Selected Regions Of Interest Research Articles

Evaluation of Renal Tissue Oxygenation Using Blood Oxygen Level-Dependent Magnetic Resonance Imaging in Chronic Kidney Disease

Background: Blood oxygen level-dependent magnetic resonance imaging (BOLD-MRI) has been widely used to assess renal oxygenation changes in different kidney diseases in recent years. This study was designed to evaluate and compare renal tissue oxygenation using 2 BOLD-MRI analysis methods, namely, the regional and whole-kidney region of interest (ROI) selection methods. Methods: The study ended up with 10 healthy controls and 40 chronic kidney disease (CKD) patients without dialysis. Their renal BOLD-MRI data were analyzed using whole-kidney ROI selection method and compared with regional ROI selection method. Results: We found the cortical, medullary, and whole-kidney R2* values were significantly higher in CKD patients than those in controls. Compared with the regional ROI selection method, the whole-kidney ROI selection method yielded higher cortical R2* values in both controls and CKD patients. The whole-kidney R2* values of deteriorating renal function group were significantly higher than those in stable renal function group. Conclusions: Cortical and medullary oxygenation was decreased significantly in CKD patients compared with the healthy controls, particularly in the medulla. The whole-kidney R2* values were positively correlated with kidney function and inversely correlated with the estimated glomerular filtration rate and effective renal plasma flow. Whole-Kidney R2* value might effectively predict the progression of renal function in patients with CKD.

An Image Quality Assessment System for Evaluating MR Reconstruction Pipeline Using Single Image Acquisition Method

Image quality assessment becomes critical for measuring the performance of Magnetic Resonance (MR) image reconstruction algorithms. The raw data acquired from MR scanner is processed through a series of components in the MR reconstruction pipeline to generate an image. The MR reconstruction pipeline includes the algorithms for reconstruction, quality enhancement, artifact reduction, and so on. The implementation of the pipeline requires evaluation of large number of filters and reconstruction algorithms, which demands a simple and effective mechanism to assess the quality of output at each stage. The main goal of the proposed work is to provide research groups and MR algorithm developers with protocols and tools necessary to conveniently assess the quality of MR images and to be able to compare the reconstruction methods among each other. This paper focuses on the implementation of an image quality assessment mechanism using the single image method by specifying Region of Interest (ROI) from object and background as suggested by National Electrical Manufacturers Association (NEMA). The single image method does not produce consistent results as the SNR values change drastically based on the background noise. It has been observed that, by careful selection of noise region, the drastic changes in values of SNR can be avoided with single acquisition techniques. This mechanism is supported by a visual aiding tool to carefully select signal and noise regions and avoid the errors introduced in blind selection of ROI. Using the single acquisition method and visual aiding tool, SNR comparison of images from two different reconstruction methods can be easily evaluated.

A fuzzy based ROI selection for encryption and watermarking in medical image using DWT and SVD

Nowadays secure medical image watermarking had become a stringent task in telemedicine. This paper presents a novel medical image watermarking method by fuzzy based Region of Interest (ROI) selection and wavelet transformation approach to embed encrypted watermark. First, the source image will undergo fuzzification to determine the critical points through central and final intensity along the radial line for selecting region of interest (ROI). Second, watermark image is altered to time-frequency domain through wavelet decomposition where the sub-bands are swapped based on the magnitude value obtained through logistic mapping. In the each sub-band all the pixels get swapped, results in fully encrypted image which guarantees the watermark to a secure, reliable and an unbreakable form. In order to provide more robustness to watermark image, singular values are obtained for encrypted watermark image and key component is calculated for avoiding false positive error. Singular values of the source and watermark image are modified through key component. Experimental results reveal that the proposed algorithm attains high robustness and improved security to the watermarked image against various kinds of attacks.

