Regions Of Interest In Images Research Articles

An approach to improve Palmprint Recognition Accuracy by Using Different Region of Interest Methods with Local Binary Pattern Techniques

Objective: To extract the Region of Interest (ROI) of palmprint image by using appropriate methods and to improve the accuracy of palmprint recognition system. Methods/Statistical Analysis: This piece of work is primarily addressing the different mechanisms for extracting ROI area. The techniques like Competitive Hand Valley Detection (CHVD), and Euclidean Distance (ED) were applied as the part of pre-processing, while the Feature Extraction mechanism LBP was utilized to extract the texture feature from different type of ROIs of palmprint image. Findings: The experimental results showed that CHVD with LBP gave best result with high accuracy reached to 96.10534% and Equal Error Rate (EER) of 3.894661%, while in ED the best result showed accuracy reached to 88.23611% and EER of 11.76389%. Application/Improvements: The study mainly concentrated on developing palmprint authentication system with less EER and high accuracy. Keywords: Analysis, Competitive Hand Valley Detection (CHVD), Euclidean Distance, Local Binary Pattern (LBP), Palmprint Recognition

An approach to improve Palmprint Recognition Accuracy by Using Different Region of Interest Methods with Local Binary Pattern Techniques

Objective: To extract the Region of Interest (ROI) of palmprint image by using appropriate methods and to improve the accuracy of palmprint recognition system. Methods/Statistical Analysis: This piece of work is primarily addressing the different mechanisms for extracting ROI area. The techniques like Competitive Hand Valley Detection (CHVD), and Euclidean Distance (ED) were applied as the part of pre-processing, while the Feature Extraction mechanism LBP was utilized to extract the texture feature from different type of ROIs of palmprint image. Findings: The experimental results showed that CHVD with LBP gave best result with high accuracy reached to 96.10534% and Equal Error Rate (EER) of 3.894661%, while in ED the best result showed accuracy reached to 88.23611% and EER of 11.76389%. Application/Improvements: The study mainly concentrated on developing palmprint authentication system with less EER and high accuracy.

ROI Based Post Image Quality Assessment Technique on Multiple Localized Filtering Method On Kinect HD Sensor Mobile Robot

The ROI (Region of Interest) Image Quality Assessment is an image quality assessment model based on the SSI (Structural Similarity Index) index used in the specific image region desired to be assessed. Output assessmen value used by this image assessment model is 1 which means identical and -1 which means not identical. Assessment model of ROI Quality Assessment in this research is used to measure image quality on Kinect sensor capture result used in Mobile HD Robot after applied Multiple Localized Filtering Technique. The filter is applied to each capture sensor depth result on Kinect, with the aim to eliminate structural noise that occurs in the Kinect sensor. Assessment is done by comparing image quality before filter and after filter applied to certain region. The kinect sensor will be conditioned to capture a square black object measuring 10cm x 10cm perpendicular to a homogeneous background (white with RGB code 255,255,255). The results of kinect sensor data will be taken through EWRF 3022 by visual basic 6.0 program periodically 10 times each session with frequency 1 time per minute. The results of this trial show the same similar index (value 1: identical) in the luminance, contrast, and structural section of the edge region or edge region of the specimen. The value indicates that the Multiple Localized Filtering Technique applied to the noise generated by the Kinect sensor, based on the ROI Image Quality Assessment model has no effect on the image quality generated by the sensor.

Image Appearance-Based Facial Expression Recognition

We investigate facial expression recognition (FER) based on image appearance. FER is performed using state-of-the-art classification approaches. Different approaches to preprocess face images are investigated. First, region-of-interest (ROI) images are obtained by extracting the facial ROI from raw images. FER of ROI images is used as the benchmark and compared with the FER of difference images. Difference images are obtained by computing the difference between the ROI images of neutral and peak facial expressions. FER is also evaluated for images which are obtained by applying the Local binary pattern (LBP) operator to ROI images. Further, we investigate different contrast enhancement operators to preprocess images, namely, histogram equalization (HE) approach and a brightness preserving approach for histogram equalization. The classification experiments are performed for a convolutional neural network (CNN) and a pre-trained deep learning model. All experiments are performed on three public face databases, namely, Cohn–Kanade (CK[Formula: see text]), JAFFE and FACES.

