Blended Images Research Articles

Molecular characterization of solitary pulmonary nodules in dual-energy CT nonlinear image fusion technology

Objective To investigate the feasibility and to optimize the parameters of nonlinear blending technique in dual-energy CT on solitary pulmonary nodules (SPN). Methods The simulated enhanced SPN were used the mixture of nonionic iodinated contrast agent (Iopromide 370mgI/100 ml) and normal saline and then randomly placed inside an anthropomorphic chest phantom. The phantom was examined on SOMATOM definition flash with dual mode (80/140 kV) and single energy mode (120 kV) (the same CTDIvol). Nonlinear blending images and linear blending images with a weighting factor of 0.3 were generated and the image qualities were analyzed. Results For different simulated density SPN, when 0 HU was chosen as the Blending Center (BC) and 0 to 30 HU were chosen as the Blending width (BW), the nonlinear blending images yielded a higher contrast-to-noise (CNR). There were significant differences in the image noise and signal-to-noise (SNR) of different simulated density SPN at non-linear blending images, linear blending images and 120 kV images (p < .05); But the differences of CNR between the three groups were not statistically significant (p > .05). The SNR of different simulated density SPN at non-linear blending images was significantly increased compared with it at linear blending images and 120 kV images (p < .05); And the image noise at non-linear blending was lower than it at linear blending images (p < .05). Conclusion Nonlinear blending technique in dual-energy CT can increase the SNR of enhanced SPN, and it is helpful in diagnosis of SPN.

Classification of Tomato Plants Diseases Using Convolutional Neural Network

Tomato plants are many cultivated by farmers to get their fruit. Several obstacles in the cultivation process result in the process of producing products that require maximization. Constraints faced by farmers are diseases that attack plants. Farmers in dealing with the disease simply recognize the disease with their naked eyes and take action without knowing how to deal with it. Several approaches have been made in the recognition process that can be handled by using deep learning. The results shown using this process produce a good performance. Therefore, this paper has the aim of making and evaluating recognition of the diseases in plants seen on tomato leaves automatically using a deep learning approach. Convolutional Neural Network (CNN) is one of the deep learning methods used in handling object recognition processes. Recognition process tomato plants disease used a data set consisting of 6 different types of disease. Recognition process used 100 image data for each type of disease as training data, while as many as 60 image data are used as testing. This study used three different types of data sets that are used differently, consisting of original image RGB, blending images, and a mixture of RGB images and blending images. The classification results using a mixture of RGB images and blending images have a better performance than others by producing a Genuine Acceptance Rate (GAR) of 96.7% following by the percentage of False Acceptance Rate (FAR) values of 3.3% and False Rejection Rate (FRR) of 3.3%.

High-resolution optical microscopy for characterising microstructural deformation in microtensile testing.

Imaging surface deformation of a coupon specimen in microtensile testing with an optical microscope presents challenges due to the narrow depth of field (DoF) of optical microscopes. Materials being heterogeneous at microscopic length scale, the sample surface deforms into a complex 3D surface texture, evolving continuously as the loading increases. Because of the narrow DoF, the region that is in focus within the field of view (FoV) decreases substantially in size with the increasing out-of-plane heterogeneous deformation. To address this challenge, a method based on image blending and stabilisation of the captured image frames is proposed. Image blending combines the partial regions that are in focus from a set of successive image frames captured at different working distances from the object surface plane to construct a single image that has a large part of the FoV in focus. The blended images are then obtained at different levels of macroscopic strains, that is the global homogeneous strain, in order to characterise the evolution of the heterogeneous deformation. The image stabilisation removes any misalignments of the blended images by spatially realigning them choosing a common feature as a reference point. The validation of the proposed method with conventionally and additively manufactured stainless steel 316L (SS 316L) specimens demonstrates excellent improvement in image quality. Almost 100% of the FoV is maintained in focus regardless of the amount of out-of-plane heterogeneous deformation caused during tensile testing, which is quite remarkable for optical microscopy imaging. Consequently, the blended and stabilised images enhanced the accuracy of digital image correlation (DIC). Time-lapse videos of the deformation generated using these images captured the evolution of the slip bands and their transmission through twinning boundaries in the stainless steel microstructure. Overall, this study demonstrates the feasibility of using image-processing techniques to advance optical microscopy to image complex 3D surfaces evolving with time.

