Contrast Enhancement Technique Research Articles

Identification of Suitable Contrast Enhancement Technique for Improving the Quality of Astrocytoma Histopathological Images.

Contrast enhancement plays an important part in image processing. In histology, the application of a contrast enhancement technique is necessary since it can help pathologists in diagnosing the sample slides by increasing the visibility of the morphological and features of cells in an image. Various techniques have been proposed to enhance the contrast of microscopic images. Thus, this paper aimed to study the effectiveness of contrast enhancement techniques in enhancing the Ki67 images of astrocytoma. Three contrast enhancement techniques consist of contrast stretching, histogram equalization, and CLAHE techniques were proposed to enhance the sample images. The performance of each technique was compared by computing seven quantitative measures. The CLAHE technique was preferred for enhancing the contrast of the astrocytoma images. This technique produces good results especially in contrast enhancement, edge conservation and enhancement, brightness preservation, and minimum distortions to the enhanced images.

Pose and Illumination Invariant Hybrid Feature Extraction for Newborn

Background: Abduction, swapping and mix-ups are the unfortunate events that could happen to Newborn while in hospital premises and medical personnel are finding it difficult to curb this unfortunate incident. Accurate patient identification (ID) is essential for patient safety, especially with our smallest and most vulnerable paediatric patients. The level of security is very crucial issue in maternity ward and the problem of missing and swapping of Newborn is of prime concern to the persons involved and affected. There is a common perception in the society that nothing can be done to prevent this unfortunate tragedy. In comparison to developed nations the developing countries are facing more challenges because of overcrowding and scarcity of medical facilities in the hospital. The face of a newborn baby changes appearance in different lighting conditions in spite of various contrast enhancement techniques. The class of images which are under similar environment conditions (inter-class) may result in more differences than that of the intra-class images under different illumination conditions. This gives rise to the misclassification of images. Objective: The main objective of this paper is perform newborn face recognition in a hybrid approach. Speeded Up Robust Features (SURF) and Local Binary Pattern (LBP). Methods: The scientific contributions of the proposed work are stated below: For face recognition of newborns in a semi controlled environment. Overcoming the pose and illumination challenge. Uses single gallery image. Uses a hybrid approach to improve the results. Conclusion: In this paper new semi supervised technique is used for demonstrate the improve the performance of the newborn face recognition system in different illumination and pose conditions. In our. Results: The average Rank 1 accuracy of the proposed method is 93.65% whereas that of the existing algorithms is quite low. The proposed technique is 12.8% more accurate than LBP, 10.5% more accurate than SURF, 12.8% more accurate than LDA and 18.1% more accurate than PCA for rank1.

The improvement of cardiac multispiral computed tomography protocol for planning interventional arrhythmia management

Purpose. Comparison of computer tomography (СT) contrast enhancement (CE) protocols for optimal visualization of cardiac chamber, definition it’s influence on results of non-invasive superficial cardiac mapping.Materials and methods. The study included 93 patients with heart rhythm disorders who planned catheter ablation of arrhythmia. Noninvasive cardiac mapping was made for topical diagnostics. It includes multichannel ECG-registration and CT with intravenous СE (1st group monophasic (50 patients), 2nd group split-bolus (18 patients), 3rd group with pre-bolus (25 patients). Qualitative and quantitative (measurement of mean blood attenuation in four chambers, calculation of ventricular-myocardial contrast-to-noise ratio VM-LV и VM-RV for left ventricle (LV) and right ventricle (RV), respectively) parameters were compared between groups. Fusion of ECG and CT data was made semi-automatic with diagnostic complex «Amycard 01К».Results. Regardless of CE technique was noted sufficient and homogeneous contrast attenuation of left atrium (LA) and LV (mean blood attenuation in LA more than 278 HU, LV 250 HU, VM-LV 0,582). Enhancement of right heart was insufficient with monophasic protocol, in most cases the average CT density was lower than 200 HU, VM-RV 0,256. Split-bolus protocol improves visualization of right atrium (RA) and RV (blood density in RA 258HU, RV 227HU, VM-RV 0,541), however there was heterogeneity of RA cavity because of artifacts from superior vena cava (VC) and unenhanced blood from inferior inferior VC. Using of pre-bolus increases contrast ratio between RA myocardium and blood due to increasing CT density of blood in inferior VC (blood density 294 HU). Quality of right ventricle CE was similar to 2nd group (blood density 264 HU, VM-RV 0,565).Conclusion. CE protocols split-bolus and with pre-bolus improve visualization of right ventricle, supporting the high level enhancement of left heart. Protocol with pre-bolus is preferable for exact differentiation of right atrial endocardial contour.