Stability Analysis of CT Radiomics Features With Respect to the Variation of Manual Segmentation in Oropharyngeal Cancer

Accurate segmentation of tumors and quantification of tumor features are important for cancer detection, diagnosis, monitoring, and planning therapeutic intervention. Due to inherent noise components in multiparametric imaging and inter-observer variations, it is common that various segmentation methods, including both manual segmentation and computer-aided segmentation methods, may produce large segmentation errors in tumor volumes and their associated radiomics features. Such errors may eventually lead to large prediction errors. The aim of this study is to carry out the stability analysis for various radiomics features with respect to segmentation results based on the contrast-enhanced CT axial images. All the 436 CT images were imported to a commercial software and the gross tumor volumes of primary disease (GTVp) were segmented by two expert radiation oncologists. In order to illustrate the variations of segmentation results, additional two segmentation results were set up via resizing the original segmented regions of interest (ROIs) based on their geometric information on the surface. For the three ROI image groups, we calculated 109 radiomics features, and wavelet-based features. Then, a logistic regression model was built to investigate the correlation between the radiomics features extracted from GTVp and the response to chemoradiation in terms of overall survival (OS). Finally, based on the prediction probabilities, we assessed the inter-rater reliability and reproducibility via calculating the intraclass correlation coefficients (ICC) and concordance correlation coefficients (CCC), respectively. One of the features, 25percentile, outperforms other features in terms of both ICC and CCC. Its ICC and CCC between the original segmentation group and resized segmentation groups are around 0.27. In terms of the prediction accuracy, top features with high AUC extracted from different segmentation results are different from each other. Another interesting finding is that gray-level co-occurrence matrix (GLCM) based features have low ICC and CCC (<0.3) in comparison between original segmentation group and each resized segmentation group, while have high ICC and CCC (>0.9) in comparison between two resized segmentation groups. Considering the prediction performance, these GLCM based features derived from original segmentation group perform well with the area under the curve (AUC) above 0.7, which means these traditional features are sensitive to segmentation results. The segmentation variability affects both the radiomics features and prediction accuracy. Moreover, it is of great meaning to discover radiomics features with robustness of segmentation variability in oropharyngeal cancer, which can be warranted for treatment monitoring and prognosis prediction.

Computer aided thyroid nodule detection system using medical ultrasound images

Abstract Thyroid nodule is one of the endocrine problem caused due to abnormal growth of cells. This survival rate can be enhanced by earlier detection of nodules. Thus, the accurate detection of nodule is of utmost importance in providing effective diagnosis to increase the survival rate. However, accuracy of nodule detection from ultrasound images is suffered due to speckle noise. It considerably deteriorates the image quality and makes the differentiation of fine details quite difficult. Most of the detection systems for the thyroid nodules are semi-automated entailing manual intervention to draw rough outline of the nodule at some level or require manual segmentation in training or testing phases that increase the inaccuracies and evaluation time. To handle this, a fully Computer-Aided Detection system is presented for speckle reduction and segmentation of nodules from thyroid ultrasound images. The proposed system has three components: speckle reduction to reduce speckle noise and preserve the diagnostic features of ultrasound image, automatic generation of Region of interest (ROI) that identifies suspicious regions and fully automatic segmentation of nodule in processed ROI image. The proposed segmentation method outperformed other methods by gaining high True Positive (TP) value (95.92 ± 3.70%), False Positive (FP) value (7.04 ± 4.21%), Dice Coefficient (DC) value (93.88 ± 2.59%), Overlap Metric (OM) (91.18 ± 7.04 pixels) and Hausdroff Distance (HD) (0.52 ± 0.20 pixels). This system can facilitate the endocrinologists by providing second opinion to improve diagnosis of nodules as benign or malignant.