Image evaluation of Dual-Energy CT Pulmonary Angiography ; Using Linear Blended and Virtual Monoenergetic Images Plus Reconstruction

Image evaluation of Dual-Energy CT Pulmonary Angiography ; Using Linear Blended and Virtual Monoenergetic Images Plus Reconstruction

Accuracy of Pulmonary Nodule Volumetry Using Noise-Optimized Virtual Monoenergetic Image and Nonlinear Blending Image Algorithms in Dual-Energy Computed Tomography: A Phantom Study.

The aim of the study was to assess accuracy of pulmonary nodule volumetry using noise-optimized virtual monoenergetic image (VMI+) and nonlinear blending image (NBI) algorithms in dual-energy computed tomography (DECT). An anthropomorphic chest phantom with 10 simulated nodules (5 solid nodules and 5 ground-glass opacities) was scanned using DECT80/Sn140kV, DECT100/Sn140kV, and single-energy CT (SECT120kV/200mAs), respectively. The dual-energy images were reconstructed using VMI+ (70 keV) and NBI algorithms. The contrast-to-noise ratio and absolute percentage error (APE) of nodule volume were measured to assess image quality and accuracy of nodule volumetry. The radiation dose was also estimated. The contrast-to-noise ratio of SECT120kV/200mAs was significantly higher than that of NBI80/Sn140kV and VMI+80/Sn140kV (both corrected P < 0.05), whereas there were no significant differences between NBI100/sn140kV and SECT120kV/200mAs and between VMI+100/sn140kV and SECT120kV/200mAs (both corrected P > 0.05). The APE of SECT120kV/200mAs was significantly lower than that of NBI80/Sn140kV and VMI+80/Sn140kV in both types of nodules (all corrected P < 0.05), whereas there were no significant differences between VMI+100/sn140kV and SECT120kV/200mAs in solid nodules and between NBI100/Sn140kV and SECT120kV/200mAs in ground-glass opacities (both corrected P > 0.05). The radiation dose of DECT100/Sn140kV and DECT80/Sn140kV were significantly lower than that of SECT120kV/200mAs (both corrected P < 0.05). The DECT100/sn140kV can ensure image quality and nodule volumetry accuracy with lower radiation dose compared with SECT120kV/200mAs. Specifically, the VMI+ algorithm could be used in solid nodules and NBI algorithm in ground-glass opacities.

Near Simultaneous Laser Scanning Confocal and Atomic Force Microscopy (Conpokal) on Live Cells.

Techniques available for micro- and nano-scale mechanical characterization have exploded in the last few decades. From further development of the scanning and transmission electron microscope, to the invention of atomic force microscopy, and advances in fluorescent imaging, there have been substantial gains in technologies that enable the study of small materials. Conpokal is a portmanteau that combines confocal microscopy with atomic force microscopy (AFM), where a probe “pokes” the surface. Although each technique is extremely effective for the qualitative and/or quantitative image collection on their own, Conpokal provides the capability to test with blended fluorescence imaging and mechanical characterization. Designed for near simultaneous confocal imaging and atomic force probing, Conpokal facilitates experimentation on live microbiological samples. The added insight from paired instrumentation provides co-localization of measured mechanical properties (e.g., elastic modulus, adhesion, surface roughness) by AFM with subcellular components or activity observable through confocal microscopy. This work provides a step by step protocol for the operation of laser scanning confocal and atomic force microscopy, simultaneously, to achieve same cell, same region, confocal imaging, and mechanical characterization.

