Variations In Image Contrast Research Articles

Comprehensive analysis of synthetic learning applied to neonatal brain MRI segmentation.

Brain segmentation from neonatal MRI images is a very challenging task due to large changes in the shape of cerebral structures and variations in signal intensities reflecting the gestational process. In this context, there is a clear need for segmentation techniques that are robust to variations in image contrast and to the spatial configuration of anatomical structures. In this work, we evaluate the potential of synthetic learning, a contrast-independent model trained using synthetic images generated from the ground truth labels of very few subjects. We base our experiments on the dataset released by the developmental Human Connectome Project, for which high-quality images are available for more than 700 babies aged between 26 and 45 weeks postconception. First, we confirm the impressive performance of a standard UNet trained on a few volumes, but also confirm that such models learn intensity-related features specific to the training domain. We then confirm the robustness of the synthetic learning approach to variations in image contrast. However, we observe a clear influence of the age of the baby on the predictions. We improve the performance of this model by enriching the synthetic training set with realistic motion artifacts and over-segmentation of the white matter. Based on extensive visual assessment, we argue that the better performance of the model trained on real T2w data may be due to systematic errors in the ground truth. We propose an original experiment allowing us to show that learning from real data will reproduce any systematic bias affecting the training set, while synthetic models can avoid this limitation. Overall, our experiments confirm that synthetic learning is an effective solution for segmenting neonatal brain MRI. Our adapted synthetic learning approach combines key features that will be instrumental for large multisite studies and clinical applications.

Diabetic Plantar Foot Segmentation in Active Thermography Using a Two-Stage Adaptive Gamma Transform and a Deep Neural Network.

Pathological conditions in diabetic feet cause surface temperature variations, which can be captured quantitatively using infrared thermography. Thermal images captured during recovery of diabetic feet after active cooling may reveal richer information than those from passive thermography, but diseased foot regions may exhibit very small temperature differences compared with the surrounding area, complicating plantar foot segmentation in such cold-stressed active thermography. In this study, we investigate new plantar foot segmentation methods for thermal images obtained via cold-stressed active thermography without the complementary information from color or depth channels. To better deal with the temporal variations in thermal image contrast when planar feet are recovering from cold immersion, we propose an image pre-processing method using a two-stage adaptive gamma transform to alleviate the impact of such contrast variations. To improve upon existing deep neural networks for segmenting planar feet from cold-stressed infrared thermograms, a new deep neural network, the Plantar Foot Segmentation Network (PFSNet), is proposed to better extract foot contours. It combines the fundamental U-shaped network structure, a multi-scale feature extraction module, and a convolutional block attention module with a feature fusion network. The PFSNet, in combination with the two-stage adaptive gamma transform, outperforms multiple existing deep neural networks in plantar foot segmentation for single-channel infrared images from cold-stressed infrared thermography, achieving an accuracy of 97.3% and 95.4% as measured by Intersection over Union (IOU) and Dice Similarity Coefficient (DSC) respectively.

Preclinical validation of the advection diffusion flow estimation method using computational patient specific coronary tree phantoms.

Coronary computed tomography angiography (CCTA) does not allow the quantification of reduced blood flow due to coronary artery disease (CAD). In response, numerical methods based on the CCTA image have been developed to compute coronary blood flow and assess the impact of disease. However to compute blood flow in the coronary arteries, numerical methods require specification of boundary conditions that are difficult to estimate accurately in a patient-specific manner. We describe herein a new noninvasive flow estimation method, called Advection Diffusion Flow Estimation (ADFE), to compute coronary artery flow from CCTA to use as boundary conditions for numerical models of coronary blood flow. ADFE uses image contrast variation along the tree-like structure to estimate flow in each vessel. For validating this method we used patient specific software phantoms on which the transport of contrast was simulated. This controlled validation setting enables a direct comparison between estimated flow and actual flow and a detailed investigation of factors affecting accuracy. A total of 10 CCTA image data sets were processed to extract all necessary information for simulating contrast transport. A spectral element method solver was used for computing the ground truth simulations with high accuracy. On this data set, the ADFE method showed a high correlation coefficient of between estimated flow and the ground truth flow together with an average relative error of only . Comparing the ADFE method with the best method currently available (TAFE) for image-based blood flow estimation, which showed a correlation coefficient of and average error of , it can be concluded that the ADFE method has the potential to significantly improve the quantification of coronary artery blood flow derived from contrast gradients in CCTA images.