Panoramic Dental Radiography Image Enhancement Using Multiscale Mathematical Morphology

Panoramic dental radiography is one of the most used images of the different dental specialties. This radiography provides information about the anatomical structures of the teeth. The correct evaluation of these radiographs is associated with a good quality of the image obtained. In this study, 598 patients were consecutively selected to undergo dental panoramic radiography at the Department of Radiology of the Faculty of Dentistry, Universidad Nacional de Asunción. Contrast enhancement techniques are used to enhance the visual quality of panoramic dental radiographs. Specifically, this article presents a new algorithm for contrast, detail and edge enhancement of panoramic dental radiographs. The proposed algorithm is called Multi-Scale Top-Hat transform powered by Geodesic Reconstruction for panoramic dental radiography enhancement (MSTHGR). This algorithm is based on multi-scale mathematical morphology techniques. The proposal extracts multiple features of brightness and darkness, through the reconstruction of the marker (obtained by the Top-Hat transformation by reconstruction) starting from the mask (obtained by the classic Top-Hat transformation). The maximum characteristics of brightness and darkness are added to the dental panoramic radiography. In this way, the contrast, details and edges of the panoramic radiographs of teeth are improved. For the tests, MSTHGR was compared with the following algorithms: Geodesic Reconstruction Multiscale Morphology Contrast Enhancement (GRMMCE), Histogram Equalization (HE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dual Sub-Image Histogram Equalization (DSIHE), Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), Quadri-Histogram Equalization with Limited Contrast (QHELC), Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Gamma Correction (GC). Experimentally, the numerical results show that the MSTHGR obtained the best results with respect to the Contrast Improvement Ratio (CIR), Entropy (E) and Spatial Frequency (SF) metrics. This indicates that the algorithm performs better local enhancements on panoramic radiographs, improving their details and edges.

Differential Mean Deviation Factor based Contrast Enhancement Technique Using DWT-SVD for Non-Contrast CT Scan Images

In proper clinical analysis and diagnosis of a patient, accurate visual representation of CT scan images plays a vital role. Due to technical constraints and poor imaging device, sometimes low contrast images can be obtained. To interpret and analyzed the minute details present in the image, the image needs to be enhanced appropriately. This proposed novel low contrast enhancement technique rely on the singular value decomposition (SVD) with discrete wavelet transform (DWT) for improving the minute visual information present in a image as well as retaining the mean brightness. By scaling the values of the singular matrix properly these existing SVD based techniques enhances the contrast of image but the contrast in case of the original CT scan image is too low and hinder proper enhancement result in poor analysis. In this suggested algorithm, the proposed differential mean-deviation factor properly equalized the singular matrix. Introduction of this parameter adjusted the singular value matrix in such a way that proper contrast enhancement is attained. Results of the simulation also reveal that the suggested approach also balanced the brightness of the picture more reliably and increases the contrast comparatively with negligible visual artifacts. It outperforms traditional methodologies of equalization of images such as GHE (Global histogram equalization) and LHE (local histogram equalization). Our proposed algorithm is evaluated and compared using different CT scan images and different algorithms based on SVD and DWT but the inclusion of our proposed factor mean deviation provides a niche over the existing ones in terms of subjective and objective analysis.

Automatic colonic polyp detection using integration of modified deep residual convolutional neural network and ensemble learning approaches

Background and ObjectiveThe increased incidence of colorectal cancer (CRC) and its mortality rate have attracted interest in the use of artificial intelligence (AI) based computer-aided diagnosis (CAD) tools to detect polyps at an early stage. Although these CAD tools have thus far achieved a good accuracy level to detect polyps, they still have room to improve further (e.g. sensitivity). Therefore, a new CAD tool is developed in this study to detect colonic polyps accurately. MethodsIn this paper, we propose a novel approach to distinguish colonic polyps by integrating several techniques, including a modified deep residual network, principal component analysis and AdaBoost ensemble learning. A powerful deep residual network architecture, ResNet-50, was investigated to reduce the computational time by altering its architecture. To keep the interference to a minimum, median filter, image thresholding, contrast enhancement, and normalisation techniques were exploited on the endoscopic images to train the classification model. Three publicly available datasets, i.e., Kvasir, ETIS-LaribPolypDB, and CVC-ClinicDB, were merged to train the model, which included images with and without polyps. ResultsThe proposed approach trained with a combination of three datasets achieved Matthews Correlation Coefficient (MCC) of 0.9819 with accuracy, sensitivity, precision, and specificity of 99.10%, 98.82%, 99.37%, and 99.38%, respectively. ConclusionsThese results show that our method could repeatedly classify endoscopic images automatically and could be used to effectively develop computer-aided diagnostic tools for early CRC detection.