A multi-phase blending method with incremental intensity for training detection networks

Object detection is an important topic for visual data processing in the visual computing area. Although a number of approaches have been studied, it still remains a challenge. There is a suitable way to promote image classifiers by blending training with blended images and corresponding blended labels. However, our experiments show that directly moving existing blending methods from classification to object detection will cause the training process become harder and eventually will lead to a bad performance. Inspired by our discovery, this paper presents a multi-phase blending method with incremental blending intensity to improve the accuracy of object detectors and achieve remarkable improvements. Firstly, to adapt blending method to detection task, we propose a smoothly scheduled and incremental blending intensity to control the degree of multi-phase blending. Based on the above dynamic coefficient, we propose an incremental blending method, in which the blending intensity is smoothly increased from zero to full. Therefore, more complex and various data can be created to achieve the goal of regularizing the network. Secondly, we also design an incremental hybrid loss function to replace the original loss function. The blending intensity in our loss function increases smoothly, which is controlled by our scheduled coefficient. Thirdly, we further discard more negative examples in our multi-phase training process than other typical training methods and processes. By doing so, we can regularize the neural network to enhance generalization capability with data diversity and eventually to improve the accuracy in object detection. Another advantage is that there is no negative effect on evaluation because our method is just applied during the training process. Typical experiments show the proposed method improves the generalization of the detection networks. On PASCAL VOC and MS COCO, our method outperforms the state-of-the-art RFBNet of one-stage detectors for real-time processing.

Perceptions of blended destination image: the case of Rio de Janeiro and Brazil

PurposeThe purpose of this paper is to investigate the image of Brazil and Rio de Janeiro as a blended tourism destination (TD) image, by examining the categories of the image, and whether it is positive or negative and cognitive or affective.Design/methodology/approachA quantitative approach was employed to verify the relations between the image of Brazil and Rio de Janeiro by visit, location, categories and subcategories and dimensions. Data were collected online from a “snowball” sample and were analyzed applying non-parametric hypothesis testing. Statistical analysis was performed with Statistical Package for the Social Sciences software version 25.0 for Windows.FindingsResults reveal that respondents share the same image of the city of Rio de Janeiro and Brazil, a conclusion that is partially confirmed by statistical findings. The quantitative results also showed that the image is positive and “natural attractions” and “unique city attractions” are, respectively, the most mentioned subcategory and category. Cognitive attributes of the image have been more mentioned than affective ones.Originality/valueTo the best of the authors’ knowledge, the image of Brazil and Rio de Janeiro as a blended TD image has never been explored before, however, it has been assumed as being the same.

Upturn of the contour-interaction function at small flanking bar-to-target separations

Nearby flanking bars degrade letter identification and resolution, a phenomenon known as contour interaction. However, many previous studies found that the relationship between foveal letter identification and flanker separation is non-monotonic, with an upturn in performance at very small target-to-flanker separations. Here, we replicate this observation and show that a similar upturn occurs also for targets presented at 5 deg in the inferior field, if the target-to-flanker separation is sufficiently small. The presence and magnitude of the observed performance upturn depends on the flanking-bar width, being more evident for narrower compared to wider flanking bars. We interpret our results to indicate that neural interactions between nearby contours reduce performance when the target and flanking bars form discrete neural images. At sufficiently small separations, the images of the target and flanking bars can not be distinguished and performance is governed by the contrast of the target in the blended neural image.

Dual-energy CT in the differentiation of crystal depositions of the wrist: does it have added value?