Semi‐supervised learning for ABUS tumor detection using deep learning method

AbstractAutomated breast ultrasound (ABUS) imaging system is a practical technique to automatically scan the whole breast. Automatic tumor detection plays a significant role in the clinic. However, training deep convolutional neural networks (CNNs) for tumor detection needs a large quantity of labeled data. It is time‐consuming and expensive to manually annotate tumor positions in ABUS images. In this paper, a novel semi‐supervised learning EfficientDet (SSL‐E) model is proposed for ABUS tumor detection. Our SSL‐E model solves the tumor detection problem from high similarity and serious unbalance between tumors and backgrounds. Considering the image contrast variation and tumor scale variations in ABUS images, color transformation and geometric transformation are employed for data augmentations. Then the consistency between image and its augmented version is developed, thus the robustness of the detector can be improved. Aiming at the problem of serious unbalance between tumors and backgrounds, a novel copy‐paste synthesis strategy is designed, which can generate more tumor samples and enhance tumor diversity. This method is tested on 68 tumor volumes and 68 normal volumes, including 43,248 slices (1683 tumor slices and 41,565 normal slices). It obtains a promising result with sensitivity of 90.2% and false positives per image (FPs/I) at 0.15.

Identification of OH groups on SrTiO3(100)-(13×13)-R33.7° reconstructed surface by non-contact atomic force microscopy and scanning tunneling microscopy

Atomic resolution imaging of a SrTiO3(100)-(13×13)-R33.7° reconstructed surface using non-contact atomic force microscopy (NC-AFM) and its simultaneous measurement with scanning tunneling microscopy (STM) is presented. Simultaneous STM and NC-AFM imaging reveals three patterns of image contrast depending on the tip apex condition and the relationship between the SrTiO3(100)-(13×13)-R33.7° surface reconstructed structure and the NC-AFM image contrast. The NC-AFM image contrast variation is deduced from the tip apex polarity on the basis of an analysis of two images with opposite contrast. This interpretation is consistent with the results of simultaneous imaging of the SrTiO3(100)-(13×13)-R33.7° surface. Furthermore, the results and interpretation identified an OH group, which is one of the surface defects, and this adsorption site.

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and image contrast. Conventional methods lack accurate and automated designs. Artificial intelligence (AI)-based UNet has recently dominated BIS. This is the first review of its kind that microscopically addressed UNet types by complexity, stratification of UNet by its components, addressing UNet in vascular vs. non-vascular framework, the key to segmentation challenge vs. UNet-based architecture, and finally interfacing the three facets of AI, the pruning, the explainable AI (XAI), and the AI-bias. PRISMA was used to select 267 UNet-based studies. Five classes were identified and labeled as conventional UNet, superior UNet, attention-channel UNet, hybrid UNet, and ensemble UNet. We discovered 81 variations of UNet by considering six kinds of components, namely encoder, decoder, skip connection, bridge network, loss function, and their combination. Vascular vs. non-vascular UNet architecture was compared. AP(ai)Bias 2.0-UNet was identified in these UNet classes based on (i) attributes of UNet architecture and its performance, (ii) explainable AI (XAI), and, (iii) pruning (compression). Five bias methods such as (i) ranking, (ii) radial, (iii) regional area, (iv) PROBAST, and (v) ROBINS-I were applied and compared using a Venn diagram. Vascular and non-vascular UNet systems dominated with sUNet classes with attention. Most of the studies suffered from a low interest in XAI and pruning strategies. None of the UNet models qualified to be bias-free. There is a need to move from paper-to-practice paradigms for clinical evaluation and settings.

Whole slide imaging (WSI) scanner differences influence optical and computed properties of digitized prostate cancer histology

