2D Grayscale Images Research Articles

Fast In Situ Image Reconstruction for Proton Radiography.

Proton beam therapy is an emerging modality for cancer treatment that, compared to X-ray radiation therapy, promises to provide better dose delivery to clinical targets with lower doses to normal tissues. Crucial to accurate treatment planning and dose delivery is knowledge of the water equivalent path length (WEPL) of each ray, or pencil beam, from the skin to every point in the target. For protons, this length is estimated from relative stopping power based on X-ray Hounsfield units. Unfortunately, such estimates lead to 3 to 4% uncertainties in the proton range prediction. Therefore, protons in the Bragg peak may overshoot (or undershoot) the desired stopping depth in the target causing tissue damage beyond the target volume. Recent studies indicate that tomographic imaging using protons has the potential to provide directly more accurate measurement of RSPs with significantly lower radiation dose than X-rays. We are currently working on a proton radiography system that promises to provide accurate two-dimensional (2D) images of WEPL values for protons that pass through the body. These will be suitable for positioning and range verification in daily treatments. In this study, we demonstrate that this system is capable of rapidly achieving such accurate images in clinically meaningful times. We have developed a software platform to characterize the potential performance of the prototype proton radiography system. We use Geant4 to simulate raw data detected by the device. An especially-written software - pRad - was written to process these data as they are received and uses iterative methods to generate radiographs. The software has been designed to generate a radiograph from a few million protons in under a minute after receiving the first proton from the device. We used a head phantom with known chemical compositions that could be modelled quite accurately in Geant4 simulations of proton radiographs. The radiographs are displayed as pixelated WEPL values displayed on a 2D gray scale image of WEPL values. Rapid radiograph reconstruction of 3D phantoms using simulated proton pencil beams have been achieved with our software platform. On a modest desktop computer with a single central processing unit (CPU) and a single graphics processing unit (GPU), it takes about 11 seconds to reconstruct images using iterative linear algorithms to reconstruct a radiograph from 7.6 million protons. For the radiographic reconstructions of the head phantom described here, the mean WEPL errors, in the proton radiograph using a large majority of the pixels in the complete image were less than 1 mm when compared to images obtained without proton scattering and without detector resolution included. We have demonstrated, through computer simulations of proton irradiation of a pediatric head phantom using the newly built pRad detector and image reconstruction software, that high quality proton radiographs can be generated for patient alignment and verification of water equivalent thickness of the patient before each treatment.

Convolutional neural network for breast cancer diagnosis using diffuse optical tomography

We have developed a computer-aided diagnosis system based on a convolutional neural network that aims to classify breast mass lesions in optical tomographic images obtained using a diffuse optical tomography system, which is suitable for repeated measurements in mass screening. Sixty-three optical tomographic images were collected from women with dense breasts, and a dataset of 1260 2D gray scale images sliced from these 3D images was built. After image preprocessing and normalization, we tested the network on this dataset and obtained 0.80 specificity, 0.95 sensitivity, 90.2% accuracy, and 0.94 area under the receiver operating characteristic curve (AUC). Furthermore, a data augmentation method was implemented to alleviate the imbalance between benign and malignant samples in the dataset. The sensitivity, specificity, accuracy, and AUC of the classification on the augmented dataset were 0.88, 0.96, 93.3%, and 0.95, respectively.

Ultrasound grayscale image quality comparison between a 2D intracavitary transducer and a 3D intracavitary transducer used in 2D mode: A phantom study.

