Enhancer for ovarian cyst segmentation using adaptive thresholding technique

Objective: To achieve the accurate segmentation of ovarian cyst from the ultrasound images. Method: Ovarian cyst ultrasound images are taken from ultrasound images.com and sonoworld.com. The cysts are segmented using adaptive thresholding technique. The segmented image (binary image) is divided into sub blocks and then number of binary transition in each block is calculated. Based on the number of transition, the pixel values are replaced by 0 or the same pixel value is maintained. In order to measure the performance of the proposed enhancer various measures like Accuracy (ACC), Dice Coefficient (DC), Jaccard Similarity Index (JSI), Matthews correlation coefficient (MCC), Sensitivity, Specificity and Precision are measured. Findings: In order to analyse the performance of the enhancer with adaptive thresholding technique, 100 ultrasound ovarian cyst images are taken. The enhancer produced better result than the existing adaptive thresholding technique. Novelty/Application: The proposed enhancer enriches the quality of the ovarian cyst segmentation.

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Self-Learning Network-based segmentation for real-time brain M.R. images through HARIS

PeerJ Computer Science ◽

10.7717/peerj-cs.654 ◽

2021 ◽

Vol 7 ◽

pp. e654

Author(s):

Parvathaneni Naga Srinivasu ◽

Valentina Emilia Balas

Keyword(s):

Learning Strategies ◽

Matthews Correlation Coefficient ◽

Similarity Index ◽

Training Model ◽

Initial Assessment ◽

Jaccard Similarity ◽

Learning Network ◽

Proposed Model ◽

Sensitivity Specificity ◽

Self Learning

In recent years in medical imaging technology, the advancement for medical diagnosis, the initial assessment of the ailment, and the abnormality have become challenging for radiologists. Magnetic resonance imaging is one such predominant technology used extensively for the initial evaluation of ailments. The primary goal is to mechanizean approach that can accurately assess the damaged region of the human brain throughan automated segmentation process that requires minimal training and can learn by itself from the previous experimental outcomes. It is computationally more efficient than other supervised learning strategies such as CNN deep learning models. As a result, the process of investigation and statistical analysis of the abnormality would be made much more comfortable and convenient. The proposed approach’s performance seems to be much better compared to its counterparts, with an accuracy of 77% with minimal training of the model. Furthermore, the performance of the proposed training model is evaluated through various performance evaluation metrics like sensitivity, specificity, the Jaccard Similarity Index, and the Matthews correlation coefficient, where the proposed model is productive with minimal training.

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Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

Scientific Programming ◽

10.1155/2021/3471327 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Zhuxiang Shen ◽

Wei Li ◽

Hui Han

Keyword(s):

Wavelet Transform ◽

Image Denoising ◽

Similarity Index ◽

Speckle Noise ◽

Disease Diagnosis ◽

Function Optimization ◽

Threshold Function ◽

Ultrasound Images ◽

Better Than

To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.

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Denoising Ultrasound Medical Images

Medical Imaging ◽

10.4018/978-1-5225-0571-6.ch031 ◽

2017 ◽

pp. 761-775

Author(s):

A.S.C.S. Sastry ◽

P.V.V. Kishore ◽

Ch. Raghava Prasad ◽

M.V.D. Prasad

Keyword(s):

Image Quality ◽

Medical Images ◽

Ultrasound Image ◽

Similarity Index ◽

Speckle Noise ◽

Ultrasound Images ◽

Medical Ultrasound ◽

Soft Thresholding ◽

Block Based

Medical ultrasound imaging has revolutioned the diagnostics of human body in the last few decades. The major drawback of ultrasound medical images is speckle noise. Speckle noise in ultrasound images is because of multiple reflections of ultrasound waves from hard tissues. Speckle noise degrades the medical ultrasound images lessening the visible quality of the image. The aim of this paper is to improve the image quality of ultrasound medical images by applying block based hard and soft thresholding on wavelet coefficients. Medical ultrasound image transformation to wavelet domain uses debauchee's mother wavelet. Divide the approximate and detailed coefficients into uniform blocks of size 8×8, 16×16, 32×32 and 64×64. Hard and soft thresholding on these blocks of approximate and detailed coefficients reduces speckle noise. Inverse transformation to original spatial domain produces a noise reduced ultrasound image. Experiments on medical ultrasound images obtained from diagnostic centers in Vijayawada, India show good improvements to ultrasound images visually. Quality of improved images in measured using peak signal to noise ratio (PSNR), image quality index (IQI), structural similarity index (SSIM).