PurposeDigital pathology is becoming an increasingly popular area of advancement in both research and clinically. Pathologists are now able to manage and interpret slides digitally, as well as collaborate with external pathologists with digital copies of slides. Differences in slide scanners include variation in resolution, image contrast, and optical properties, which may influence downstream image processing. This study tested the hypothesis that varying slide scanners would result in differences in computed pathomic features on prostate cancer whole mount slides. DesignThis study collected 192 unique tissue slides from 30 patients following prostatectomy. Tissue samples were paraffin-embedded, stained for hematoxylin and eosin (H&E), and digitized using 3 different scanning microscopes at the highest available magnification rate, for a total of 3 digitized slides per tissue slide. These scanners included a (S1) Nikon microscope equipped with an automated sliding stage, an (S2) Olympus VS120 slide scanner, and a (S3) Huron TissueScope LE scanner. A color deconvolution algorithm was then used to optimize contrast by projecting the RGB image into color channels representing optical stain density. The resulting intensity standardized images were then computationally processed to segment tissue and calculate pathomic features including lumen, stroma, epithelium, and epithelial cell density, as well as second-order features including lumen area and roundness; epithelial area, roundness, and wall thickness; and cell fraction. For each tested feature, mean values of that feature per digitized slide were collected and compared across slide scanners using mixed effect models, fit to compare differences in the tested feature associated with all slide scanners for each slide, including a random effect of subject with a nested random effect of slide to account for repeated measures. Similar models were also computed for tissue densities to examine how differences in scanner impact downstream processing. ResultsEach mean color channel intensity (i.e., Red, Green, Blue) differed between slide scanners (all P<.001). Of the color deconvolved images, only the hematoxylin channel was similar in all 3 scanners (all P>.05). Lumen and stroma densities between S3 and S1 slides, and epithelial cell density between S3 and S2 (P>.05) were comparable but all other comparisons were significantly different (P<.05). The second-order features were found to be comparable for all scanner comparisons, except for lumen area and epithelium area. ConclusionThis study demonstrates that both optical and computed properties of digitized histological samples are impacted by slide scanner differences. Future research is warranted to better understand which scanner properties influence the tissue segmentation process and to develop harmonization techniques for comparing data across multiple slide scanners.

Observation of in-plane shear stress fields in off-axis SiC wafers by birefringence imaging.

For the nondestructive characterization of SiC wafers for power device application, birefringence imaging is one of the promising methods. In the present study, it is demonstrated that birefringence image contrast variation in off-axis SiC wafers corresponds to the in-plane shear stress under conditions slightly deviating from crossed Nicols according to both theoretical consideration and experimental observation. The current results indicate that the characterization of defects in SiC wafers is possible to achieve by birefringence imaging.

Bone radiomics reproducibility: a three-centered study on the impacts of image contrast, edge enhancement, and latitude variations.

This study aims to measure the reproducibility of radiomics features in ankle bone radiography over changes in post-processing parameters including contrast, edge enhancement and latitude.Lateral ankle bone radiographies for sixty patients were obtained from three digital radiology centers. All images were acquired by same image acquisition settings. A two-dimensional region of interest was drawn in any image and 93 features from 6 feature sets including first and second order were extracted. The coefficient of variation (COV) and intraclass correlation coefficient (ICC) were calculated to assess feature reproducibility for each center and among all centers in three scenarios: Adams(Nat Rev Endocrinol 9(1):28, 2013) ten different contrast Brown et al. (J Med Imaging 5(1):011017, 2018) ten different edge enhancement and Hirvasniemi et al. (Osteoarthr Cartilage 27(6):906-914, 2019) ten different image latitude parameters.Based on ICC analysis, it is observed that 46-100-44% of Histogram, 54-72-42% of GLCM, 43-76-36% of GLDM, 60-90-17% of GLRLM, 33-19-21% of GLSZM and 13-20-0% of NGTDM radiomics features had 90% < ICC < 100% over changes in contrast-edge enhancement-latitude changes respectively. Based on COV, GLRLM was only feature set that 100% of their features had COV ≤ 5% over changes in contrast and edge enhancement.The results presented here, indicating that radiomics features extracted are vulnerable over changes in contrast, edge enhancement and latitude. The most reproducible features that introduced in this study could be used for further clinical decision making.

Automatically Extracted Machine Learning Features from Preoperative CT to Early Predict Microvascular Invasion in HCC: The Role of the Zone of Transition (ZOT).