Improved Method of Contrast-enhanced Ultrasound examination of the Kidneys in Patients with Type 2 Diabetes Mellitus

Aim of the research. Тo evaluate the diagnostic efficacy of the contrast-enhanced ultrasound examination of kidneys in patients with type 2 diabetes mellitus with a dose of 0.5 ml of contrast agent administered.Materials and methods. We examined 12 patients with a verified diagnosis of type 2 diabetes. The age of the examined patients was 31–59 years, the mean age was 49 ± 1.3 years. All patients underwent complex diagnostics, including ultrasound examination of the kidneys in B-mode with further renal vessels color Doppler imaging to assess vessels hemodynamics. A contrast-enhanced ultrasound examination of the kidneys was carried out using Sonovue contrast agent at the doses of 2.5 ml (according to the European Good Clinical Practice Recommendations for Contrast-Enhanced Ultrasound) and 0.5 ml (an improved technique of contrast agent intravenous bolus administration).Results. The improved technique (0.5 ml of contrast agent intravenous bolus administration) in comparison with the European Recommendations technique (2.5 ml of contrast agent intravenous bolus administration) used in patients with type 2 diabetes showed no or minor differences in the qualitative and quantitative indicators of contrast-enhanced ultrasound examination which did not affect the interpretation of the results.Conclusion. The improved contrast-enhanced ultrasound examination technique using 0.5 ml of a contrast agent showed its possibilities in diagnosing diabetic nephropathy in patients with type 2 diabetes, and also revealed the potential of an economically beneficial distribution of a contrast agent without losing the quality and information content of the study.

CT Guided Diagnosis: Cascaded U-Net for 3D Segmentation of Liver and Tumor

Volumetric estimation of the liver tumor is the first step to identifying the livers critical disorder. The liver and its tumor ratio prerequisite measures to select the therapeutic procedure. 3D printing and virtual reality platform require a segmented liver entity mask to evaluate the pre and post-treatment analysis. A cascaded U-Net model is proposed for automatic segmentation of liver and tumor in CT images. LiTS CT data set utilized for this study. The images were pre-processed using the windowing technique for contrast enhancement. Two U-Net models were modified for liver and tumor segmentation, respectively and connected in a cascaded manner. U-Net decoder end was modified in comparison to the original U-Net. The probability map of the first U-Net fed to the second U-Net and the input image to segment out the liver tumor. Eight subject volumetric CT datasets were utilized to test the cascaded U-Net performance and achieved average Dice coefficient for liver and tumor 0.95 and 0.69, respectively. Liver tumor diagnosis and treatment accuracy depend upon the precision of segmentation algorithms. Designed model segmented liver almost accurately and tumor segmented with limited accuracy. A further modification is required for the tumor segmentation cause of the occurrence of false negative.

A novel brightness preserving joint histogram equalization technique for contrast enhancement of brain MR images

Low contrast is a challenging factor in brain magnetic resonance (MR) images due to its structural complexity. Histogram equalization (HE) approach is often used in enhancing the contrast in brain MR images. However, the spatial information is not taken into account in this approach. Further, the problem of preserving structural details while retaining the brightness is also an important concern. To solve these, we suggest a novel stationary wavelet transform based brightness preserving joint histogram equalization (SWT-BPJHE) scheme for brain MR image contrast enhancement. Our contributions are – i) use of SWT to extract the low sub-band wavelength coefficients from the low contrast input image for enhancement, ii) to isolate the high frequency wavelength coefficients from enhancement, retaining the structural details, iii) to preserve brightness. The suggested scheme is experimented with synthetic brain MR images from BrainWeb and clinical images from Howard Whole Atlas databases. The performance is evaluated in terms of several validation indices followed by statistical analysis. The outcomes reveal the superiority of the suggested scheme in comparison to state-of-the-art methods.