Detection of Schizophrenia in brain MR images based on segmented ventricle region and deep belief networks

Schizophrenia (SZ) is a brain disorder that affects manifold cognitive domains which include language, memory, attention and executive functions. Magnetic resonance imaging is used to capture structural abnormalities in brain regions. Many studies have indicated brain region volume changes in Schizophrenia patients. In this work, an attempt has been made to analyze the schizophrenic subjects based on ventricle region using deep belief networks (DBNs). The effectiveness of the proposed method is evaluated on center of biomedical research excellence database. Initially, the ventricle region from the normal and SZ images is segmented using multiplicative intrinsic component optimization method. The DBN using different learning algorithms such as stochastic gradient descent (SGD), adaptive gradient (Adagrad) and root-mean-square propagation (RMSProp) is used to train the considered region. The effect of number of layers and the learning algorithm used to discriminate the normal and SZ subjects in DBN is analyzed. Then, the DBN model is evaluated on test set using accuracy, precision, sensitivity and specificity measures. The classification performance of the proposed system is analyzed using receiver operating characteristic curve. Further, the performance of DBN based on segmented ventricle is compared with region of interest (ROI) image that consists of ventricle along with other tissues. Here threefold validations are carried out for the same set of images. Results show that DBN with RMSProp learning with two hidden layers gives better performance compared to other learning methods such as SGD and Adagrad. In addition, DBN on segmented ventricle region gives least error compared to ROI image. DBN with segmented ventricle provides better classification accuracy of 90%. The proposed method achieves high area under the curve (0.899) for the segmented ventricle image, which clearly demonstrates its effectiveness. Thus, the DBN with RMSProp learning-based classification of segmented ventricle could be used as a supplement in the investigation of Schizophrenia.

Induction Motor Centrifugal Blower Health Diagnostic Based on Color Segmentation of Thermal Image and Vibration Signal Feature

The rotating machinery requires condition monitoring which its measurement without being intrusive operation, especially on the equipment needed to continue running. One such machinery is a centrifugal blower induction motor. Infrared thermography and vibration are important and effective technologies to diagnose of health condition it without destructive or disturb of operations. The diagnostics of induction motor are based on the analysis results data onto vibration and processing thermal image. This paper focused on thermography image processing based on color segmentation which it will produce ROI (region of interest) images. The ROI image is extracted based on HSV color and shape feature. Feature extraction is intended to determine value of mean, standard deviation, kurtosis, skewness and entropy HSV and shape features (area, perimeter, metric, and eccentricity). The highest RMS (root mean square) vibration data is used as reference to classify data into normal and abnormal. Parameters that can be used to classify normal and abnormal conditions based on data analysis are standard deviation Hue, kurtosis HS, skewness HSV, entropy HSV and metric.

AUTOMATIC IMAGE SEGMENTATION METHOD FOR BREAST CANCER ANALYSIS USING THERMOGRAPHY

Breast Cancer (BC) is considered one from various diseases that has got great attention in the last decades. This is because the high mortality rates among young women in the whole world according to this disease. The early detection of this disease is necessary to enhance the opportunity of survival. Thermography is an efficient screening tool that can help in detecting BC by indicating parts of the body where an abnormal temperature variation is found. To realize an effective BC detection system using thermography, it is necessary to locate the region of interest (ROI) in the thermograms prior to analysis. This paper introduces a new automatic segmentation method (SM) for identifying the ROI image from breast thermograms. It depends on the statistics of image in DMR-IR database. After detecting the boundaries of ROI using this new SM, two different approaches for accurate identifying and enhancing the breast boundaries are investigated: the Hough transform (HT) algorithm to locate and define the parabola curves in the breast image, and a second approach introducing the enhancement of the contrast of detected boundaries in the breast ROI image. To evaluate the results of this method, statistical features have been extracted from the segmented ROI image. Then, we used the Support Vector Machine (SVM) and the Artificial Neural Network (ANN) classifiers to detect the normal and abnormal breasts based on these features. The experimental results prove that the accuracy of SVM, and ANN classifiers reached to 96.67% and 96.07%, respectively. This denotes that the proposed automatic SM is a favorable technique for extracting the breast ROI image from breast thermograms.

Toward a service-based workflow for automated information extraction from herbarium specimens.