Multifaceted fused-CNN based scoring of breast cancer whole-slide histopathology images

Automating the scoring of Whole-Slide Images (WSIs) is a challenging task because the search space for selecting region of interest (ROI) is huge due to the very large sizes of WSIs. A Multifaceted Fused-CNN (MF-CNN) and a Hybrid-Descriptor are proposed to develop an integrated scoring system for Breast Cancer histopathology WSIs. Suitable color and textural features are identified to help mitotic count based selection of ROIs at lower resolution. To recognize complex patterns, the MF-CNN considers multiple facets of the input image. It counts mitoses, extracts handcrafted features from ROIs and utilizes global texture of the images to form a Hybrid-Descriptor for training a classifier assigning scores to WSIs. The proposed system is evaluated on a publicly available benchmark (TUPAC16) and produced the highest score of 0.582 in terms of Cohen’s Kappa. It surpassed human experts’ level accuracy of ROI selection and can therefore reduce the burden of manual ROI selection for WSIs.

An Automated Method for Quality Control in MRI Systems: Methods and Considerations

Objective: The purpose of this study was to develop an automated method for performing quality control (QC) tests in magnetic resonance imaging (MRI) systems, investigate the effect of different definitions of QC parameters and its sensitivity with respect to variations in regions of interest (ROI) positioning, and validate the reliability of the automated method by comparison with results from manual evaluations. Materials and Methods: Magnetic Resonance imaging MRI used for acceptance and routine QC tests from five MRI systems were selected. All QC tests were performed using the American College of Radiology (ACR) MRI accreditation phantom. The only selection criterion was that in the same QC test, images from two identical sequential sequences should be available. The study was focused on four QC parameters: percent signal ghosting (PSG), percent image uniformity (PIU), signal-to-noise ratio (SNR), and SNR uniformity (SNRU), whose values are calculated using the mean signal and the standard deviation of ROIs defined within the phantom image or in the background. The variability of manual ROIs placement was emulated by the software using random variables that follow appropriate normal distributions. Results: Twenty-one paired sequences were employed. The automated test results for PIU were in good agreement with manual results. However, the PSG values were found to vary depending on the selection of ROIs with respect to the phantom. The values of SNR and SNRU also vary significantly, depending on the combination of the two out of the four standard rectangular ROIs. Furthermore, the methodology used for SNR and SNRU calculation also had significant effect on the results. Conclusions: The automated method standardizes the position of ROIs with respect to the ACR phantom image and allows for reproducible QC results.

WITHDRAWN: Cornea edge intensity thresholding using supervised clustering technique

Abstract Edge detection, Contrast missing in texture, ROI (region of interest) selection and background are some of the problems in segmentation of region wise. Implementation of various segmentation methods recommended with improved results can overcome these issues. There are some drawbacks in this method such that envisaging the similar range of pixel intensity. Threshold technique with intensity based has been proposed in this paper. This technique chooses values of intensity that exist in the line of boundary and edges & corners of the finer and thinner layers in medical images of grayscale. There are five layers in the human eye’s cornea region which can look multiple intensities over layers of boundary. Deformation of boundary layers may result in diseases of keratoconus. K-means clustering algorithm is used to group layers intensity and thresholding method which is intensity based. Efficiency and performance of edge segmentation method and process is improved by proposed approach. With the help of Matlab software, results from current existing method are compared results of proposed approach. So, intensity based thresholding method improves the System’s efficiency and performance.

Diffusion kurtosis imaging in evaluating gliomas: different region of interest selection methods on time efficiency, measurement repeatability, and diagnostic ability.