PurposeIt is unclear if a 3D transducer with the special design of mechanical swing or 2D array could provide acceptable 2D grayscale image quality for the general diagnosis purpose. The aim of this study is to compare the 2D image quality of a 3D intracavitary transducer with a conventional 2D intracavitary transducer using clinically relevant phantom experiments.MethodsAll measurements were performed on a GE Logiq E9 scanner with both a 2D (IC5‐9‐D) and a 3D (RIC5‐9‐D) transducer used in 2D mode. Selection of phantom targets and acquisition parameters were determined from analysis of 33 clinical pelvic exams. Depth of penetration (DOP), contrast response, contrast of anechoic cylinders (diameter: 6.7 mm) at 1.5 and 4.5 cm depths in transverse planes, and in‐plane resolution represented by full‐width half‐maximum of pin targets at multiple depths were measured with transmit frequencies of 7 and 8 MHz. Spherical signal‐noise‐ratio (SNR) (diameter: 4 and 2 mm) at multiple depths were measured at 8 MHz.ResultsRIC5‐9‐D demonstrated <8% decrease in DOP for both transmit frequencies (7 MHz: 69.7 ± 8.2 mm; 8 MHz: 64.3 ± 7.8 mm) compared with those from IC5‐9‐D (7 MHz: 73.9 ± 4.4 mm; 8 MHz: 69.4 ± 7.8 mm). A decreased anechoic contrast was observed with a 4.5 cm depth for RIC5‐9‐D (7 MHz: 23.2 ± 1.8 dB, P > 0.05; 8 MHz: 17.7 ± 0.9 dB, P < 0.01) compared with IC5‐9‐D (7 MHz: 25.9 ± 1.2 dB; 8 MHz: 21.5 ± 0.8 dB). The contrast response and spatial resolution performance were comparable between the two transducers. RIC5‐9‐D showed comparable SNR of anechoic spheres compared to IC5‐9‐D.Conclusions2D images from a 3D probe exhibited comparable overall image quality for routine clinical pelvic imaging.

Bearing Fault Diagnosis Method Based on Deep Convolutional Neural Network and Random Forest Ensemble Learning.

Recently, research on data-driven bearing fault diagnosis methods has attracted increasing attention due to the availability of massive condition monitoring data. However, most existing methods still have difficulties in learning representative features from the raw data. In addition, they assume that the feature distribution of training data in source domain is the same as that of testing data in target domain, which is invalid in many real-world bearing fault diagnosis problems. Since deep learning has the automatic feature extraction ability and ensemble learning can improve the accuracy and generalization performance of classifiers, this paper proposes a novel bearing fault diagnosis method based on deep convolutional neural network (CNN) and random forest (RF) ensemble learning. Firstly, time domain vibration signals are converted into two dimensional (2D) gray-scale images containing abundant fault information by continuous wavelet transform (CWT). Secondly, a CNN model based on LeNet-5 is built to automatically extract multi-level features that are sensitive to the detection of faults from the images. Finally, the multi-level features containing both local and global information are utilized to diagnose bearing faults by the ensemble of multiple RF classifiers. In particular, low-level features containing local characteristics and accurate details in the hidden layers are combined to improve the diagnostic performance. The effectiveness of the proposed method is validated by two sets of bearing data collected from reliance electric motor and rolling mill, respectively. The experimental results indicate that the proposed method achieves high accuracy in bearing fault diagnosis under complex operational conditions and is superior to traditional methods and standard deep learning methods.

Optimum adaptive array stochastic resonance in noisy grayscale image restoration

Considering the widespread noise interference in the two-dimensional (2D) image transmission processing, we proposed an optimal adaptive bistable array stochastic resonance (SR)-based grayscale image restoration enhancement method under low peak signal-to-noise ratio (PSNR) environments. In this method, the Hilbert scanning is adopted to reduce the dimension of the original grayscale image. The 2D image signal is converted into a one-dimensional (1D) binary pulse amplitude modulation (BPAM) signal. Meanwhile, we use the adaptive bistable array SR module to enhance the 1D low SNR BPAM signal. In order to obtain the restored image, we transform the enhanced BPAM signal into a 2D grayscale image signal. Simulation results show that the proposed method significantly outperforms the classical image restoration methods (i.e., mean filter, Wiener filter and median filter) both on the grayscale level and the PSNR of the restored image, particularly in a low PSNR scenario. Larger array size brings better image restoration effect.

Edge Detection on DICOM Image using Triangular Norms in Type-2 Fuzzy

In image processing, edge detection is an important venture. Fuzzy logic plays a vital role in image processing to deal with lacking in quality of an image or imprecise in nature. This present study contributes an authentic method of fuzzy edge detection through image segmentation. Gradient of the image is done by triangular norms to extract the information. Triangular norms (T norms) and triangular conorms (T conorms) are specialized in dealing uncertainty. Therefore triangular norms are chosen with minimum and maximum operators for the purpose of morphological operations. Also, mathematical properties of aggregation operator to represent the role of morphological operations using Triangular Interval Type-2 Fuzzy Yager Weighted Geometric (TIT2FYWG) and Triangular Interval Type-2 Fuzzy Yager Weighted Arithmetic (TIT2FYWA) operators are derived. These properties represent the components of image processing. Here Edge detection is done for DICOM image by converting into 2D gray scale image, using Type-2 fuzzy MATLAB and which is the novelty of this work.