Simple SummaryMicrovascular invasion (MVI) is a universally recognised predictor of hepatocellular carcinoma (HCC) recurrence after curative treatments, whose diagnosis, nowadays, is still postponed to surgery, through histopathological specimen investigation. This retrospective study aims to exploit a radiomic approach to pre-procedural diagnosis MVI in early-stage HCC, with a diameter ≤ 3 cm. The main novelty of the study is the use of the zone of transition (ZOT), crossing tumour and peritumour, detected adaptively through a standardized procedure based on the analysis of image gradients. After generating radiomics features from ZOT and tumour core in arterial and venous computed tomography phases, a classifier is trained and validated using a signature of four features only. The achieved radiomic model enables the early diagnosis of small HCC (≤3 cm) showing MVI with high specificity (82%) and sensitivity (79%), perspectively providing an effective tool to detect the best candidates for surgical treatment and liver transplantation (negative predictive value = 87%).Background: Microvascular invasion (MVI) is a consolidated predictor of hepatocellular carcinoma (HCC) recurrence after treatments. No reliable radiological imaging findings are available for preoperatively diagnosing MVI, despite some progresses of radiomic analysis. Furthermore, current MVI radiomic studies have not been designed for small HCC nodules, for which a plethora of treatments exists. This study aimed to identify radiomic MVI predictors in nodules ≤3.0 cm by analysing the zone of transition (ZOT), crossing tumour and peritumour, automatically detected to face the uncertainties of radiologist’s tumour segmentation. Methods: The study considered 117 patients imaged by contrast-enhanced computed tomography; 78 patients were finally enrolled in the radiomic analysis. Radiomic features were extracted from the tumour and the ZOT, detected using an adaptive procedure based on local image contrast variations. After data oversampling, a support vector machine classifier was developed and validated. Classifier performance was assessed using receiver operating characteristic (ROC) curve analysis and related metrics. Results: The original 89 HCC nodules (32 MVI+ and 57 MVI−) became 169 (62 MVI+ and 107 MVI−) after oversampling. Of the four features within the signature, three are ZOT heterogeneity measures regarding both arterial and venous phases. On the test set (19MVI+ and 33MVI−), the classifier predicts MVI+ with area under the curve of 0.86 (95%CI (0.70–0.93), p∼10), sensitivity = 79% and specificity = 82%. The classifier showed negative and positive predictive values of 87% and 71%, respectively. Conclusions: The classifier showed the highest diagnostic performance in the literature, disclosing the role of ZOT heterogeneity in predicting the MVI+ status.

Advantages of optical modulation in terahertz imaging for study of graphene layers

It was demonstrated that optical modulation together with simultaneous terahertz (THz) imaging application enables an increase in contrast by an order of magnitude, thereby illustrating the technique as a convenient contactless tool for characterization of graphene deposited on high-resistivity silicon substrates. It was shown that the single- and double-layer graphene can be discriminated and characterized via variation of THz image contrast using a discrete frequency in a continuous wave mode. Modulation depth of 45% has been reached, and the contrast variation from 0.16 up to 0.23 is exposed under laser illumination for the single- and double-layer graphene, respectively. The technique was applied in the development and investigation of graphene-based optical diffractive elements for THz imaging systems.

Blind Tone-Mapped HDR Image Quality Measurement by Analysis of Low-Level and High-Level Perceptual Characteristics

Tone-mapping technology aims to transform High Dynamic Range (HDR) images into standard dynamic range images for visualizing on standard screens. However, various visual artifacts will inevitably be induced during the tone-mapping process. To quantify the visual quality degradation for Tone-Mapped Images (TMIs) accurately and automatically, a robust blind TMI quality measurement method is developed by analysis of low-level and high-level perceptual characteristics. Specifically, for low-level visual features, considering that the tone-mapping operator is prone to destroy the local image contrast, which conveys vital structural information, we resort to the joint statistical features based on gradient map and Laplacian of Gaussian response to portray the local image contrast variation in TMIs. In addition, Chromatic Local Binary Pattern (CLBP) is leveraged to measure the colorfulness degradation over four chromatic descriptor maps. For high-level quality-aware features, deep-learned features are utilized by a pre-trained convolutional neural network to characterize the semantics variation. Finally, the extracted low-level and high-level features are combined and mapped into an overall quality score by regression function. Extensive experiments show that our metric achieves a higher performance over two publicly available TMI benchmark databases.

Benchmarking automated detection of the retinal external limiting membrane in a 3D spectral domain optical coherence tomography image dataset of full thickness macular holes

In this article, we present a new benchmark for the segmentation of the retinal external limiting membrane (ELM) using an image dataset of spectral domain optical coherence tomography (OCT) scans in a patient population with idiopathic full-thickness macular holes. Specifically, the dataset used contains OCT images from one eye of 107 patients with an idiopathic full-thickness macular hole. In total, the dataset contains 5243 individual 2-dimensional (2-D) OCT image slices, with each patient contributing 49 individual spectral-domain OCT tagged image slices. We display precise image-wise binary annotations to segment the ELM line. The OCT images present high variations in image contrast, motion, brightness, and speckle noise which can affect the robustness of applied algorithms, so we performed an extensive OCT imaging and annotation data quality analysis. Imaging data quality control included noise, blurriness and contrast scores, motion estimation, darkness and average pixel scores, and anomaly detection. Annotation quality was measured using gradient mapping of ELM line annotation confidence, and idiopathic full-thickness macular hole detection. Finally, we compared qualitative and quantitative results with seven state-of-the-art machine learning-based segmentation methods to identify the ELM line with an automated system.