Comparing the diagnostic efficacy of diffusion kurtosis imaging (DKI) derived from different region of interest (ROI) methods in tumor parenchyma for grading and predicting IDH-1 mutation and 1p19q co-deletion status of glioma patients and correlating with their survival data. Sixty-six patients (29 females; median age, 45years) with pathologically proved gliomas (low-grade gliomas, 36; high-grade gliomas, 30) were prospectively included, and their clinical data were collected. All patients underwent DKI examination. DKI maps of each metric were derived. Three groups of ROIs (ten spots, ROI-10s; three biggest tumor slices, ROI-3s; and whole-tumor parenchyma, ROI-whole) were manually drawn by two independent radiologists. The interobserver consistency, time spent, diagnostic efficacy, and survival analysis of DKI metrics based on these three ROI methods were analyzed. The intraexaminer reliability for all parameters among these three ROI methods was good, and the time spent on ROI-10s was significantly less than that of the other two methods (p < 0.001). DKI based on ROI-10s demonstrated a slightly better diagnostic value than the other two ROI methods for grading and predicting the IDH-1 mutation status of glioma, whereas DKI metrics derived from ROI-10s performed much better than those of the ROI-3s and ROI-whole in identifying 1p19q co-deletion. In survival analysis, the model based on ROI-10s that included patient age and mean diffusivity showed the highest prediction value (C-index, 0.81). Among the three ROI methods, the ROI-10s method had the least time spent and the best diagnostic value for a comprehensive evaluation of glioma. It is an effective way to process DKI data and has important application value in the clinical evaluation of glioma. • The intraexaminer reliability for all DKI parameters among different ROI methods was good, and the time spent on ROI-10 spots was significantly less than the other two ROI methods. • DKI metrics derived from ROI-10 spots performed the best in ROI selection methods (ROI-10s, ten-spot ROIs; ROI-3s, three biggest tumor slices ROI; and ROI-whole, whole-tumor parenchyma ROI) for a comprehensive evaluation of glioma. • The ROI-10 spots method is an effective way to process DKI data and has important application value in the clinical evaluation of glioma.

Electrical equipment identification in infrared images based on ROI-selected CNN method

The thermographic evaluation method is widely used in substations to detect potential faults in advance. However, thousands of infrared images are expected for each substation. Almost all the data is collected, sorted and analyzed by humans. Therefore, to automate the data management and diagnosis, this paper proposes a method based on the deep convolutional neural network (CNN) for infrared image recognition of electrical equipment. Firstly, a deep CNN identification model is built based on MobileNet with initial weights in ImageNet. In addition, combined with the dataset augmentation including cropping, flipping, rotation and zoom, 3547 images of 500 kV substation equipment are used for training. Besides, a fine-tuning method is adopted to optimize the training. Finally, a fast region of interest (ROI) selection method based on hotspot sensitivity in infrared images is used to improve the identification accuracy. The original image aspect ratio is fixed throughout the process. Results demonstrate that the prediction accuracy of the proposed method reaches 97.72% in validation, and the ROI selection method improves the confidence by 8% in the test. As a result, this proposed method can promote the calculation efficiency, and has good application prospects in embedded devices such as cameras and substation robots.

A Comparison of Regions of Interest in Parenchymal Analysis for Breast Cancer Risk Assessment.

Computerized parenchymal analysis has shown potential to be utilized as an imaging biomarker to estimate the risk of breast cancer. Parenchymal analysis of digital mammograms is based on the extraction of computerized measures to build machine learning-based models for the prediction of breast cancer risk. However, the choice of the region of interest (ROI) for feature extraction within the breast remains an open problem. In this work we perform a comparison between five different methods suggested in the literature for automated ROI selection, including the whole breast (WB), the maximum squared (MS), the retro-areolar region (RA), the lattice-based (LB), and the polar-based (PB) selection methods. For the experiments, we built a retrospective dataset of 896 screening mammograms from 224 women (112 cases and 112 healthy controls). The performance of each ROI selection method was measured in terms of the area under the curve (AUC) values. The AUC values varied between 0.55 and 0.79 depending on the method and experimental settings. The best performance on an independent test set was achieved by the MS method (AUC of 0.59, 95% CI: 0.55-0.64). This method is fully-automated and does not require adjusting hyper-parameters. Based on our results, we prompt the use of the MS method for ROI selection in the computerized parenchymal analysis for breast cancer risk assessment.

