Multiple Image Types Research Articles

Cross-correlation image analysis for real-time single particle tracking.

Accurately measuring the translations of objects between images is essential in many fields, including biology, medicine, chemistry, and physics. One important application is tracking one or more particles by measuring their apparent displacements in a series of images. Popular methods, such as the center of mass, often require idealized scenarios to reach the shot noise limit of particle tracking and, therefore, are not generally applicable to multiple image types. More general methods, such as maximum likelihood estimation, reliably approach the shot noise limit, but are too computationally intense for use in real-time applications. These limitations are significant, as real-time, shot-noise-limited particle tracking is of paramount importance for feedback control systems. To fill this gap, we introduce a new cross-correlation-based algorithm that approaches shot-noise-limited displacement detection and a graphics processing unit-based implementation for real-time image analysis of a single particle.

Histopathology-validated gross tumor volume delineations of intraprostatic lesions using PSMA-positron emission tomography/multiparametric magnetic resonance imaging

Background and purposeDose escalation in external radiotherapy of prostate cancer shows promising results in terms of biochemical disease-free survival. Boost volume delineation guidelines are sparse which may cause high interobserver variability. The aim of this research was to characterize gross tumor volume (GTV) delineations based on multiparametric magnetic resonance imaging (mpMRI) and prostate specific membrane antigen-positron emission tomography (PSMA-PET) in relation to histopathology-validated Gleason grade 4 and 5 regions. Material and methodsThe study participants were examined with [68Ga]PSMA-PET/mpMRI prior to radical prostatectomy. Four radiation oncologists delineated GTVs in 15 study participants, on four different image types; T2-weighted (T2w), diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE) and PSMA-PET scans separately. The simultaneous truth and performance level estimation (STAPLE) algorithm was used to generate combined GTVs. GTVs were subsequently compared to histopathology. We analysed how Dice similarity coefficient (DSC) and lesion coverage are affected by using single versus multiple image types as well as by adding a clinical target volume (CTV) margin. ResultsMedian DSC (STAPLE) for different GTVs varied between 0.33 and 0.52. GTVPSMA-PET/mpMRI generated the highest median lesion coverage at 0.66. Combining different image types achieved similar lesion coverage as adding a CTV margin to contours from a single image type, while reducing non-malignant tissue inclusion within the target volume. ConclusionThe combined use of mpMRI or PSMA-PET/mpMRI shows promise, achieving higher DSC and lesion coverage while minimizing non-malignant tissue inclusion, in comparison to the use of a single image type with an added CTV margin.

LSCEC: Design of a High-efficiency Leaf-image Segmentation & Classification Model via Ensemble Compute Processes

Real-time leaf image segmentation requires consideration of shadow effects, light variations, capture-angle variations, and zoom effects. Designing an efficient segmentation model that is capable of considering these effects is highly complex, and requires context-specific segmentation techniques. Such techniques are computationally complex, and require larger delays, which limits their applicability for real-time use cases. Moreover, such models are highly context-sensitive and require continuous reconfiguration for multiple image types. To overcome these limitations, this text proposes design of a high-efficiency leaf-image segmentation & classification model via ensemble compute processes. The model initially uses saliency maps for background removal, and cascades it with an ensemble of multiple Fuzzy C Means (FCM) segmentation techniques. These include, Enhanced FCM, k FCM, and original FCM, which results in 3 different image sets. Each of these image sets are individually processed to identify green & yellow coloured foreground pixels. The extracted image mask is filled for better segmentation, and resulting image sets are processed for extraction of features. To efficiently represent the images, a set of Colour, Texture & Convolutional feature maps are extracted, which assists in identification of multidomain feature sets. These feature sets are classified by a combination of Naïve Bayes (NB) Multilayer Perceptron (MLP), Logistic Regression (LR), and Support Vector Machine (SVM) based classification engines, which assists in improving efficiency of disease detection for different crop types. Due to this combination, the proposed model is able to achieve 3.5% better classification accuracy, 1.8% faster response, 4.9% higher precision, and 5.5% better recall when compared with state-of-the-art models. Due to which, the proposed model is capable of deployment for a wide variety of real-time use cases.

Deep Neural Network for Cardiac Magnetic Resonance Image Segmentation.