To evaluate the ability of dual-energy computed tomography (DECT) to improve diagnostic discrimination between gout and other crystal arthropathies such as calcium pyrophosphate deposition disease (CPPD) of the wrist in a clinical patient population. This retrospective case-control study included 29 patients with either gout (case group; n = 9) or CPPD (control group; n = 20) who underwent DECT of the wrist for clinically suspected crystal arthropathy. Color-coded urate and enhanced calcium as well as virtual 120 kVe blended images were reconstructed from the DECT datasets. Two independent and blinded readers evaluated each reconstructed dataset for the presence of depositions in 17 predefined regions. Additionally, a global diagnosis was made first for 120 kVe images only, based solely on morphologic criteria, and subsequently for all reconstructed images. Sensitivity for the global diagnosis of gout was 1.0 (95% CI 0.63-1) for both DECT and 120 kVe images with specificities of 0.70 (95% CI 0.46-0.87) for DECT and 0.80 (95% CI 0.56-0.93) for 120 kVe images. Color-coded DECT images did not detect more depositions than monochrome standard CT images. Discrimination of crystal arthropathies of the wrist is limited using DECT and primarily relying on color-coded images. Evaluation of morphologic criteria on standard CT images is essential for accurate diagnosis.

Nonconvex optimization-based inverse spectral decomposition

Abstract As a time–frequency analysis tool, inverse spectral decomposition (ISD) could be utilized to obtain a high-resolution time–frequency map via the inversion strategy. In the established inversion function, an analytic signal is disintegrated as a coefficient matrix whose elements represent the weights of the wavelet components with the different dominant frequencies and the time location in the complex wavelet library. By using a sparse constraint, a high-quality inverse decomposition result could be generated. In this paper, a modified ISD technique based on a nonconvex optimization algorithm is proposed to pursue a sparser coefficient solution found in the inversion processing by decreasing the redundant information. This new approach applies the lp(0 &lt; p &lt; 1) penalty term to build an accurate mapping relationship between the original signal and its time–frequency spectrum. This adequate regularization in reconstructed function serves as a better alternative to the l1 norm one in conventional ISD. Lower signal-to-noise ratio (SNR) and much weaker incoherence of wavelet library cannot impact on the accuracy of output with the ISD based on the lp norm (0 &lt; p &lt; 1) constraint. Synthetic data tests with the Gabor transform, the conventional ISD method and the nonconvex optimization-based ISD are applied to demonstrate the performance gaps. In the real application area, a blended image with instantaneous spectral attribute volumes produced by the new approach assists the identification of the geological anomalous body, which is verified via a physical model data and field data tests.

Spatiotemporal Image Fusion in Remote Sensing

In this paper, we discuss spatiotemporal data fusion methods in remote sensing. These methods fuse temporally sparse fine-resolution images with temporally dense coarse-resolution images. This review reveals that existing spatiotemporal data fusion methods are mainly dedicated to blending optical images. There is a limited number of studies focusing on fusing microwave data, or on fusing microwave and optical images in order to address the problem of gaps in the optical data caused by the presence of clouds. Therefore, future efforts are required to develop spatiotemporal data fusion methods flexible enough to accomplish different data fusion tasks under different environmental conditions and using different sensors data as input. The review shows that additional investigations are required to account for temporal changes occurring during the observation period when predicting spectral reflectance values at a fine scale in space and time. More sophisticated machine learning methods such as convolutional neural network (CNN) represent a promising solution for spatiotemporal fusion, especially due to their capability to fuse images with different spectral values.

A Robust Decomposition Based Algorithm For Removal Of Pattern Noise From Images

This article aims a melting pool of complex vectors, that is, the aggregation and the minimization problem of sufficiency spectra. A mixture of this blended standard and image decline issue works admirably to reduce and deteriorate the example of concussion which occurs when old pictures are filtered with granular surfaces. In most cases, the appealing appropriation of regular photos easily reduces from low repetition to the high repetition band, while the episode of concussion is scarcely circulating. We agree along these lines that a picture viewed includes an idle image and an example clamor, describing them separately by using the full range and capacity work. This enables the two parts to decompose sensibly. In contrast to the comparative strategies of deterioration, for instance, robust PCA, our technique is decent, less computer expenditure, and moreover less time suited for any image organization