An automated blastomere identification method for the evaluation of day 2 embryos during IVF/ICSI treatments

PurposeEvaluation of human embryos is one of the most important challenges in vitro fertilization (IVF) programs. The morphology and the morphokinetic parameters of the early cleaving embryo are of critical clinical importance. This stage spans the first 48 h post-fertilization, in which the embryo is dividing in smaller blastomeres at specific time-points. The morphology, in combination with the symmetry of the blastomeres seems to be powerful features with strong prognostic value for embryo evaluation. To date, the identification of these features is based on human inspection in timed intervals, at best using camera systems that simply work as surveillance systems without any precise alerting and decision support mechanisms. The purpose of the study presented in this paper was to develop a computer vision technique to automatically detect and identify the most suitable cleaving embryos (preferably at day 2 post-fertilization) for embryo transfer (ET) during IVF/ICSI treatments. Methods and resultsTo this end, texture and geometrical features were used to localize and analyze the whole cleaving embryo in 2D grayscale images captured during in vitro embryo formation. Because of the ellipsoidal nature of blastomeres, the contour of each blastomere was modeled with an optimal fitting ellipse while the mean eccentricity of all ellipses is computed. The mean eccentricity in combination with the number of blastomeres forms the feature space on which the final criterion for the embryo evaluation was based. ConclusionsExperimental results with low quality 2D grayscale images demonstrated the effectiveness of the proposed technique and provided evidence of a novel automated approach for predicting embryo quality.

Image Transmission through OFDM System under the Influence of AWGN Channel

OFDM system is one among the modern techniques which is most abundantly used in next generation wireless communication networks for transmitting many forms of digital data in efficient manner than compared with other existing traditional techniques. In this paper, one such kind of a digital data corresponding to a two dimensional (2D) gray-scale image is used to evaluate the functionality and overall performance of an OFDM system under the influence of modeled AWGN channel in MATLAB simulation environment. Within the OFDM system, different configurations of notable modulation techniques such as M-PSK and M-QAM are considered for evaluation of the system and necessary valid conclusions are made from the comparison of several observed MATLAB simulation results.

An automated algorithm for the detection of cortical interruptions on high resolution peripheral quantitative computed tomography images of finger joints.

ObjectivesTo introduce a fully-automated algorithm for the detection of small cortical interruptions (≥0.246mm in diameter) on high resolution peripheral quantitative computed tomography (HR-pQCT) images, and to investigate the additional value of manual correction of the automatically obtained contours (semi-automated procedure).MethodsTen metacarpophalangeal joints from seven patients with rheumatoid arthritis (RA) and three healthy controls were imaged with HR-pQCT. The images were evaluated by an algorithm according to the fully- and semi-automated procedure for the number and surface of interruptions per joint. Reliability between the fully- and semi-automated procedure and between two independent operators was tested using intra-class correlation coefficient (ICC) and the proportion of matching interruptions. Validity of single interruptions detected was tested by comparing it to visual scoring, as gold standard. The positive predictive value (PPV) and sensitivity were calculated.ResultsThe median number of interruptions per joint was 14 (range 2 to 59) and did not significantly differ between the fully- and semi-automated procedure (p = 0.37). The median interruption surface per joint was significantly higher with the fully- vs. semi-automated procedure (respectively, 8.6mm2 vs. 5.8mm2 and 6.1mm2, p = 0.01). Reliability was almost perfect between the fully- and semi-automated procedure for both the number and surface of interruptions (ICC≥0.95) and the proportion of matching interruptions was high (≥76%). Also the inter-operator reliability was almost perfect (ICC≥0.97, proportion of matching interruptions 92%). The PPV ranged from 27.6% to 29.9%, and sensitivity from 69.7% to 76.3%. Most interruptions detected with the algorithm, did show an interruption on a 2D grayscale image. However, this interruption did not meet the criteria of an interruption with visual scoring.ConclusionThe algorithm for HR-pQCT images detects cortical interruptions, and its interruption surface. Reliability and validity was comparable for the fully- and semi-automated procedures. However, we advise the use of the semi-automated procedure to assure quality. The algorithm is a promising tool for a sensitive and objective assessment of cortical interruptions in finger joints assessed by HR-pQCT.