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Denoising Ultrasound Medical Images

International Journal on Measurement Technologies and Instrumentation Engineering ◽

10.4018/ijmtie.2015010101 ◽

2015 ◽

Vol 5 (1) ◽

pp. 1-14

Author(s):

A.S.C.S.Sastry ◽

P.V.V.Kishore MIEE ◽

Ch.Raghava Prasad ◽

M.V.D.Prasad

Keyword(s):

Image Quality ◽

Medical Images ◽

Ultrasound Image ◽

Similarity Index ◽

Speckle Noise ◽

Inverse Transformation ◽

Ultrasound Images ◽

Medical Ultrasound ◽

Soft Thresholding

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Estimating OLED Display Device Lifetime from pixel and screen brightness and its application

Color and Imaging Conference ◽

10.2352/issn.2169-2629.2019.27.60 ◽

2019 ◽

Vol 2019 (1) ◽

pp. 331-338 ◽

Cited By ~ 1

Author(s):

Jérémie Gerhardt ◽

Michael E. Miller ◽

Hyunjin Yoo ◽

Tara Akhavan

Keyword(s):

Image Processing ◽

Power Consumption ◽

Model Parameters ◽

Display Device ◽

Image Processing Algorithm ◽

Pixel Value ◽

Device Lifetime ◽

The Impact ◽

Oled Display

In this paper we discuss a model to estimate the power consumption and lifetime (LT) of an OLED display based on its pixel value and the brightness setting of the screen (scbr). This model is used to illustrate the effect of OLED aging on display color characteristics. Model parameters are based on power consumption measurement of a given display for a number of pixel and scbr combinations. OLED LT is often given for the most stressful display operating situation, i.e. white image at maximum scbr, but having the ability to predict the LT for other configurations can be meaningful to estimate the impact and quality of new image processing algorithms. After explaining our model we present a use case to illustrate how we use it to evaluate the impact of an image processing algorithm for brightness adaptation.

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Restaging of patients with lymphoma

Nuklearmedizin ◽

10.3413/nukmed-0114 ◽

2008 ◽

Vol 47 (01) ◽

pp. 37-42 ◽

Cited By ~ 12

Author(s):

T. Pfluger ◽

V. Schneider ◽

M. Hacker ◽

N. Bröckel ◽

D. Morhard ◽

...

Keyword(s):

Low Dose ◽

Non Hodgkin Lymphoma ◽

Contrast Enhanced ◽

Pet Ct ◽

Significant Difference ◽

18F Fdg ◽

Sensitivity Specificity ◽

Fdg Pet Ct

SummaryAim: Assessment of the clinical benefit of i.v. contrast enhanced diagnostic CT (CE-CT) compared to low dose CT with 20 mAs (LD-CT) without contrast medium in combined [18F]-FDG PET/CT examinations in restaging of patients with lymphoma. Patients, methods: 45 patients with non-Hodgkin lymphoma (n = 35) and Hodgkin's disease (n = 10) were included into this study. PET, LD-CT and CECT were analyzed separately as well as side-by-side. Lymphoma involvement was evaluated separately for seven regions. Indeterminate diagnoses were accepted whenever there was a discrepancy between PET and CT findings. Results for combined reading were calculated by rating indeterminate diagnoses according the suggestions of either CT or PET. Each patient had a clinical follow-up evaluation for >6 months. Results: Region-based evaluation suggested a sensitivity/specificity of 66/93% for LD-CT, 87%/91% for CE-CT, 95%/96% for PET, 94%/99% for PET/LD-CT and 96%/99% for PET/CE-CT. The data for PET/CT were obtained by rating indeterminate results according to the suggestions of PET, which turned out to be superior to CT. Lymphoma staging was changed in two patients using PET/ CE-CT as compared to PET/LD-CT. Conclusion: Overall, there was no significant difference between PET/LD-CT and PET/CE-CT. However, PET/CE-CT yielded a more precise lesion delineation than PET/LD-CT. This was due to the improved image quality of CE-CT and might lead to a more accurate investigation of lymphoma.