Deblending galaxy superpositions with branched generative adversarial networks

Abstract Near-future large galaxy surveys will encounter blended galaxy images at a fraction of up to 50 per cent in the densest regions of the Universe. Current deblending techniques may segment the foreground galaxy while leaving missing pixel intensities in the background galaxy flux. The problem is compounded by the diffuse nature of galaxies in their outer regions, making segmentation significantly more difficult than in traditional object segmentation applications. We propose a novel branched generative adversarial network to deblend overlapping galaxies, where the two branches produce images of the two deblended galaxies. We show that generative models are a powerful engine for deblending given their innate ability to infill missing pixel values occluded by the superposition. We maintain high peak signal-to-noise ratio and structural similarity scores with respect to ground truth images upon deblending. Our model also predicts near-instantaneously, making it a natural choice for the immense quantities of data soon to be created by large surveys such as Large Synoptic Survey Telescope, Euclid, and Wide-Field Infrared Survey Telescope.

Dual-Source Dual-Energy CT in Detection and Characterization of Urinary Stones in Patients With Large Body Habitus: Observations in a Large Cohort.

The objective of our study was to investigate the impact of large body habitus on dual-energy CT (DECT) image quality and stone characterization. We retrospectively included 105 consecutive patients with large body habitus (> 90 kg) who underwent stone protocol DECT between 2015 and 2017. The evaluation of DECT datasets was performed for image quality assessment based on European Guidelines on Quality Criteria for Computed Tomography and for determination of stone composition (i.e., uric acid vs non-uric acid). Correlation between DECT characterization and crystallography results was performed when available. The cohort was divided into two groups on the basis of body weight (≤ 104 kg and > 104 kg), and comparisons were made for image quality and stone characterization. One hundred ninety-seven urinary tract calculi (size: mean ± SD, 5.7 ± 5.3 mm; range, 1.4-56 mm) were detected in 73% (79/108) of examinations in 105 patients (weight: mean ± SD, 104.0 ± 12.7 kg; range, 91-163 kg). The overall mean image quality score of blended images and color maps was 3.7 and 3.9, respectively, and the effective dual-energy FOV limitation did not hamper stone characterization. The diagnostic acceptability scores of blended images and color maps were slightly lower in patients weighing > 104 kg than in patients ≤ 104 kg (mean scores [highest score, 4 points]: blended images, 3.62 vs 3.82 [p = 0.0314]; color maps, 3.75 vs 3.98 [p = 0.0034]), but the scores were within acceptable range. Stone characterization as uric acid versus non-uric acid was achieved in 80% (158/197) of calculi (size: mean ± SD, 6.4 ± 5.7 mm; range, 1.6-56 mm), and DECT stone characterization was (95.6%) accurate with reference to crystallography. Twenty percent (39/197) of calculi could not be characterized on DECT, and these calculi were significantly smaller in size (size: mean ± SD, 2.8 ± 1.4 mm; range, 1.4-8.2 mm; p < 0.001) than those that could be characterized. The mean size of uncharacterized calculi was slightly larger in patients weighing > 104 kg (3.3 ± 1.6 mm) than in those weighing ≤ 104 kg (2.2 ± 0.6 mm). In patients with large body habitus, dual-source DECT provides acceptable image quality and allows characterization of almost all clinically significant calculi.

360-Degree Panoramic Image Stitching for Un-ordered Images Based on Harris Corner Detection

Objectives: This work aims to provide a system to arrange images automatically and produce the High-resolution panoramic images of a 360-degree wide range. Methods: This work consists of many stages which start by arranging the set of unordered input images. The Harris corner detector is used in the process of detecting key-points. Euclidean distance measure is used to compute the distance between points and determine matches. The stage of stitching consists of several steps which are detecting key-points, matching, estimating the shifting matrix between images by using Random Sample Consensus (RANSAC) algorithm, aligning, and blending. Findings: Experiments show that this method is able to arrange different sets of images, where samples taken are between 15–18 images. Depending on the threshold values in the verification process applied to the matched points, which range from (0.4 to 0.6), the number of extracted features and the degree of accuracy can be controlled. Also, the process of blending images based on the taking of a different percentage of the overlapping areas according to the proximity to the contact line gave a distinctive blending result. Application: Production of panoramic images are used in real estate photography, the creation of an attractive experience on viewing all location details, and lots of other uses. Keywords: Blending, Harris Corner Detector, Image Stitching, Panorama

Dual-Source Dual-Energy CT Portal Venous Phase Abdominal CT Scans in Large Body Habitus Patients: Preliminary Observations on Image Quality and Material Decomposition.