Optimization of follow-up MRI protocol for malignant neoplasms of the female pelvis

Introduction. MRI is widely used to follow-up malignant neoplasms of the female pelvis. Aim. To optimize the use of MRI sequences for follow-up of female patients with malignant pelvic neoplasms. Materials and methods. Retrospective analysis of female pelvic MRI has been performed on 1.5 T MR scanner. This study includes 530 protocols. Results. The authors describe different protocols for assessment and differential diagnosis of recurrence, postradiation and postoperative changes. It includes applicable sequences, axis and the structure which require evaluation. In all cases, dynamic follow-up has to include analysis of patient's previous studies, anamnesis, previous treatment, as well as the end of chemotherapy and radiotherapy. All of the above, combined with the oncologist's treatment of choice determines the objective of this study. Nowadays, MRI is the method which most accurately characterizes postoperative and postradiation structural changes. MRI is useful in detection of pelvic lymph nodes. The role of MRI has high potential in assessing the effectiveness of chemotherapy and radiation therapy. Conclusion. In order to make the optimal scan protocol, follow-up of malignant neoplasms of the pelvic organs in women require the use of clinical and anamnestic data along with the results of patient's previous studies. The combined use of various sequences and techniques can improve the diagnostic significance of pelvic MRI at follow-up. Contrast enhancement has additional role in diagnostics of recurrent of pelvic organs in women. The use of different contrast enhancement techniques has proven effective in case of postradiation and postoperative complications.

Noninvasive assessment of radiation-induced renal injury in mice

Purpose The kidney is a radiosensitive late-responding normal tissue. Injury is characterized by radiation nephropathy and decline of glomerular filtration rate (GFR). The current study aimed to compare two rapid and cost-effective methodologies of assessing GFR against more conventional biomarker measurements. Methods C57BL/6 mice were treated with bilateral focal X-irradiation (1x14Gy or 5x6Gy). Functional measurements of kidney injury were assessed 20 weeks post-treatment. GFR was estimated using a transcutaneous measurement of fluorescein-isothiocyanate conjugated (FITC)-sinistrin renal excretion and also dynamic contrast-enhanced CT imaging with a contrast agent (ISOVUE-300 Iopamidol). Results Hematoxylin and eosin (H&E) and Periodic acid-Schiff staining identified comparable radiation-induced glomerular atrophy and mesangial matrix accumulation after both radiation schedules, respectively, although the fractionated regimen resulted in less diffuse tubulointerstitial fibrosis. Albumin-to-creatinine ratios (ACR) increased after irradiation (1x14Gy: 100.4 ± 12.2 µg/mg; 6x5Gy: 80.4 ± 3.02 µg/mg) and were double that of nontreated controls (44.9 ± 3.64 µg/mg). GFR defined by both techniques was negatively correlated with BUN, mesangial expansion score, and serum creatinine. The FITC-sinistrin transcutaneous method was more rapid and can be used to assess GFR in conscious animals, dynamic contrast-enhanced CT imaging technique was equally safe and effective. Conclusion This study demonstrated that GFR measured by dynamic contrast-enhanced CT imaging is safe and effective compared to transcutaneous methodology to estimate kidney function.

Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images

Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.

The role of dynamic and diffusion MR imaging in therapeutic response assessment after microwave ablation of hepatocellular carcinoma using LI-RADS v2018 treatment response algorithm

