology. This list of characters will contain, but not be limited to, landmarks, quantitative traits such as measurements of distances, areas, angles, proportions, colors, histograms, patterns, shapes, and outlines. The taxonomic groups will be limited by the data available, and we will be using fish as the topic of interest in this preliminary study. In this current study, we have focused on extracting morphological characters that are relying on anatomical features of fish, such as location of the eye, body length, and area of the head. We developed a schematic workflow to describe how we processed the data and extract the information (Fig. 1). We performed our analysis on the segmented images produced by Karpatne Team within the BGNN project (Bart et al. 2021). Segmentation analysis was performed using Artificial Neural Networks - Semantic Segmentation (Long et al. 2015); the list of segments to be detected were given as eye, head, trunk, caudal fin, pectoral fin, dorsal fin, anal fin, pelvic fin for fish. Segmented images, metadata and species lists were given as input to the workflow. During the cleaning and filtering subroutines, a subset of data was created by filtering down to the desired segmented images with corresponding metadata. In the validation step, segmented images were checked by comparing the number of specimens in the original image to the separate bounding-boxed specimen images, noting: violations in the segmentations, counts of segments, comparisons of relative positions of the segments among one another, traces of batch effect; comparisons according to their size and shape and finally based on these validation criteria each segmented image was assigned a score from 1 to 5 similar to Adobe XMP Basic namespace. The landmarks and the traits to be used in the study were extracted from the current literature, while mindful that some of the features may not be extracted successfully computationally. By using the landmark list, landmarks have been extracted by adapting the descriptions from the literature on to the segments, such as picking the left most point on the head as the tip of snout and top left point on the pelvic fin as base of the pelvic fin. These 2D vectors (coordinates), are then fine tuned by adjusting their positions to be on the outline of the fish, since most of the landmarks are located on the outline. Procrustes analysis*3 was performed to scale all of the measurements together and point clouds were generated. These vectors were stored as landmark data. Segment centroids were also treated as landmarks. Extracted landmarks were validated by comparing their relative position among each other, and then if available, compared with their manually captured position. A score was assigned based on these comparisons, similar to the segmentation validation score. Based on the trait list definitions, traits were extracted by measuring the distances between two landmarks, angles between three landmarks, areas between three or more landmarks, areas of the segments, ratios between two distances or areas and between a distance and a square rooted area and then stored as trait data. Finally these values were compared within their own species clusters for errors and whether the values were still within the bounds. Trait scores were calculated from these error calculations similar to segmentation scores aiming selecting good quality scores for further analysis such as Principal Component Analysis. Our work on extraction of features from segmented digital specimen images has shown that the accuracy of the traits such as measurements, areas, and angles depends on the accuracy of the landmarks. Accuracy of the landmarks is highly dependent on segmentation of the parts of the specimen. The landmarks that are located on the outline of the body (combination of head and trunk segments of the fish) are found to be more accurate comparing to the landmarks that represents inner features such as mouth and pectoral fin in some taxonomic groups. However, eye location is almost always accurate, since it is based on the centroid of the eye segment. In the remaining part of this study we will improve the score calculation for segments, images, landmarks and traits and calculate the accuracy of the scores by comparing the statistical results obtained by analysis of the landmark and trait data.

Anti-gravity stem-seeking restoration algorithm for maize seed root image phenotype detection