Knowledge based fuzzy c-means method for rapid brain tissues segmentation of magnetic resonance imaging scans with CUDA enabled GPU machine

Fuzzy C-Means (FCM) plays a major role in brain tissue segmentation. The proposed method aims to implements rapid brain tissue segmentation from MRI human head scans using FCM in CPU and GPU. This method is known as FCM-GENIUS. This paper presents three novel steps to enrich the performance of conventional FCM algorithm in CPU. There are region of interest (ROI) selection, knowledge based initialization and knowledge based optimization. The ROI selection is a preprocessing step contains brain extraction and bounding box processes. An automatic knowledge based initialization to FCM algorithm using histogram smoothing for centroids selection from middle slice of the given MRI brain volume. Optimization helps to improve the computation speed up of FCM algorithm using MRI slice adjacency property. The materials used for the proposed work are gathering from internet brain segmentation repository (IBSR). The accuracy of segmentation also compared with traditional and existing methods. The proposed method yield equal segmentation accuracy compared with existing methods but reduces the segmentation time considerably up to seven times and average number of iterations up to three times. In addition, parallel FCM implements in GPU machine and the performance was compared with the conventional FCM in CPU. The single instruction multiple data (SIMD) model was used with the hybrid CPU–GPU implementation in the GPU machine to accelerate the medical image segmentation.

Traffic sign detection and recognition by improving Region of Interest (ROI) division to support driver assistance system

Traffic signs detection and recognition is a technology that allows automatic identification of traffic signs. In the implementation, it mostly uses a camera to capture video or real-time conditions. The Traffic detection and recognition system can give benefits to improve safety for drivers or passengers. Therefore, traffic signs detection and recognition system are one of the important parts in Driver Assistance Systems (DAS), besides that it is also used in video surveillance to improve traffic safety in the field of Intelligent Transportation System (ITS). In this paper, we propose a method for detecting and recognizing traffic signs by combining color and shape detection and divide the interest object to several regions for object recognition. The first step is image transformation, which aims to determine interest objects in the camera view. Such a process was using hue, saturation, value (HSV) color space, grey level, and black and white (BW) level. The next step is the filter and the segmentation process. The result of the filter and segmentation process then detects area objects by using region determination. The last step is the region of interest (ROI), which is used to recognizing sign traffic by the sum of absolute differences (SAD), and we try to improve area selection of ROI around nine parts. The results of the experiment proved that the proposed method was relatively good with the success rate for detecting objects about 97,38 %, and the true positive of recognition result about 81,67 %.

Evaluation of Thermographic Imaging in Canine Hindlimb Muscles After 6 Min of Walking-A Pilot Study.

Objective: To evaluate changes in superficial temperature of hindlimb muscles before and after a 6-min walk in healthy dogs.Methods: Two infrared thermographic images were captured of the proximal and distal hindlimbs of 11 healthy dogs before and after a 6-min walk. Orthopedic exam and objective gait analysis confirmed the healthy status of study subjects. Superficial temperatures of the gastrocnemius, biceps femoris, and gracilis were assessed. Analysis of images was performed using 2 different methods of region of interest (ROI) selection. ROI were selected first using one point (single pixel) in the muscle and then separately by selecting a line (LN) corresponding to many points of each muscle belly from which an average was taken. P < 0.05 was considered significant.Results: There was no significant change in temperature using point ROI before and after 6 min of walking of the gastrocnemius, gracilis, and biceps femoris muscles (p = 0.273, p = 0.349, p = 0.351, respectively). Using linear ROI, both biceps femoris and gracilis muscles exhibited significant increases in temperature (p < 0.0001, p = 0.032, respectively). There was no significant increase in temperature of gastrocnemius muscle for both point and linear ROI selection (p = 0.273, p = 0.448, respectively). The right biceps femoris temperatures were higher compared to left biceps femoris using the linear ROI before and after walks (p < 0.0001). The overall (left and right limbs pooled) standard deviation of point selected values were greater than LN selected values of the biceps femoris (1.35 and 1.11) and gastrocnemius (1.51 and 1.23). In contrast, standard deviation for the gracilis measurements were decreased using point selection vs. LN selection (1.09 and 1.3).Conclusions: The biceps femoris and gracilis muscles demonstrated significant increases in surface temperature after 6 min of walking using the linear method of ROI. Measurement of numerous points along the entire length of the biceps femoris and gastrocnemius muscles may provide a more accurate assessment of the increased vascularity within the tissues resulting from work compared to single point selection.Clinical Significance: Prior activity and ROI selection method should be considered when interpreting thermography results.