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Neutral theory: applicability and neutrality of using generic health-related quality of life tools in diseases or conditions where specific tools are available

BMC Medical Research Methodology ◽

10.1186/s12874-021-01279-w ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Ravi Jandhyala

Keyword(s):

Rare Disease ◽

Neutral Theory ◽

Health Related Quality ◽

Poor Hrqol ◽

Sf 36 ◽

Disease Condition ◽

Related Quality ◽

Health Related ◽

Sensitivity Specificity

Abstract Background Health-related quality of life (HRQoL) tools are limited by the indicators included in the construct and variation in interpretation by different researchers. Neutral Theory describes the ideal construct that includes all relevant indicators and, therefore, complete accuracy, or neutrality. Neutral Theory can thereby provide the framework to develop or test constructs. To assess the application of Neutral Theory, the neutrality of generic tools (SF-36 and EQ-5D) at measuring HRQoL was compared to disease/condition-specific tools, with the latter considered surrogates for the Neutral construct. Methods Full descriptions of all disease/condition-specific HRQoL tools published on PubMed (to 01-Jul-19) were sourced. For each tool, the number of items with and without a direct match within the SF-36 and EQ-5D was recorded and the sensitivity/specificity calculated. Results The SF-36 and EQ-5D did not achieve a sensitivity/specificity both > 50% against any of the 163 disease/condition-specific tools identified. At 20% prevalence of poor HRQoL, the false positive rate (FPR) was > 75% for all but two tools against the SF-36 and six tools against the EQ-5D. Increasing poor HRQoL to 80%, 47 tools for the SF-36 and 48 tools for the EQ-5D had a FPR < 50%. For rare disease tools (< 1/2000 population; n = 17), sensitivity/specificity ranged from 0 to 40%/5–31% for the SF-36 and 0–22%/29–100% for the EQ-5D. For non-rare (n = 75) and symptom-specific tools (n = 71) sensitivity/specificity was: 0–100%/0–100% (SF-36) and 0–50%/0–100% (EQ-5D); and 0–60%/0–19% (SF-36) and 0–25%/0–100% (EQ-5D), respectively. No concordance was recorded for 18% (2/11) of results from studies of rare disease tools versus the SF-36 (no data vs EQ-5D). For non-rare, disease-specific tools, results were discordant for 30% (25/84) and 35% (23/65) of studies against the SF-36 and EQ-5D, respectively. For symptom-specific tools, corresponding results were 36% (24/66) and 16% (5/31). Conclusions Generic HRQoL tools appear poorly correlated with disease/condition-specific tools, which indicates that adoption of Neutral Theory in the development and assessment of HRQoL tools could improve their relevance, accuracy, and utility in economic evaluations of health interventions.

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Development and assessment of a telesonography system for musculoskeletal imaging

European Radiology Experimental ◽

10.1186/s41747-021-00227-z ◽

2021 ◽

Vol 5 (1) ◽

Author(s):

Mohammed Obaid ◽

Qianwei Zhang ◽

Scott J. Adams ◽

Reza Fotouhi ◽

Haron Obaid

Keyword(s):