A hybrid scheme of image compression employing wavelets and 2D-PCA

In this paper, we have presented a method of compressing 2D grey-scale images employing wavelets and two-dimensional principal component analysis (2D-PCA). Principal component analysis (PCA) is an already established technique for image compression which primarily aims at exploiting inter pixel redundancies present in the image, while wavelet is a tool widely used in multi-resolution image processing. In the proposed method the image is subjected to a multi-resolution decomposition using wavelet. Subsequently, 2D-PCA is applied on the set of detail images at each level of resolution. The compressed form of the image is constituted by representative pairs of principal components and projection vectors from each level of resolution along with the approximate image at the coarsest resolution. The proposed method requires relatively few number of principal components (of varied dimension) to produce improved compression ratio with acceptable peak signal to noise ratio (PSNR). The method has been implemented and tested on a set of real 2D grey-scale images and the results have been assessed on both qualitative and quantitative basis by measuring parameters like compression ratio (CR), PSNR, structural similarity index measurement (SSIM) and the overall performance is found to be satisfactory.

Multi-scale contrast enhancement of oriented features in 2D images using directional morphology

This paper presents a multi-scale contrast enhancement scheme for improving the visual quality of directional features present in 2D gray scale images. Directional morphological filters are employed to locate and extract the scale-specific image features with different orientations which are subsequently stored in a set of feature images. The final enhanced image is constructed by weighted combination of these feature images with the original image. While construction, the feature images corresponding to progressively smaller scales are made to have higher proportion of contribution through the use of progressively larger weights. The proposed method has been formulated, implemented and executed on a set of real 2D gray scale images with oriented features. The experimental results visually establish the efficacy of the method. The proposed method has been compared with other similar methods both on subjective and objective basis and the overall performance is found to be satisfactory.

Topology-based image segmentation using LBP pyramids

In this paper, we present a new image segmentation algorithm which is based on local binary patterns (LBPs) and the combinatorial pyramid and which preserves structural correctness and image topology. For this purpose, we define a codification of LBPs using graph pyramids. Since the LBP code characterizes the topological category (local max, min, slope, saddle) of the gray level landscape around the center region, we use it to obtain a minimal image representation in terms of the topological characterization of a given 2D grayscale image. Based on this idea, we further describe our hierarchical texture aware image segmentation algorithm and compare its segmentation output and the minimal image representation.

Evaluation of Two-Dimensional Gray-Scale Images for Microtexture Analysis of Aggregate Surface

AbstractA powerful texture feature extraction algorithm based on transform-based analysis of two-dimensional (2D) gray-scale images was employed in the aggregate image measurement system (AIMS) to quantitatively describe aggregate surface microtexture by way of a parameter called texture index (TI). TI has been used successfully to distinguish between unpolished and polished aggregates. The main goal of this research was to further evaluate this system using a broader range of aggregates in terms of mineral composition including highly uniform limestone sources and highly variable granitic sources. Results confirmed that TI successfully distinguished polished from unpolished aggregates for all mineral compositions. However, an excessively broad range of TI values was observed. Specifically, TI values of aggregates with highly variable mineral composition were far greater than any value previously reported in the literature. Interestingly, unusually high TI values were also observed for limestone aggregate...

Detailed Anatomy of the Nasolabial Muscle in Human Fetuses as Determined by Micro-CT Combined With Iodine Staining.

This study aims to investigate the 3-dimensional (3D) anatomical structure of the orbicularis oris and nasalis, which are closely associated with the appearance of the upper lip and lower part of the nose. The relationship of the complicated 3D anatomical structure with the outline shape was also determined. Microcomputed tomography combined with iodine staining was used to scan the nasolabial tissues of 3 aborted fetuses. The strictly aligned, corrected, full-capacity, 2-dimensional (2D) grayscale images obtained were then used to reconstruct 3D structures using a 3D reconstruction software. 2D grayscale slices and a 3D anatomical model of the orbicularis oris and nasalis of the specimens were obtained. The 2D images and the 3D model confirmed the orbicularis oris anatomical structure reported in previous studies and also provided new insights (such as the close association of the formation of the philtral dimple, lip peak, philtral ridge, and nasal sill with the orbicularis oris). In addition, the results show that the nasolabial muscle consists of muscle fibers from different sources and is divided into four distinct parts: pars marginalis, pars peripheralis, muscle fibers of the levator labii superioris, and nasalis muscle fibers. The 3D anatomical structures indicate that the orbicularis oris and nasalis are closely associated with the appearances of the upper lip and lower part of the nose. The results may aid plastic surgeons in performing cleft-lip correction surgery.