Quantitative analysis of dynamic contrast enhancement MRI between orbital lymphoma and inflammatory mass based on different regions of interest selection

To assess the value of dynamic contrast enhanced MRI (DCE-MRI) quantitative parameters in differentiating orbital lymphoma from inflammatory mass based on different regions of interest selection. The study population consists of 83 patients with histologically proven orbital masses (39 lymphomas and 44 inflammatory masses). Two methods of selecting regions of interest (ROI) were compared to obtain quantitative parameters: (a) a round or ovoid region of 8 mm2 covering the most enhanced area, and (b) covering the lesion but excluding areas of hemorrhage or necrosis on the slice with the largest tumor area. Parametric maps were obtained for quantitative parameters including Ktrans, Ve and Kep. Quantitative parameters that distinguish orbital lymphoma from orbital inflammatory mass were analyzed by receiver operating characteristic curves (ROC). Kep values (ROI selection method a) were significantly higher in orbital lymphoma compared with orbital inflammatory mass (p = 0.003) and Ve values (ROI selection method a and b) of orbital lymphoma were significantly lower than orbital inflammatory mass (p = 0.01, p = 0.006, respectively). Kep (ROI selection method a) could distinguish lymphoma from inflammatory mass with sensitivity (97.4%, 38/39), specificity (52.3%, 23/44), positive predictive value (64.4%, 38/59), negative predictive value (95.8%, 23/24) and accuracy (73.5%, 61/83). Ve (ROI selection method a) could distinguish lymphoma from inflammatory mass with sensitivity (54.5%, 24/44), specificity (79.5%, 31/39), positive predictive value (75%, 24/32), negative predictive value (60.8%, 31/51) and accuracy (66.3%, 55/83). Ve (ROI selection method b) distinguished lymphoma from inflammatory mass with sensitivity (48.7%, 21/44), specificity (86.4%, 34/39), positive predictive value (80.8%, 21/26), negative predictive value (59.6%, 34/57), and accuracy (66.3%, 55/83). The pairwise comparison of the AUCs among these significant quantitative parameters showed no difference in distinguishing orbital lymphoma from orbital inflammatory mass (Z = 1.066, p = 0.286, Z = 0.998, p = 0.318 and Z = 0.370, p = 0.711, respectively). Quantitative parameters can differentiate orbital lymphoma from inflammatory mass. ROI selection methods had no definite effect on the diagnostic performance.

Utility of T2 mapping in the staging of thyroid-associated ophthalmopathy: efficiency of region of interest selection methods.

Discriminating the stage of thyroid-associated ophthalmopathy (TAO) is crucial for the treatment strategy and prognosis prediction. Utility of conventional magnetic resonance imaging in the disease staging is limited. To investigate the performance of T2 mapping based on different region of interest (ROI) selection methods in the staging of TAO. Thirty-two patients with TAO were retrospectively enrolled. Two radiologists independently measured the T2 relaxation time (T2RT) of extraocular muscles using two different ROIs (hotspot [ROIHS]: T2RT-hot; single-slice [ROISS]: T2RT-mean, T2RT-max, T2RT-min). Independent-samples t test, Wilcoxon signed rank test, Spearman correlation analysis, receiver operating characteristic (ROC) curves analyses, multiple ROC comparisons, and intra-class correlation coefficient (ICC) were used for statistical analyses. No significant difference was found in the measuring time between ROIHS and ROISS methods (P = 0.066). T2RT-mean demonstrated the highest ICC for measurement, followed by T2RT-max and T2RT-min, and T2RT-hot showed the poorest reproducibility. Active TAOs showed significantly higher values for all the T2RTs than inactive mimics (all P < 0.001). Significant positive correlations were found between T2RTs and CAS (all P < 0.005). T2RT-hot and T2RT-max showed significantly higher areas under the curve than that of T2RT-mean (P = 0.013 and 0.024, respectively), while the difference between T2RT-hot and T2RT-max was not significant (P = 0.970). The T2RTs derived from both ROI selection methods could be useful for the staging of TAO. The results of measuring time, reproducibility, and diagnostic performance suggest that T2RT-max would be the optimal indicator for staging.