The analysis and interpretation of cardiac magnetic resonance (CMR) images are often time-consuming. The automated segmentation of cardiac structures can reduce the time required for image analysis. Spatial similarities between different CMR image types were leveraged to jointly segment multiple sequences using a segmentation model termed a multi-image type UNet (MI-UNet). This model was developed from 72 exams (46% female, mean age 63 ± 11 years) performed on patients with hypertrophic cardiomyopathy. The MI-UNet for steady-state free precession (SSFP) images achieved a superior Dice similarity coefficient (DSC) of 0.92 ± 0.06 compared to 0.87 ± 0.08 for a single-image type UNet (p < 0.001). The MI-UNet for late gadolinium enhancement (LGE) images also had a superior DSC of 0.86 ± 0.11 compared to 0.78 ± 0.11 for a single-image type UNet (p = 0.001). The difference across image types was most evident for the left ventricular myocardium in SSFP images and for both the left ventricular cavity and the left ventricular myocardium in LGE images. For the right ventricle, there were no differences in DCS when comparing the MI-UNet with single-image type UNets. The joint segmentation of multiple image types increases segmentation accuracy for CMR images of the left ventricle compared to single-image models. In clinical practice, the MI-UNet model may expedite the analysis and interpretation of CMR images of multiple types.

Parameter Analysis of Multiscale Two-Dimensional Fuzzy and Dispersion Entropy Measures Using Machine Learning Classification.

Two-dimensional fuzzy entropy, dispersion entropy, and their multiscale extensions ( and , respectively) have shown promising results for image classifications. However, these results rely on the selection of key parameters that may largely influence the entropy values obtained. Yet, the optimal choice for these parameters has not been studied thoroughly. We propose a study on the impact of these parameters in image classification. For this purpose, the entropy-based algorithms are applied to a variety of images from different datasets, each containing multiple image classes. Several parameter combinations are used to obtain the entropy values. These entropy values are then applied to a range of machine learning classifiers and the algorithm parameters are analyzed based on the classification results. By using specific parameters, we show that both and approach state-of-the-art in terms of image classification for multiple image types. They lead to an average maximum accuracy of more than 95% for all the datasets tested. Moreover, results in a better classification performance than that extracted by as a majority. Furthermore, the choice of classifier does not have a significant impact on the classification of the extracted features by both entropy algorithms. The results open new perspectives for these entropy-based measures in textural analysis.

A Multiple-Format Steganography Algorithm for Color Images

This paper presents an image Steganography algorithm that can work for cover images of multiple formats. Having a single algorithm for multiple image types provides several advantages. For example, we can apply uniform security policies across all image formats, we can adaptively select the most suitable cover image based on data length, network bandwidth and allowable distortions, etc. We present our algorithm based on the abstract concept of image components that can be adapted for JPEG, Bitmap, TIFF and PNG cover images. To the best of our knowledge, the proposed algorithm is the first Steganography algorithm that can work for multiple cover image formats. In addition, we have utilized concepts like capacity pre-estimation, adaptive partition schemes and data spreading to embed secret data with enhanced security. The proposed method is tested for robustness against Steganalysis with favorable results. Moreover, comparative results for the proposed algorithm are very promising for three different cover image formats.

Linked 4-Way Multimodal Brain Differences in Schizophrenia in a Large Chinese Han Population

Multimodal fusion has been regarded as a promising tool to discover covarying patterns of multiple imaging types impaired in brain diseases, such as schizophrenia (SZ). In this article, we aim to investigate the covarying abnormalities underlying SZ in a large Chinese Han population (307 SZs, 298 healthy controls [HCs]). Four types of magnetic resonance imaging (MRI) features, including regional homogeneity (ReHo) from resting-state functional MRI, gray matter volume (GM) from structural MRI, fractional anisotropy (FA) from diffusion MRI, and functional network connectivity (FNC) resulted from group independent component analysis, were jointly analyzed by a data-driven multivariate fusion method. Results suggest that a widely distributed network disruption appears in SZ patients, with synchronous changes in both functional and structural regions, especially the basal ganglia network, salience network (SAN), and the frontoparietal network. Such a multimodal coalteration was also replicated in another independent Chinese sample (40 SZs, 66 HCs). Our results on auditory verbal hallucination (AVH) also provide evidence for the hypothesis that prefrontal hypoactivation and temporal hyperactivation in SZ may lead to failure of executive control and inhibition, which is relevant to AVH. In addition, impaired working memory performance was found associated with GM reduction and FA decrease in SZ in prefrontal and superior temporal area, in both discovery and replication datasets. In summary, by leveraging multiple imaging and clinical information into one framework to observe brain in multiple views, we can integrate multiple inferences about SZ from large-scale population and offer unique perspectives regarding the missing links between the brain function and structure that may not be achieved by separate unimodal analyses.