Our objective was to evaluate image quality (IQ) and material decomposition in patients with large body habitus undergoing portal venous phase abdominal computed tomography (CT) scans on dual-source dual-energy CT (dsDECT) scanners. This retrospective analysis included 30 scans from consecutive patients (19 males/11 females, mean ± SD age = 55.3 ± 17.5 years, range = 27-87 years) with large body habitus (≥90 kg, mean ± SD weight = 105.4 ± 12.35, range = 91-145 kg) who underwent portal venous phase abdominal DECT examinations on dsDECT scanner between Jan 2015 and Dec 2015. Qualitative and quantitative evaluation of IQ of DECT data sets (blended, iodine, and virtual noncontrast images) was performed. The patients were categorized into 2 groups (group A, ≤104 kg; group B, >104 kg). The mean ± SD patient body weight in group A was 97.2 ± 4.5 kg (range = 91-104 kg) and 114.8 ± 11.7 kg (range = 104.3-145.2 kg) for patients in group B. The diagnostic acceptability of the blended images in patients > 104 kg was lower (3.6 vs 4, <3 in 4/14 vs 0/16, P = 0.03). The extension of visceral anatomy beyond DE field of view (DEFOV) was seen in 60% (28 organs in 18 patients), the most common organs being liver and spleen. The incidence of visceral organs outside DEFOV was significantly higher in patients > 104 kg (18 vs 10, P = 0.03). Outside the DEFOV, blended images demonstrated higher image noise (mean: 14.48, range = 10.09-26.83 vs mean: 9.5, range = 7.3-15.8) P < 0.001) and lower signal-to-noise ratio (mean: 4.15, range = 1.5-7.6 vs mean: 7.5, range = 4.2-9.9) P < 0.001), and material-specific information was not available in this region. Within the DEFOV, the IQ of iodine maps and virtual non-contrast images were diagnostically acceptable with diagnostic acceptability of 3 or greater in nearly all patients. A 40-cm transverse diameter cut-off provided a good predictor of extension of visceral anatomy outside the effective DEFOV. Dual-source DECT allows diagnostically acceptable IQ and material separation in patients with large body habitus with the major limitation of exclusion of patient anatomy and organs outside the effective dual-energy field of view.

Unraveling the Spectral Energy Distributions of Clustered YSOs

Abstract Despite significant evidence suggesting that intermediate- and high-mass stars form in clustered environments, how stars form when the available resources are shared is still not well understood. A related question is whether the initial mass function (IMF) is in fact universal across galactic environments, or whether it is an average of IMFs that differ, for example, in massive versus low-mass molecular clouds. One of the long-standing problems in resolving these questions and in the study of young clusters is observational: how to accurately combine multiwavelength data sets obtained using telescopes with different spatial resolutions. The resulting confusion hinders our ability to fully characterize clustered star formation. Here we present a new method that uses Bayesian inference to fit the blended spectral energy distributions and images of individual young stellar objects (YSOs) in confused clusters. We apply this method to the infrared photometry of a sample comprising 70 Spitzer-selected, low-mass (M cl &lt; 100 M ⊙) young clusters in the galactic plane, and we use the derived physical parameters to investigate how the distribution of YSO masses within each cluster relates to the total mass of the cluster. We find that for low-mass clusters this distribution is indistinguishable from a randomly sampled Kroupa IMF for this range of cluster masses. Therefore, any effects of self-regulated star formation that affect the IMF sampling are likely to play a role only at larger cluster masses. Our results are also compatible with smoothed particle hydrodynamics models that predict a dynamical termination of the accretion in protostars, with massive stars undergoing this stopping at later times in their evolution.