Root phenotype detection is key to cultivating seeds with excellent traits, and requires a complete root image. However, soil occlusion, uneven lighting, and other factors cause broken points and segments in the root image. To solve this problem, an anti-gravity stem-seeking (AGSS) root image restoration algorithm is proposed in this paper to repair root images and extract root phenotype information for different resistant maize seeds. First, the obtained root image was processed using uniform illumination, grayscale, binarization, and morphological filtering to separate it from the background. Subsequently, a root skeleton map was generated using a thinning algorithm for pixel-level image processing, and the Taproot junction G was obtained. Subsequently, root pixel coordinates were obtained by traversing the root skeleton map.All root-segment endpoint coordinates were obtained using the endpoint judgment rule and stored in the endpoint list.The lateral and primary root endpoints were separated based on the lateral root judgment rule, and stored in the side root and primary root endpoint lists, respectively. Subsequently, the primary root endpoints were processed and fitted in the order short to long using arbitrary-two-endpoint spacing until all breakpoints of the primary root were found.The coordinates of the top endpoints of each principal root were obtained. Finally, the top endpoint was connected to point G based on the Bezier curve-fitting method to achieve complete root repair. The proposed AGSS root image restoration algorithm was applied to detect the root systems of maize with different resistances and wheat to evaluate its performance against the standard dataset.The results indicated a detection accuracy of greater than 90% for root taproot length and diameter.It was also found that maize drought resistance was positively correlated with root length and diameter, but negatively with the lateral root number.In contrast, the waterlogging and salt resistance traits of maize were positively correlated with the number of lateral roots. In conclusion, the proposed AGSS root image restoration algorithm can quickly and effectively repair root images, is suitable for different resistance evaluations of maize seeds, and is conducive to the detection of root phenotypes. Compared with deep learning methods, this algorithm displays advantages of fast repair, low hardware platform requirements, and less requirement of training images. The algorithm is highly suitable for deploying in small embedded systems, with broad application prospects.

Safety Monitoring of Neural Networks Using Unsupervised Feature Learning and Novelty Estimation

Neural networks are currently suggested to be implemented in several different driving functions of autonomous vehicles. While showing promising results the drawback lies in the difficulty of safety verification and ensuring operation as intended. The aim of this paper is to increase safety when using neural networks, by proposing a monitoring framework based on novelty estimation of incoming driving data. The idea is to use unsupervised instance discrimination to learn a similarity measure across ego-vehicle camera images. By estimating a von Mises-Fisher distribution of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">expected</i> ego-camera images they can be compared with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">unexpected</i> novel images. A novelty measurement is inferred through the likelihood of test frames belonging to the expected distribution. The suggested method provides competitive results to several other novelty or anomaly detection algorithms on the CIFAR-10 and CIFAR-100 datasets. It also shows promising results on real world driving scenarios by distinguishing novel driving scenes from the training data of BDD100 k. Applied on the identical training-test data split, the method is also able to predict the performance profile of a segmentation network. Finally, examples are provided on how this method can be extended to find novel segments in images.

Images Segmentation Based on Cutting the Graph into Communities

This article considers the problem of image segmentation based on its representation as an undirected weighted graph. Image segmentation is equivalent to partitioning a graph into communities. The image segment corresponds to each community. The growing area algorithm search communities on the graph. The average edge weight in the community is a measure of the separation quality. The correlation radius determines the number of next nearest neighbors connected by edges. Edge weight is a function of the difference between color and geometric coordinates of pixels. The exponential law calculates the weights of an edge in a graph. The computer experiment determines the parameters of the algorithm.

Smart Waste Management and Classification Systems Using Cutting Edge Approach

With a rapid increase in population, many problems arise in relation to waste dumps. These emits hazardous gases, which have negative effects on human health. The main issue is the domestic solid waste collection, management, and classification. According to studies, in America, nearly 75% of waste can be recycled, but there is a lack of a proper real-time waste-segregating mechanism, due to which only 30% of waste is being recycled at present. To maintain a clean and green environment, we need a smart waste management and classification system. To tackle the above-highlighted issue, we propose a real-time smart waste management and classification mechanism using a cutting-edge approach (SWMACM-CA). It uses the Internet of Things (IoT), deep learning (DL), and cutting-edge techniques to classify and segregate waste items in a dump area. Moreover, we propose a waste grid segmentation mechanism, which maps the pile at the waste yard into grid-like segments. A camera captures the waste yard image and sends it to an edge node to create a waste grid. The grid cell image segments act as a test image for trained deep learning, which can make a particular waste item prediction. The deep-learning algorithm used for this specific project is Visual Geometry Group with 16 layers (VGG16). The model is trained on a cloud server deployed at the edge node to minimize overall latency. By adopting hybrid and decentralized computing models, we can reduce the delay factor and efficiently use computational resources. The overall accuracy of the trained algorithm is over 90%, which is quite effective. Therefore, our proposed (SWMACM-CA) system provides more accurate results than existing state-of-the-art solutions, which is the core objective of this work.