Multispectral fluorescence imaging of human ovarian and fallopian tube tissue for early-stage cancer detection.

With early detection, 5-year survival rates for ovarian cancer exceed 90%, yet no effective early screening method exists. Emerging consensus suggests over 50% of the most lethal form of the disease originates in the fallopian tube. Twenty-eight women undergoing oophorectomy or debulking surgery provided informed consent for the use of surgical discard tissue samples for multispectral fluorescence imaging. Using multiple ultraviolet and visible excitation wavelengths and emissions bands, 12 fluorescence and 6 reflectance images of 47 ovarian and 31 fallopian tube tissue samples were recorded. After imaging, each sample was fixed, sectioned, and stained for pathological evaluation. Univariate logistic regression showed cancerous tissue samples had significantly lower intensity than noncancerous tissue for 17 image types. The predictive power of multiple image types was evaluated using multivariate logistic regression (MLR) and quadratic discriminant analysis (QDA). Two MLR models each using two image types had receiver operating characteristic curves with area under the curve exceeding 0.9. QDA determined 56 image type combinations with perfect resubstituting using as few as five image types. Adaption of the system for future in vivo fallopian tube and ovary endoscopic imaging is possible, which may enable sensitive detection of ovarian cancer with no exogenous contrast agents.

A Study on the Pre - service Secondary School Teachers’ Single &amp; Multiple Image Types of Teacher

A Study on the Pre - service Secondary School Teachers’ Single &amp; Multiple Image Types of Teacher

Reporter Works with Multiple Imaging Types

Abstract A cell-tracking system can work with 4 molecular imaging techniques and offer high sensitivity in deep tissues.

Fusion of multiple image types for the creation of radiometrically-accurate synthetic scenes

The Digital Imaging and Remote Sensing Image Generation (DIRSIG) model is an established, first-principles based scene simulation tool that produces synthetic multi-spectral and hyperspectral images from the visible to long wave infrared (0.4 to 20 microns). Over the last few years, significant enhancements such as spectral polarimetric and active Light Detection and Ranging (lidar) models have also been incorporated into the software, providing an extremely powerful tool for algorithm testing and sensor evaluation. However, the extensive time required to create large-scale scenes has limited DIRSIG's ability to generate scenes "on demand." To date, scene generation has been a laborious, time-intensive process, as the terrain model, CAD objects and background maps have to be created and attributed manually. To shorten the time required for this process, we have developed a comprehensive workflow aimed at reducing the man-in-the-loop requirements for many aspects of synthetic hyperspectral scene construction. Through a fusion of 3D lidar data with passive imagery, we have been able to partially-automate many of the required tasks in the creation of high-resolution urban DIRSIG scenes. This paper presents a description of these techniques.

A model for a PC-based, universal-format, multimedia digitization system: Moving beyond the scanner 1

Rationale and Objectives. Digitizing images for use in case presentations based on hardcopy films, slides, photographs, negatives, books, and videos can present a challenging task. Scanners and digital cameras have become standard tools of the trade. Unfortunately, use of these devices to digitize multiple images in many different media formats can be a time-consuming and in some cases unachievable process. The authors’ goal was to create a PC-based solution for digitizing multiple media formats in a timely fashion while maintaining adequate image presentation quality. Materials and Methods. The authors’ PC-based solution makes use of off-the-shelf hardware applications to include a digital document camera (DDC), VHS video player, and video-editing kit. With the assistance of five staff radiologists, the authors examined the quality of multiple image types digitized with this equipment. The authors also quantified the speed of digitization of various types of media using the DDC and video-editing kit. Results. With regard to image quality, the five staff radiologists rated the digitized angiography, CT, and MR images as adequate to excellent for use in teaching files and case presentations. With regard to digitized plain films, the average rating was adequate. As for performance, the authors recognized a 68% improvement in the time required to digitize hardcopy films using the DDC instead of a professional quality scanner. Conclusions. The PC-based solution provides a means for digitizing multiple images from many different types of media in a timely fashion while maintaining adequate image presentation quality.