Abstract 15755: Adverse Cardiac Remodeling in Obese Children

Background: Obesity (OB) and hypertension (HTN) are risk factors for early cardiovascular disease (CVD). The aim of this study was to determine the effect of OB and HTN on myocardial mechanics in children using speckle-tracking echocardiography (STE). Methods: Echocardiograms (echo) and ambulatory blood pressure monitors were performed in children referred for HTN from 2009 to 2014. Traditional echo Doppler indices, mass and volume parameters, 24-hr systolic blood pressures (24SBP) and 24-hr diastolic BP indices were obtained. Post-processing was performed on the 2D gray scale images of LV in apical 4-chamber and parasternal short axis mid-papillary views to obtain strain indices (Image Arena, Tomtec). Associations of obesity and BP to the myocardial indices were explored independently using Spearman correlation coefficient. Results: Among 107 patients, 6 - 21 yrs age, 79.4% were males; 42% OB and 53% were hypertensive. Mean Z scores for BMI were 1.5 ± 1.1 and SBP were 1.5 ± 1.2. Mean LV mass (LVM, 2D area-length) was 131 + 42.8 gms, LVMI 2.7 was 40.7 + 9.6 gms, LVM/volume ratio was 0.94 + 0.17. Mean mitral septal E/e’ was 8.3 + 2.1, lateral E/e’ was 5.9 + 1.5 and mitral E/A ratio was 1.8 + 0.4. The mean global longitudinal strain (GLS) was – 17.7 + 3.6, global circumferential strain (GCS) was -20.4 + 3.8, average radial strain (ARS) was 22 + 11.9, average longitudinal strain rate (ALSR) was -1.0 + 0.35 and average circumferential strain rate (ACSR) was -1.5 + 0.5 for all patients. BMI Z scores positively correlated with LVM (r=0.38, p&lt;0.01), LVM/vol (r=0.32, p&lt;0.01), mitral E/e’ (r=0.28, p&lt;0.01), LVMI 2.7 (r=0.5, &lt;0.01). Increased BMI negatively correlated to GLS, GCS and ACSR (r=0.3, p&lt;0.01). Higher SBP, diastolic and mean BP were associated with higher mitral medial E/e’ (r=0.3, p&lt;0.01), however did not have statistically significant correlations to strain indices. Conclusions: Obesity causes adverse cardiac remodeling in obese children. This may provide insights into mechanisms of long-term CV effects of obesity. Myocardial effects of HTN were not apparent, possibly due to heterogeneity of the study group and low power.

A parallel generalized relaxation method for high-performance image segmentation on GPUs

Fast and scalable software modules for image segmentation are needed for modern high-throughput screening platforms in Computational Biology. Indeed, accurate segmentation is one of the main steps to be applied in a basic software pipeline aimed to extract accurate measurements from a large amount of images. Image segmentation is often formulated through a variational principle, where the solution is the minimum of a suitable functional, as in the case of the Ambrosio–Tortorelli model. Euler–Lagrange equations associated with the above model are a system of two coupled elliptic partial differential equations whose finite-difference discretization can be efficiently solved by a generalized relaxation method, such as Jacobi or Gauss–Seidel, corresponding to a first-order alternating minimization scheme. In this work we present a parallel software module for image segmentation based on the Parallel Sparse Basic Linear Algebra Subprograms (PSBLAS), a general-purpose library for parallel sparse matrix computations, using its Graphics Processing Unit (GPU) extensions that allow us to exploit in a simple and transparent way the performance capabilities of both multi-core CPUs and of many-core GPUs. We discuss performance results in terms of execution times and speed-up of the segmentation module running on GPU as well as on multi-core CPUs, in the analysis of 2D gray-scale images of mouse embryonic stem cells colonies coming from biological experiments.