Visual Loop Closure Detection With Instance Segmentation and Image Inpainting in Dynamic Scenes Using Wearable Camera

Dynamic interference is an important factor that affects the accuracy of loop closure detection in visual SLAM. Traditional methods that remove the feature points in dynamic regions are prone to insufficient image features, which will decrease the reliability of loop closure detection. To overcome this problem, a novel loop closure detection approach based on image inpainting and feature selection is proposed. A dynamic object instance segmentation guided image inpainting network is utilized to segment and repair dynamic regions in scene images. To avoid the negative influence of image inpainting, an image quality evaluation network is used to confine the extraction of Superpoint features only in the properly inpainted patches. Therefore, valid Superpoint features only in the areas with high inpainting qualities are selected as the input of the Bag of Words model for loop-closure detection. We tested the proposed method on both the KITTI dataset and a wearable camera system. Experimental results indicate that the proposed method improves the accuracy of loop closure detection in dynamic scenes.

SWTRU: Star-shaped Window Transformer Reinforced U-Net for medical image segmentation

In the last decade, deep neural networks have been widely applied to medical image segmentation, achieving good results in computer-aided diagnosis tasks etc. However, the task of segmenting highly complex, low-contrast images of organs and tissues with high accuracy still faces great challenges. To better address this challenge, this paper proposes a novel model SWTRU (Star-shaped Window Transformer Reinforced U-Net) by combining the U-Net network which plays well in the image segmentation field, and the Transformer which possesses a powerful ability to capture global contexts. Unlike the previous methods that import the Transformer into U-Net, an improved Star-shaped Window Transformer is introduced into the decoder of the SWTRU to enhance the decision-making capability of the whole method. The SWTRU uses a redesigned multi-scale skip-connection model, which retains the inductive bias of the original FCN structure for images while obtaining fine-grained features and coarse-grained semantic information. Our method also presents the FFIM (Filtering Feature Integration Mechanism) to integration and dimensionality reduction of the fused multi-layered features, which reduces the computation. Our SWTRU yields 0.972 DICE on CHLISC for liver and tumor segmentation, 0.897 DICE on LGG for glioma segmentation, and 0.904 DICE on ISIC2018 for skin diseases’ segmentation, achieves substantial improvements over the current SoTA across 9 different medical image segment methods. SWTRU can combine feature mapping from different scales, high-level semantics, and global contextual relationships, this architecture is effective in the medical image segmentation. The experimental findings indicate that SWTRU produces superior performance on the medical image segmentation tasks.

Dual-modality synthetic mammogram construction for breast lesion detection using U-DARTS

Multimodal image fusion is an emergent research area for cancer detection. It provides a wide variety of visual qualities for the accurate medical diagnosis. However, this process requires accurate registration of each image modality for its efficient and effective use. To address the aforementioned issue, a novel synthetic mammogram construction model is proposed. An image enhancement approach is applied to enhance the image quality. Dual-modality structural feature (DMSF) based mapping function is designed to transform a mammogram from a thermal image segment. This paper also proposes modified Differentiable ARchiTecture Search (DARTS) named as (U-DARTS) to detect and classify the breast lesion. In U-DARTS, a stochastic gradient descent optimizer is used. The proposed approach is tested over DMR and INbreast datasets. The results exhibit a significant improvement in the performance of the proposed model over the existing techniques. The validation and testing accuracies of 98% and 91%, respectively, are achieved. Overall, the proposed approach establishes supremacy in so far as the mammogram construction and further lesion detection are concerned.

A Tuned Whale Optimization-Based Stacked-LSTM Network for Digital Image Segmentation

A Tuned Whale Optimization-Based Stacked-LSTM Network for Digital Image Segmentation

A transfer learning based deep learning model to diagnose covid-19 CT scan images.