Degrees Of Freedom ◽

Ultrasound Images ◽

Ultrasound Probe ◽

Rural And Remote ◽

Significant Delay ◽

Remote Communities ◽

Internet Connection ◽

Good For ◽

4 Degrees Of Freedom

Abstract Background Telesonography systems have been developed to overcome barriers to accessing diagnostic ultrasound for patients in rural and remote communities. However, most previous telesonography systems have been designed for performing only abdominal and obstetrical exams. In this paper, we describe the development and assessment of a musculoskeletal (MSK) telesonography system. Methods We developed a 4-degrees-of-freedom (DOF) robot to manipulate an ultrasound probe. The robot was remotely controlled by a radiologist operating a joystick at the master site. The telesonography system was used to scan participants’ forearms, and all participants were conventionally scanned for comparison. Participants and radiologists were surveyed regarding their experience. Images from both scanning methods were independently assessed by an MSK radiologist. Results All ten ultrasound exams were successfully performed using our developed MSK telesonography system, with no significant delay in movement. The duration (mean ± standard deviation) of telerobotic and conventional exams was 4.6 ± 0.9 and 1.4 ± 0.5 min, respectively (p = 0.039). An MSK radiologist rated quality of real-time ultrasound images transmitted over an internet connection as “very good” for all telesonography exams, and participants rated communication with the radiologist as “very good” or “good” for all exams. Visualisation of anatomic structures was similar between telerobotic and conventional methods, with no statistically significant differences. Conclusions The MSK telesonography system developed in this study is feasible for performing soft tissue ultrasound exams. The advancement of this system may allow MSK ultrasound exams to be performed over long distances, increasing access to ultrasound for patients in rural and remote communities.

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Fully Data-Driven Pseudohealthy Synthesis for Planning Valve-Sparing Aortic Root Reconstruction using Conditional Variational Autoencoders

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-3072 ◽

2020 ◽

Vol 6 (3) ◽

pp. 284-287

Author(s):

Jannis Hagenah ◽

Mohamad Mehdi ◽

Floris Ernst

Keyword(s):

Aortic Root ◽

Similarity Index ◽

Ground Truth ◽

Representation Learning ◽

Patient Specific ◽

Ultrasound Images ◽

Specific Geometry ◽

The Individual ◽

Native Root ◽

Original Information

AbstractAortic root aneurysm is treated by replacing the dilated root by a grafted prosthesis which mimics the native root morphology of the individual patient. The challenge in predicting the optimal prosthesis size rises from the highly patient-specific geometry as well as the absence of the original information on the healthy root. Therefore, the estimation is only possible based on the available pathological data. In this paper, we show that representation learning with Conditional Variational Autoencoders is capable of turning the distorted geometry of the aortic root into smoother shapes while the information on the individual anatomy is preserved. We evaluated this method using ultrasound images of the porcine aortic root alongside their labels. The observed results show highly realistic resemblance in shape and size to the ground truth images. Furthermore, the similarity index has noticeably improved compared to the pathological images. This provides a promising technique in planning individual aortic root replacement.

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Evaluation of alpha-l-fucosidase for the diagnosis of hepatocellular carcinoma based on meta-analysis

LaboratoriumsMedizin ◽

10.1515/labmed-2019-0152 ◽

2020 ◽

Vol 0 (0) ◽

Cited By ~ 1

Author(s):

Lei Xi ◽

Chunqing Yang

Keyword(s):

Hepatocellular Carcinoma ◽

Operating Characteristic ◽

Meta Analysis ◽

Area Under The Curve ◽

Diagnostic Odds Ratio ◽

Diagnostic Value ◽

Receiver Operating Characteristic Curves ◽

Sensitivity Specificity ◽

Review Manager

AbstractObjectivesThe main aim of the present study was to assess the diagnostic value of alpha-l-fucosidase (AFU) for hepatocellular carcinoma (HCC).MethodsStudies that explored the diagnostic value of AFU in HCC were searched in EMBASE, SCI, and PUBMED. The sensitivity, specificity, and DOR about the accuracy of serum AFU in the diagnosis of HCC were pooled. The methodological quality of each article was evaluated with QUADAS-2 (quality assessment for studies of diagnostic accuracy 2). Receiver operating characteristic curves (ROC) analysis was performed. Statistical analysis was conducted by using Review Manager 5 and Open Meta-analyst.ResultsEighteen studies were selected in this study. The pooled estimates for AFU vs. α-fetoprotein (AFP) in the diagnosis of HCC in 18 studies were as follows: sensitivity of 0.7352 (0.6827, 0.7818) vs. 0.7501 (0.6725, 0.8144), and specificity of 0.7681 (0.6946, 0.8283) vs. 0.8208 (0.7586, 0.8697), diagnostic odds ratio (DOR) of 7.974(5.302, 11.993) vs. 13.401 (8.359, 21.483), area under the curve (AUC) of 0.7968 vs. 0.8451, respectively.ConclusionsAFU is comparable to AFP for the diagnosis of HCC.

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