DCRN: An Optimized Deep Convolutional Regression Network for Building Orientation Angle Estimation in High-Resolution Satellite Images

Recently, remote sensing satellite image analysis has received significant attention from geo-information scientists. However, the current geo-information systems lack automatic detection of several building characteristics inside the high-resolution satellite images. The accurate extraction of buildings characteristics helps the decision-makers to optimize urban planning and achieve better decisions. Furthermore, Building orientation angle is a very critical parameter in the accuracy of automated building detection algorithms. However, the traditional computer vision techniques lack accuracy, scalability, and robustness for building orientation angle detection. This paper proposes two different approaches to deep building orientation angle estimation in the high-resolution satellite image. Firstly, we propose a transfer deep learning approach for our estimation task. Secondly, we propose a novel optimized DCRN network consisting of pre-processing, scaled gradient layer, deep convolutional units, dropout layers, and regression end layer. The early proposed gradient layer helps the DCRN network to extract more helpful information and increase its performance. We have collected a building benchmark dataset that consists of building images in Riyadh city. The images used in the experiments are 15,190 buildings images. In our experiments, we have compared our proposed approaches and the other approaches in the literature. The proposed system has achieved the lowest root mean square error (RMSE) value of 1.24, the lowest mean absolute error (MAE) of 0.16, and the highest adjusted R-squared value of 0.99 using the RMS optimizer. The cost of processing time of our proposed DCRN architecture is 0.0113 ± 0.0141 s. Our proposed approach has proven its stability with the input building image contrast variation for all orientation angles. Our experimental results are promising, and it is suggested to be utilized in other building characteristics estimation tasks in high-resolution satellite images.

Synchrotron X-ray topographic image contrast variation of screw-type basal plane dislocations located at different depths below the crystal surface in 4H-SiC

The contrast features of synchrotron X-ray topographic images of screw-type basal plane dislocations (BPDs) in on-axis 4H-SiC wafers have been studied. Screw BPD images are categorized into two types: one exhibiting a white line bordered by black lines and the other a pure black line contrast. Similar images for off-axis specimens and the corresponding ray-tracing simulations demonstrate that these contrasts can be attributed to the depth of the screw BPDs below the crystal surface. The correlation of the contrast features between simulations and the screw BPD topography images can be used to estimate the depth.

Synchrotron X-Ray Topographic Image Contrast Variation of BPDs Located at Different Depths Below the Crystal Surface in 4H-SiC

The contrast features of synchrotron X-ray topographic images of screw-type basal plane dislocations (BPDs) in on-axis 4H-SiC wafers have been studied. Screw BPD images are categorized into two types: one exhibiting a white line bordered by black lines and the other a pure black line contrast. Similar images for off-axis specimens and the corresponding ray-tracing simulations demonstrate that these contrasts can be attributed to the depth of the screw BPDs below the crystal surface. The correlation of the contrast features between simulations and the screw BPD topography images can be used to estimate the depth.

Image composition influences on the mood board visual reading process through eye-tracking

Mood boards (MB) are a visual tool used to provide references for inspiration and decision-making in the design development process. However, it isn't easy to obtain objective and precise measures about the MB organization if exclusively qualitative approaches identify how the image composition can affect the visual reading process. In this sense, this study aimed to evaluate how the position and physical image aspects influence an attentive MB visual reading. The results presented that variations in image contrast and positioning influence the observer's visual reading. Besides, the observers' identified tendencies were to concentrate the visualisations in the boards' central, lower and left areas, indicating the importance of this region's MB construction. Moreover, the results evidence that the visual reading of an MB presents a different pattern from those reported by studies directed to analysing the visual reading of websites. Thus, changes in image positioning influenced the viewer's perception, being a relevant point to be considered when assembling the MB's image composition. The results of this study contribute to creating criteria for the construction of MB.