Over the past years, herbarium collections worldwide have started to digitize millions of specimens on an industrial scale. Although the imaging costs are steadily falling, capturing the accompanying label information is still predominantly done manually and develops into the principal cost factor. In order to streamline the process of capturing herbarium specimen metadata, we specified a formal extensible workflow integrating a wide range of automated specimen image analysis services. We implemented the workflow on the basis of OpenRefine together with a plugin for handling service calls and responses. The evolving system presently covers the generation of optical character recognition (OCR) from specimen images, the identification of regions of interest in images and the extraction of meaningful information items from OCR. These implementations were developed as part of the Deutsche Forschungsgemeinschaft-funded a standardised and optimised process for data acquisition from digital images of herbarium specimens (StanDAP-Herb) Project.

Saliency detection and region of interest extraction based on multi-image common saliency analysis in satellite images

Abstract Saliency analysis is an effective method to extract interesting target regions from satellite images. However, when the satellite image contains salient background information or interference, it is difficult to eliminate this information and interference accurately only using single-image saliency analysis. In this paper, a novel multi-image saliency analysis (MSA) model based on multiple multispectral-images clustering saliency analysis (MMCS) and panchromatic image co-occurrence histogram saliency analysis (PCHS) is proposed to extract regions of interest (ROIs) with the common salient features. In MMCS, bisecting K-means clustering on the entire image set extracts the global correspondence in multispectral images. Then, cluster-based saliency using spectral contrast efficiently assigns the common ROIs with high saliency. Therefore, saliency maps of MMCS are computed, which effectively depress the background information and interference. In PCHS, saliency maps are obtained by computing co-occurrence histogram of panchromatic images, which aim to enhance the saliency of ROIs. After that, multi-image saliency maps are computed by a novel fusion strategy, which can depress the background information and highlight the ROIs. Finally, ROIs of multiple images are acquired by binarizing these saliency maps. Experimental results show that the MSA model can effectively eliminate the salient background information and exclude the images that contain no common salient information, thus achieving the highest ROC and PRF value.

A Bayesian framework for performance assessment and comparison of imaging biomarker quantification methods.

Quantitative biomarkers derived from medical images are being used increasingly to help diagnose disease, guide treatment, and predict clinical outcomes. Measurement of quantitative imaging biomarkers is subject to bias and variability from multiple sources, including the scanner technologies that produce images, the approaches for identifying regions of interest in images, and the algorithms that calculate biomarkers from regions. Moreover, these sources may differ within and between the quantification methods employed by institutions, thus making it difficult to develop and implement multi-institutional standards. We present a Bayesian framework for assessing bias and variability in imaging biomarkers derived from different quantification methods, comparing agreement to a reference standard, studying prognostic performance, and estimating sample size for future clinical studies. The statistical methods are illustrated with data obtained from a positron emission tomography challenge conducted by members of the NCI's Quantitative Imaging Network program, in which tumor volumes were measured manually and with seven different semi-automated segmentation algorithms. Estimates and comparisons of bias and variability in the resulting measurements are provided along with an R software package for the technical performance analysis and an online web application for sample size and power analysis.

Infrared and Visible Image Fusion based on Saliency Detection and Infrared Target Segment

Infrared and visible image fusion is significant to the overall detection and tracking performance of a video surveillance system. Considering the characteristics of infrared and visible images, the ideal fusion of the infrared and visual image should integrate the bright features of the infrared image, while preserving a considerable amount of background information of the visible image. However, current methods use inadequate information extraction. An efficient infrared and visible image fusion method is thus proposed in this paper based on saliency detection and infrared target segmentation. Firstly, an image descriptor, referred to as an image signature, is introduced. The saliency algorithm is implemented to obtain the infrared image region of interest. Secondly, an infrared target is segmented from the region of interest by adopting a thresholding method. Subsequently, the infrared target is added to the image, which is fused from the original infrared and visible image by known image fusion methods. The most remarkable advantage of the proposed method is that it can obtain a clear infrared target. To verify the proposed method, it was experimentally compared with several classic fusion algorithms. By subjectively comparing the fused results, it was demonstrated that the proposed method not only extracted the important infrared targets, but it also showed good visual quality retained in the background information.