Reduced graphene oxide (RGO)‐induced compatibilization and reinforcement of poly(vinylidene fluoride) (PVDF)–thermoplastic polyurethane (TPU) binary polymer blend

ABSTRACTCompatible blends of nonreactive thermoplastic fluoropolymer, poly(vinylidene fluoride) (PVDF) and thermoplastic polyurethane (TPU) at 70/30 weight ratio, were prepared by utilizing the unique structural feature of reduced graphene oxide (RGO). Here, RGO acts as a compatibilizer as well as a reinforcing filler. RGO interacts with both polymers and reduces the interfacial tension between them, leading to compatibilization. RGO content in the blends was varied from 0 to 0.5 wt %, and the best result was found at 0.3 wt % loading. Excellent compatibilization between PVDF and TPU was established by mechanical, morphological, and thermal property studies. Chemical interaction between the RGO/TPU and RGO/PVDF was proved by FTIR–ATR study. With the incorporation of 0.3 wt % RGO, tensile strength, Izod impact strength, and elongation at break of the blend were increased by 42%, 83%, and 43%, respectively. FESEM and AFM images of blends without loading of filler after etching out of TPU phase show nonuniformly distributed hole morphology. RGO‐containing blend has shown much finer and uniformly distributed holes that confirm improved compatibility between the two incompatible polymers. RGO also improves the thermal stability of the compatible blends considerably. At 0.3 wt % loading, the onset of thermal degradation increased by about 10 °C. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019, 136, 47010.

Characterization of Small Incidental Indeterminate Hypoattenuating Hepatic Lesions: Added Value of Single-Phase Contrast-Enhanced Dual-Energy CT Material Attenuation Analysis.

The objective of this study is to determine whether single-phase contrast-enhanced dual-energy CT (DECT) material attenuation analysis improves the characterization of small (< 2.0 cm) incidental indeterminate hypoattenuating hepatic lesions, compared with conventional single-energy CT evaluation. This retrospective study involved 55 patients (24 men and 31 women; mean [± SD] age, 63.9 ± 15.3 years) with 77 incidental hypoattenuating hepatic lesions (59 benign and 18 malignant lesions) measuring 0.5-2.0 cm who underwent single-phase contrast-enhanced DECT of the abdomen for pain. For each lesion, attenuation measurements were obtained using blended 120-kVp-equivalent images and contrast map images. DECT material attenuation images were used for iodine quantification. Optimal lesion attenuation and iodine concentration threshold values that best distinguished benign lesions from malignant lesions were generated using smooth bootstrapping. The diagnostic accuracy of the optimized thresholds was compared using the Wilcox rank sum test. The optimal mean (± standard error) attenuation threshold values that best differentiated benign and malignant lesions were 50.2 ± 5.2 HU and 11.5 ± 2.0 HU when blended 120-kVp and contrast map images, respectively, were used. The iodine concentration (expressed as milligrams of iodine per milliliter) differed significantly (p < 0.0001) between benign lesions (0.6 ± 0.4 mg I/mL) and malignant lesions (1.7 ± 0.4 mg I/mL). The optimal iodine concentration that best distinguished between benign and malignant lesions was 1.2 ± 0.1 mg I/mL. The sensitivity, specificity, and AUC value were highest for iodine concentration (0.94, 0.93, and 0.97, respectively), compared with blended images (0.89, 0.70, and 0.81, respectively) and contrast map images (0.94, 0.64, 0.77, respectively). Iodine quantification performed using single-phase contrast-enhanced DECT material attenuation images improves the characterization of small (< 2 cm) incidental indeterminate hypoattenuating hepatic lesions, compared with conventional attenuation measurements.