Signed-distance function based non-rigid registration of image series with varying image intensity

<p style='text-indent:20px;'>In this paper we propose a method for locally adjusted optical flow-based registration of multimodal images, which uses the segmentation of object of interest and its representation by the signed-distance function (OF<inline-formula><tex-math id="M1">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula> method). We deal with non-rigid registration of the image series acquired by the Modiffied Look-Locker Inversion Recovery (MOLLI) magnetic resonance imaging sequence, which is used for a pixel-wise estimation of <inline-formula><tex-math id="M2">\begin{document}$ T_1 $\end{document}</tex-math></inline-formula> relaxation time. The spatial registration of the images within the series is necessary to compensate the patient's imperfect breath-holding. The evolution of intensities and a large variation of image contrast within the MOLLI image series, together with the myocardium of left ventricle (the object of interest) typically not being the most distinct object in the scene, makes the registration challenging. The paper describes all components of the proposed OF<inline-formula><tex-math id="M3">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula> method and their implementation. The method is then compared to the performance of a standard mutual information maximization-based registration method, applied either to the original image (MIM) or to the signed-distance function (MIM<inline-formula><tex-math id="M4">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula>). Several experiments with synthetic and real MOLLI images are carried out. On synthetic image with a single object, MIM performed the best, while OF<inline-formula><tex-math id="M5">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula> and MIM<inline-formula><tex-math id="M6">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula> provided better results on synthetic images with more than one object and on real images. When applied to signed-distance function of two objects of interest, MIM<inline-formula><tex-math id="M7">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula> provided a larger registration error, but more homogeneously distributed, compared to OF<inline-formula><tex-math id="M8">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula>. For the real MOLLI image series with left ventricle pre-segmented using a level-set method, the proposed OF<inline-formula><tex-math id="M9">\begin{document}$ ^{dist} $\end{document}</tex-math></inline-formula> registration performed the best, as is demonstrated visually and by measuring the increase of mutual information in the object of interest and its neighborhood.

Statistical Histogram Decision Based Contrast Categorization of Skin Lesion Datasets Dermoscopic Images

Most of the melanoma cases of skin cancer are the life-threatening form of cancer. It is prevalent among the Caucasian group of people due to their light skin tone. Melanoma is the second most common cancer that hits the age group of 15–29 years. The high number of cases has increased the importance of automated systems for diagnosing. The diagnosis should be fast and accurate for the early treatment of melanoma. It should remove the need for biopsies and provide stable diagnostic results. Automation requires large quantities of images. Skin lesion datasets contain various kinds of dermoscopic images for the detection of melanoma. Three publicly available benchmark skin lesion datasets, ISIC 2017, ISBI 2016, and PH2, are used for the experiments. Currently, the ISIC archive and PH2 are the most challenging and demanding dermoscopic datasets. These datasets’ pre-analysis is necessary to overcome contrast variations, under or over segmented images boundary extraction, and accurate skin lesion classification. In this paper, we proposed the statistical histogram-based method for the pre-categorization of skin lesion datasets. The image histogram properties are utilized to check the image contrast variations and categorized these images into high and low contrast images. The two performance measures, processing time and efficiency, are computed for evaluation of the proposed method. Our results showed that the proposed methodology improves the pre-processing efficiency of 77% of ISIC 2017, 67% of ISBI 2016, and 92.5% of PH2 datasets.

Evaluation of CT Imaging Characteristics and Effect of CTDI Phantom Size on Contrast Materials

Recently, there have been several physics and clinical studies on the use of lower tube potentials in CT imaging, with the purpose of improving image quality or further reducing radiation dose. We investigated an experimental study using a series of different sized, polymethyl methacrylate (PMMA) phantoms, demonstrating the potential strategy for dose reduction and to distinguish component of plaque by imaging their energy responses using CT. We investigated the relationship between different sizes of cylinderic PMMA equivalent phantoms with diameter of 12, 16, 20, 24, and 32 cm and used contrast at various tube voltages (80, 100, 120, and 140 kVp) using a 16–detector row CT scanner. The contrast represented CT numbers as different materials for the water, calcium chloride, and iodine. Phantom insertions also allow quantitative measures of image noise, contrast, contrast-to-noise ratio (CNR) and figure of merit (FOM). When evaluating FOM, it was found that the lower kVp provided the better CNR. An experimental study was performed to demonstrate reduced dose for both dose efficient and practical feasibility for different patient sizes and diagnostic tasks by relating achievable CNR and the volume CT dose index (CTDI vol). The use of spectra optimized to the specific application could provide further improvements of distinguishing iodine, calcium and plaque component for patient size. The purpose of this study was to evaluate variations in image noise and contrast using different tube potentials in a CTDI phantom on contrast imaging.