BackgroundHepatocellular carcinoma (HCC) is considered as one of the major causes of morbidity and mortality worldwide. Microwave ablation (MWA) is a widely used treatment option having less morbidity and complications as compared with surgery and liver transplantation. MRI is the most widely used modality in the assessment of treatment response after MWA. Currently, LI-RADS v2018 algorithm is considered the cornerstone in daily clinical practice for assessment of the treatment response after locoregional therapy. The aim of the study was to assess the role of dynamic MRI and diffusion imaging in the assessment of treatment response and detection of tumor viability following microwave ablation therapy of HCC according to LI-RADS v2018 treatment response algorithm.ResultsThis retrospective study was performed over 45 HCC lesions underwent MWA as the only therapeutic procedure and followed up by dynamic MRI with diffusion images and then classified according to the LI-RADS treatment response criteria into LR-TR viable and LR-TR nonviable groups. All the malignant lesions found in this study showed arterial phase hyperenhancment (APHE), whether in the early or late arterial phases. Delayed washout was found in all malignant lesions as well. In the diffusion analysis, the mean ADC value for the malignant lesions was 0.900 ± 0.126 × 10-3 mm2/s, while the mean ADC of the treatment-related specific benign parenchymal enhancement was 1.284 ± 0.129 × 10-3 mm2/s with a significant statistical difference in between (P = 0.0001) and a cutoff value of 1.11 × 10-3 mm2/s. Our findings showed that the dynamic MRI has 100% sensitivity, 93.5% specificity, 87.5% PPV, and 100% NPV in the detection of tumoral activity compared with 71.43% sensitivity, 93.55% specificity, 83.33% PPV, and 87.88% NPV for diffusion images.ConclusionLI-RADS 2018 provides a treatment response algorithm superior to the previously used assessment criteria. MRI with dynamic contrast-enhanced technique and diffusion imaging provide a powerful tool in the evaluation of treatment response after microwave ablation of hepatocellular carcinoma using the LI-RADS treatment response criteria and is considered a reliable method in differentiating between the recurrent or residual malignant lesions and the posttreatment benign liver changes.

Underwater image enhancement using contrast limited adaptive histogram equalization and layered difference representation

Underwater images, which have low contrast and visibility as a result of selective attenuation based on the wavelength of the light passing through water, needs some corrections to extract meaningful information from them. In this paper, we aim to combine two different approaches; global and local contrast enhancement techniques, to obtain better visual quality while enhancing image contrasts on underwater images. While global technique (LDR) ensures the overall enhancement of the image, local technique (CLAHE) considers local brightness features of the image in RGB color space. The proposed method also applies local color correction on underwater image. While methods in the literature apply various approaches on the global histogram of channels, our method divides underwater image into non-overlapping sub-blocks and apply histogram equalization on them. The method uses HSV color space and especially S, V components for color correction. The results of the qualitative analysis show that it produces very good images, in contrast, color, and detail compared to other enhancement methods. The proposed method also decreases the effect of under- and over-enhanced areas and the blue-green effect on the output image. However, the visibility of the objects in the images are increased by color correction. For quantitative analysis, the proposed method produces the highest average value of entropy (7.83), EMEE (32.06), EME (40.97), average gradient (152.55), and Sobel count (130393) for 200 underwater images.

Contrast Enhancement Technique using Discrete Wavelet Transform with Just Noticeable Difference Model for 3D Stereoscopic Degraded Video

The Video Technologies for Medical, cultural, and social activities prefer 3D visual data rendering and processing. So 3D videos are captured by any capturing devices, like the digital cameras are not acceptable all the time due to the lack of capturing devices or indecent illumination or due to poor weather surroundings like Low light, rain, fog, mist, etc. reduces the contrast, thus the videos get degraded. 3D video contrast enhancement technique is an essential process for upgrading the quality and information content in the videos. The proposed work employs a discrete wavelet transform based enhancement technique with Jut noticeable difference model to improve the video frames and it is simple and computationally inexpensive. The application of DWT results in the Low and High-frequency sub-bands. The low-frequency components that contain the greatest amount of the information are improved using weighted threshold histogram equalization(WTHE) with the JND model algorithm while the high-frequency sub-bands are distortions and highly affected by noise. The Gaussian high pass filter is applied to each high-frequency sub-bands to remove the noise. Besides, enhancement gain control and luminance preservation are used to acquire the enhanced output video. At the end check the quality of the degraded video frame, the presented work is implemented in MATLAB 2018a and evaluated using objective parameters. Experimental results show that the proposed method can generate better and agreeable results than 2D videos.

Возможности применения стратификационной системы O-RADS и ультразвукового исследования с контрастным усилением в дифференциальной диагностике опухолей яичников