To save the life of human beings during the pandemic conditions we need an effective automated method to deal with this situation. In pandemic conditions when the available resources becomes insufficient to handle the patient’s load, then we needed some fast and reliable method which analyse the patient medical data with high efficiency and accuracy within time limitations. In this manuscript, an effective and efficient method is proposed for exact diagnosis of the patient whether it is coronavirus disease-2019 (covid-19) positive or negative with the help of deep learning. To find the correct diagnosis with high accuracy we use pre-processed segmented images for the analysis with deep learning. In the first step the X-ray image or computed tomography (CT) of a covid-19 infected person is analysed with various schemes of image segmentation like simple thresholding at 0.3, simple thresholding at 0.6, multiple thresholding (between 26–230) and Otsu’s algorithm. On comparative analysis of all these methods, it is found that the Otsu’s algorithm is a simple and optimum scheme to improve the segmented outcome of binary image for the diagnosis point of view. Otsu’s segmentation scheme gives more precise values in comparison to other methods on the scale of various image quality parameters like accuracy, sensitivity, f-measure, precision, and specificity. For image classification here we use Resnet-50, MobileNet and VGG-16 models of deep learning which gives accuracy 70.24%, 72.95% and 83.18% respectively with non-segmented CT scan images and 75.08%, 80.12% and 99.28% respectively with Otsu’s segmented CT scan images. On a comparative study we find that the VGG-16 models with CT scan image segmented with Otsu’s segmentation gives very high accuracy of 99.28%. On the basis of the diagnosis of the patient firstly we go for an arterial blood gas (ABG) analysis and then on the behalf of this diagnosis and ABG report, the severity level of the patient can be decided and according to this severity level, proper treatment protocols can be followed immediately to save the patient's life. Compared with the existing works, our deep learning based novel method reduces the complexity, takes much less time and has a greater accuracy for exact diagnosis of coronavirus disease-2019 (covid-19).

Graphical Image Region Extraction with K-Means Clustering and Watershed.

With a wide range of applications, image segmentation is a complex and difficult preprocessing step that plays an important role in automatic visual systems, which accuracy impacts, not only on segmentation results, but directly affects the effectiveness of the follow-up tasks. Despite the many advances achieved in the last decades, image segmentation remains a challenging problem, particularly, the segmenting of color images due to the diverse inhomogeneities of color, textures and shapes present in the descriptive features of the images. In trademark graphic images segmentation, beyond these difficulties, we must also take into account the high noise and low resolution, which are often present. Trademark graphic images can also be very heterogeneous with regard to the elements that make them up, which can be overlapping and with varying lighting conditions. Due to the immense variation encountered in corporate logos and trademark graphic images, it is often difficult to select a single method for extracting relevant image regions in a way that produces satisfactory results. Many of the hybrid approaches that integrate the Watershed and K-Means algorithms involve processing very high quality and visually similar images, such as medical images, meaning that either approach can be tweaked to work on images that follow a certain pattern. Trademark images are totally different from each other and are usually fully colored. Our system solves this difficulty given it is a generalized implementation designed to work in most scenarios, through the use of customizable parameters and completely unbiased for an image type. In this paper, we propose a hybrid approach to Image Region Extraction that focuses on automated region proposal and segmentation techniques. In particular, we analyze popular techniques such as K-Means Clustering and Watershedding and their effectiveness when deployed in a hybrid environment to be applied to a highly variable dataset. The proposed system consists of a multi-stage algorithm that takes as input an RGB image and produces multiple outputs, corresponding to the extracted regions. After preprocessing steps, a K-Means function with random initial centroids and a user-defined value for k is executed over the RGB image, generating a gray-scale segmented image, to which a threshold method is applied to generate a binary mask, containing the necessary information to generate a distance map. Then, the Watershed function is performed over the distance map, using the markers defined by the Connected Component Analysis function that labels regions on 8-way pixel connectivity, ensuring that all regions are correctly found. Finally, individual objects are labelled for extraction through a contour method, based on border following. The achieved results show adequate region extraction capabilities when processing graphical images from different datasets, where the system correctly distinguishes the most relevant visual elements of images with minimal tweaking.