Enhancement of morphological snake based segmentation by imparting image attachment through scale-space continuity

In this paper, we propose a new multi-scale morphological approach to curve evolution useful for object extraction through segmentation. The homogenous image structures that characterize the segmentation process are edges and terminations. Normally the conventional morphological snake (MS) technique employs morphological binary level-set operators for realizing forces. These operations handle definite components of the PDE (partial differential equation) used for modeling the dynamic system. The proposed model can segment with reasonably high level of accuracy and efficiency while ensuring smooth segmentation at object boundaries with scale space continuity. Application of discrete image force in MS is a per pixel decision based on the sign of image force PDE component. In the continuous domain however, the intensity of the image force PDE component is the primary factor for snake evolution. In our model we embed scale-space continuity into the morphological operators dictated by MS in order to realize the image force both in intensity and direction. Thus, our model confirms to the speed, agility and robustness of morphological snakes with regard to segmentation while ensuring enhanced efficiency of segmentation under noise. We have rated the performance both on qualitative and quantitative basis against benchmark results, on a set of 2D gray-scale real images both in absence and presence of noise. A comparative study has also been carried among our method, MS, geodesic active contour (GAC) and Distance Regularized Level Set Evolution (DRLSE).

Projective Invariants of D-moments of 2D Grayscale Images

This paper presents a novel method to derive invariants of 2D grayscale images under projective transformation. Invariants of images are good features for object recognition and have attracted extensive attention. Although geometric invariants of point locations such as cross ratios are well known for centuries, we have found no reported invariants for grayscale images that remain the same under projective transformation. It has even been proven that projective invariants of images cannot be derived from the standard geometric moments of images. However, this does not mean that there is no projective invariant of images in other forms. We will prove in this paper that projective invariants of images do exist as functions of the generalized moments of images. We first derive some projective invariant relations between an image function and its derivative functions. Next, we extend the traditional definition of moments by considering both the image function and its derivative functions. Then we derive a set of functions of the generalized moments that are projective invariant. Experimental results indicate that the proposed invariants have certain discriminating power for object recognition.

Scaling Transform Methods For Compressing a 2D Graphical image

Transformation is a process of converting the original picture coordinates into a different picture coordinates either by adding some values with original coordinates(Translations) or Multiplying some values with original coordinates(called Linear transformations like rotation, reflection, scaling, and shearing). In this paper, we compress a two dimensional picture using 2D scaling transformation. In the several scenarios, the utilization of this technique for image compression resulted in comparable or better performance, when compared to the Different modes of image transformations. In this paper We tried a new code for compressing an 2d gray scale image using scaling transform methods. Matlab concepts are applied to compress the image. We have plan to apply The techniques and develop a code for compressing a 3d image.

Global longitudinal strain for predicting outcome after mitral repair or cardiac surgery: here to stay?

Speckle tracking two-dimensional (2D) strain echocardiography is a novel and promising technique with multiple emerging clinical applicabilities.1,2 In most patients, the method is easy to perform during routine transthoracic echocardiography by the acquisition of the regular 2D gray-scale images, which can then be evaluated offline by the use of work stations. Strain echocardiography has been demonstrated to be useful for assessing and monitoring left ventricular (LV) systolic function in several cardiac disease entities. A major advantage of speckle tracking echocardiography is its angle-independency in comparison with tissue Doppler (TDI) which enables regional and global analysis of myocardial velocities and deformation parameters such as strain or strain rate by the use of an image-processing algorithm of the grey scale pixels. In principle, strain measures the myocardial fibre contraction magnitude, and therefore may detect subtle subclinical LV dysfunction in an early stage. LV function presents a well-established prognostic factor strongly influencing outcome in valve disease and is included in the EuroSCORE preoperative risk calculation for cardiac surgery.3 However, the echocardiographic assessment of the LV ejection fraction (EF) faces several major limitations, including load dependency and imaging quality for endocardial border delineation.1 Importantly, it has been repeatedly observed, that despite apparently preserved ‘normal’ LV EF, myocardial contractility may already be reduced, reflected by impaired longitudinal 2D strain.1,2. In the setting of severe mitral regurgitation, compensatory LV dilatation and geometry changes may sustain a normal stroke volume at rest, although myocardial contractile reserve may already deteriorate even in asymptomatic patients. Therefore, careful assessment of the LV function including longitudinal strain in …