Estimating Departure Time Using Thermal Camera and Heat Traces Tracking Technique.

Advancement in science and technology is playing an increasingly important role in solving difficult cases at present. Thermal cameras can help the police crack difficult cases by capturing the heat trace on the ground left by perpetrators, which cannot be spotted by the naked eye. Therefore, the purpose of this study is to establish a thermalfoot model using thermal imaging system to estimate the departure time. To this end, in the current work, we use a thermal camera to acquire the thermal sequence left on the floor, and convert it into the heat signal via image processing algorithm. We establish the model of thermalfoot print as we observe that the residual temperature would exponentially decrease with the departure time according to Newton’s Law of Cooling. The correlation coefficients of 107 thermalfoot models derived from the corresponding 107 heat signals are basically above 0.99. In a validation experiment, a residual analysis is conducted and the residuals between estimated departure time points and ground-truth times are almost within a certain range from −150 s to +150 s. The reverse accuracy of the thermalfoot model for estimating departure time at one-third, one-half, two-thirds, three-fourths, four-fifths, and five-sixths capture time points are 71.96%, 50.47%, 42.06%, 31.78%, 21.70%, and 11.21%, respectively. The results of comparison experiments with two subjective evaluation methods (subjective 1: we directly estimate the departure time according to obtained local curves; subjective 2: we utilize auxiliary means such as a ruler to estimate the departure time based on obtained local curves) further demonstrate the effectiveness of thermalfoot model for detecting the departure time inversely. Experimental results also demonstrated that the thermalfoot model has good performance on the departure time reversal within a short time window someone leaves, whereas it is probably only approximately 15% to accurately determine the departure time via thermalfoot model within a long time window someone leaves. The influence of outliers, ROI (Region of Interest) selection, ROI size, different capture time points and environment temperature on the performance of thermalfoot model on departure time reversal can be explored in the future work. Overall, the thermalfoot model can help the police solve crimes to some extent, which in turn brings more guarantees for people’s health, social security, and stability.

ADPM: An Alzheimer’s Disease Prediction Model for Time Series Neuroimage Analysis

Alzheimer’s Disease (AD) is a form of dementia which causes memory, thinking, and behavior disorders in humans. Effective early diagnosis and treatment of AD is of fundamental importance as it can reduce disease progression, allow more effective management of symptoms, facilitate timely patient access to advice and support, and lower associated costs of health care. Given that Alzheimer’s typically progresses in stages over an extended period of time, we propose that automated analysis of time sequential data may enhance disease prediction. We present a novel time-series Alzheimer’s Disease Prediction Model (ADPM) comprising Random Forest (RF) region of interest (ROI) selection and Gated Recurrent Units (GRU) prediction. Experiments show that our methodology achieves higher classification accuracy in comparison to existing algorithms, and can facilitate prediction of early onset AD. Furthermore, testing demonstrates that random forest ROI selection can identify disease-relative brain regions across different image modalities (MRI, PET, DTI).

The effect of regions-of-interest and elasticity modulus selection on differentiating benign and malignant cervical lymph nodes with shear wave elastography

OBJECTIVE:Imaging diagnosis of cervical lymphadenopathy has conventionally used ultrasonography. Shear wave elastography (SWE) is a recent ultrasound technological advancement that has shown promise in the important medical problem of differentiating between benign and malignant cervical lymph nodes based on quantitative measurements of elasticity modulus. However, widely varying elasticity modulus metrics and regions-of-interest (ROIs) were used in existing studies, leading to inconsistent findings and results that are hard to compare with each other.METHODS:Using a large dataset of 264 cervical lymph nodes from 200 patients, we designed a study comparing three elasticity modulus metrics (Emax, Emean, and standard deviation-SD) with three different ROIs to evaluate the effect of such selections. The metric values were compared between the benign and malignant node groups. The different ROI and metric selections were also compared through receiver operating characteristics curve analysis.RESULTS:For all ROIs, all metric values were significantly different between the two groups, indicting their diagnostic potential. This was confirmed by the ≥0.80 area under the curve (AUC) values achieved with these metrics. Different ROIs had no effect on Emax, whereas all ROIs achieved high performance at 0.88 AUC. For Emean, the smallest ROI focusing on the area of the highest elasticity achieved the best diagnostic performance. In contrast, the larger ROIs achieved higher performances for SD.CONCLUSIONS:This study illustrated the effect of elasticity modulus and ROI selection on the diagnostic performance of SWE on cervical lymphadenopathy. These new findings help guide relevant future studies and clinical applications of this important quantitative imaging modality.