Objective. To study the effectiveness of gray-scale ultrasound imaging (according to O-RADS) and contrast-enhanced ultrasound (CEUS) in the diagnosis of ovarian tumors. Patients and methods. A total of 100 patients with ovarian neoplasms were observed, in whom the diagnosis was confirmed by morphological examination. Patients were divided into two groups: group 1 (n = 67) met the O-RADS 2-3 criteria (low risk of malignancy), and group 2 (n = 33) met the O-RADS 4-5 criteria (intermediate and high risk of malignancy). Results. The mean age of patients was 32.7 ± 6.72 years. Sensitivity (Se) of O-RADS was 84.8% (95% CI: 80-88%), specificity (Sp) of O-RADS was 88.1% (95% CI: 85-91%). Sensitivity of CEUS was 90.9% (95% CI: 85-95%), specificity of CEUS was 92.5% (95% CI: 89-97%). In ROC analysis of sensitivity and specificity of CEUS, AUC = 0.895; in analysis of sensitivity and specificity of O-RADS, AUC = 0.744 (p ≤ 0.005). Conclusion. Given its high sensitivity and specificity, the O-RADS stratification system should be used in the interpretation of ultrasound imaging of any ovarian neoplasm. The CEUS can be used as an alternative to other, significantly more expensive contrast-enhanced imaging techniques, such as CT or MRI, in the differential diagnosis of “complicated” ovarian tumors. Key words: diagnosis, contrast-enhanced ultrasound, ovarian tumor, ultrasound, O-RADS

Contrast and Synthetic Multiexposure Fusion for Image Enhancement.

Many hardware and software advancements have been made to improve image quality in smartphones, but unsuitable lighting conditions are still a significant impediment to image quality. To counter this problem, we present an image enhancement pipeline comprising synthetic multi-image exposure fusion and contrast enhancement robust to different lighting conditions. In this paper, we propose a novel technique of generating synthetic multi-exposure images by applying gamma correction to an input image using different values according to its luminosity for generating multiple intermediate images, which are then transformed into a final synthetic image by applying contrast enhancement. We observed that our proposed contrast enhancement technique focuses on specific regions of an image resulting in varying exposure, colors, and details for generating synthetic images. Visual and statistical analysis shows that our method performs better in various lighting scenarios and achieves better statistical naturalness and discrete entropy scores than state-of-the-art methods.

A non-uniform quantization scheme for visualization of CT images.

Medical science heavily depends on image acquisition and post-processing for accurate diagnosis and treatment planning. The introduction of noise degrades the visual quality of the medical images during the capturing process, which may result in false perception. Therefore, medical image enhancement is an essential topic of research for the improvement of image quality. In this paper, a clustering-based contrast enhancement technique is presented for computed tomography (CT) images. Our approach uses the recursive splitting of data into clusters targeting the maximum error reduction in each cluster. This leads to grouping similar pixels in every cluster, maximizing inter-cluster and minimizing intra-cluster similarities. A suitable number of clusters can be chosen to represent high precision data with the desired bit-depth. We use 256 clusters to convert 16-bit CT scans to 8-bit images suitable for visualization on standard low dynamic range displays. We compare our method with several existing contrast enhancement algorithms and show that the proposed technique provides better results in terms of execution efficiency and quality of enhanced images.

An Automated Framework for Patient Identification and Verification Using Deep Learning

Automated patient identification and verification are very important at a medical emergency and when patients are not carrying his/her identity. It is a risk factor that identifying the correct patient identity for doctors to provide medical treatment. The majority of the identification or verification is being done by wristbands, RFID tags, fingerprint, face detection by using handcraft feature-based face recognition systems. A new framework based on robust deep learning model and contrast enhancement is proposed in this paper. In the proposed work, the light illumination problem has been addressed by the contrast enhancement technique for deep learning models to recognize the face. It is proved that the inclusion of contrast enhancement is improving patient identification and verification. To evaluate the deep learning framework, the proposed deep learning models have been trained on our own dataset and have been tested with a real-time medical providing agency. The experimental results show that the proposed framework exhibits more robust test results with accuracy than existing hand-crafted techniques under the live webcam video capture for the real-time patient detection system.

EXPLORING SPONGE-INHABITING BARNACLES OF EASTERN INDONESIA USING MICRO-CT SCANNING

We present a morphological study of Indonesian sponge-inhabiting barnacles using standard light microscopy in combination with micro-CT scanning and computer-aided 3D-reconstruction of the external shell morphology. A taxonomic analysis of the material detected four different genera of sponges inhabited by five different species of balanomorph barnacles, two of which are undescribed. Together with conventional morphological examination by dissection, we provide modern non-destructive imaging methods, using micro-CT scanning to enhance our knowledge of the morphological characters of sponge-inhabiting barnacles from eastern Indonesia. Although there were some methodological limitations regarding the contrast-enhancing technique, this study demonstrates micro-CT as a useful non-destructive technique of integrative taxonomy, for the examination of sponge-inhabiting barnacles.