A Region of Interest Selection for Vision-Based Respiratory Rate Measurement in Sleeping Scenario

Vision-based respiratory measurement can remotely measure respiratory information without affecting the sleep quality of the user. However, several scenarios during sleeping, such as when blankets cover areas of the face or body, can be a challenge when estimate respiratory rate (RR) due to the uncertainty of the region of interest (ROI). In this study, we first investigate the metrics and physical meanings of ROI selection from the perspectives of array signal processing and the concept of linear combining. Then, we propose an ROI detection algorithm based on both temporal and spatial consistency (TSC), which aims to extract the representative characteristics of respiration with fewer computational resources. Furthermore, a sleeping database, containing more than $50~hours$ of data, was built to investigate the performance of benchmarked ROI methods during long-term sleeping. The experimental results demonstrate that TSC attains the highest signal-to-noise ratio (SNR) at $20.6~dB$ and a relatively low elapsed time of 100.2 frames per second (fps). For the RR estimation accuracy, TSC reduces the mean absolute error from 1.65 breaths per minute ( $bpm$ ) to $0.97~bpm$ .

Dynamic Downscaling Segmentation for Noisy, Low-Contrast in Situ Underwater Plankton Images

Finding and segmenting objects in noisy low-contrast in situ underwater plankton images is challenging because of the difficulty of separating potential plankton objects from the complex background and numerous and diverse other particles. In the present study, a dynamic downscaling model was developed to rapidly extract complete and clean regions of interest (ROIs) from images with highly variable content and quality. The original image was downscaled, and dynamic segmentation was performed in a scale pyramid space to ensure the integrity of weak targets based on local two-dimensional (2D) entropy parameters. Subsequently, a series of local thresholds and clustering gradients was examined iteratively for ROI selection. The performance of the local 2D entropy parameters relative to water turbidity (in a scattering medium and with high background noise) and image size was examined. To suppress the background and increase the sharpness of potential targets, a sharpness descriptor and gradient clustering were employed. The method was compared with the currently commonly used local threshold-based Sauvola segmentation using the same set of images. The results showed that the proposed method improves the ROI extraction accuracy and reduces oversegmentation for in situ underwater plankton images. It was concluded that the proposed method is a fast and robust segmentation technique and could facilitate the deployment of in situ plankton imaging systems for process-based research and routine plankton monitoring.

ROI Selection Criteria for Finding the Abnormal Tissues from Breast Magnetic Resonance Imaging

The imaging methods in breast diagnostics play a preeminent role in the early detection and finding out the exact location &amp; area of the suspicious breast tissues for malignancy. The further treatment significantly depends on the tumour-tobreast size relationship. The tumor size considered as the most influential factors for pathological/clinical assessment of breast cancer. In general, localization of the tumor’s location and also the selection of a region of interest (ROI) were performed manually by an experienced radiologist. The objective of this paper is to propose an effective criterion for selection of ROI for abnormal tissues detection from breast MRI. This paper implements an efficacious ROI selection criterion for finding the exact location &amp; area of the breast abnormal tissues from magnetic resonance imaging automatically. The proposed algorithm integrates the simple techniques like filtering, edge detection and morphological operations for inner segmentation. Outer breast region segmentation is performed by selecting the peak and valley points and then connects the selected points by applying fit to circle function which makes the MR image rotation invariant. The method is implemented on the 80 images contained in S1 dataset i.e. multi-parametric breast MRI dataset and the evaluation is done through comparative analysis of predicted image with manually segmented images. The experimental results in terms of evaluation matrices i.e. Precision, Recall and Score depict the efficacy of the proposed work.