Satellite Image Compression Based On SPIHT Algorithm

In the domains that deal with a large scale image applications force the use of image compression in order to reduce the required storage. Due to the constrained bandwidth and storage capacity in the field of marine a new compression technique for the compression of the satellite images is proposed here. In our system, we are separating the region of Region of Interest (ROI) part from the original image. And this ROI images can be used for compression scheme by an algorithm known as the Set Partitioning in Hierarchical Trees (SPIHT) algorithm. The SPIHT algorithm is a part of the Lossless compression algorithm. The performance of our method can be done by calculating the PSNR values of the output images.

Satellite Image Compression Based On SPIHT Algorithm

In the domains that deal with a large scale image applications force the use of image compression in order to reduce the required storage. Due to the constrained bandwidth and storage capacity in the field of marine a new compression technique for the compression of the satellite images is proposed here. In our system, we are separating the region of Region of Interest (ROI) part from the original image. And this ROI images can be used for compression scheme by an algorithm known as the Set Partitioning in Hierarchical Trees (SPIHT) algorithm. The SPIHT algorithm is a part of the Lossless compression algorithm. The performance of our method can be done by calculating the PSNR values of the output images.

Interior construction state recognition with 4D BIM registered image sequences

Abstract Deviations from planned schedules in construction projects frequently lead to unexpected financial disadvantages. However, early assessment of delays or accelerations during the phase of construction enables the adjustment of subsequent and dependent tasks. Manually performed, this involves many human resources if as-built information is not immediately available. This is particularly valid for indoor environments, where a general overview of tasks is not given. In this paper, we present a novel method that increases the degree of automation for indoor progress monitoring. The novel method recognizes the actual state of construction activities from as-built video data based on as-planned BIM data using computer vision algorithms. To achieve that, two main steps are incorporated. The first step registers the images with the underlying 4D BIM model. This means the discovery of the pose of each image of a sequence according to the coordinate system of the building model. Being aware of the image origin, it allows for the advanced interpretation of the content in consecutive processing. In the second step, the relevant tasks of the expected state of the 4D BIM model are projected onto the image space. The resulting image regions of interest are then taken as input for the determination of the activity state. The method is extensively tested in the experiment section of this paper. Since each consecutive process is based on the output of preceding steps, each process of the introduced method is tested for its standalone characteristics. In addition, the general manner of applicability is evaluated by means of two exemplary tasks as a concluding proof of the success of the novel method. All experiments show promising results and direct towards automatic indoor progress monitoring.

Mesh-to-raster region-of-interest-based nonrigid registration of multimodal images.

Region of interest (RoI) alignment in medical images plays a crucial role in diagnostics, procedure planning, treatment, and follow-up. Frequently, a model is represented as triangulated mesh while the patient data is provided from computed axial tomography scanners as pixel or voxel data. Previously, we presented a 2-D method for curve-to-pixel registration. This paper contributes (i)a general mesh-to-raster framework to register RoIs in multimodal images; (ii)a 3-D surface-to-voxel application, and (iii)a comprehensive quantitative evaluation in 2-D using ground truth (GT) provided by the simultaneous truth and performance level estimation (STAPLE) method. The registration is formulated as a minimization problem, where the objective consists of a data term, which involves the signed distance function of the RoI from the reference image and a higher order elastic regularizer for the deformation. The evaluation is based on quantitative light-induced fluoroscopy (QLF) and digital photography (DP) of decalcified teeth. STAPLE is computed on 150 image pairs from 32 subjects, each showing one corresponding tooth in both modalities. The RoI in each image is manually marked by three experts (900 curves in total). In the QLF-DP setting, our approach significantly outperforms the mutual information-based registration algorithm implemented with the Insight Segmentation and Registration Toolkit and Elastix.

Image compression algorithm of floral canopy based on mask hybrid coding for ROI

To achieve high-quality image compression of a floral canopy, a region of interest (ROI) mask of the wavelet domain was generated through the automatic identification of the canopy ROI and lifting the bit-plane of the ROI to obtain priority of coding for the ROI-set partitioning in hierarchical trees (ROI-SPIHT) coding. The embedded zerotree wavelet (EZW) coding was conducted for the background (BG) region of the image and a relatively more low-frequency wavelet coefficient was obtained using a relatively small amount of coding. Through the weighing factor r of the ROI coding amount, the proportion of the ROI and BG coding amount was dynamically adjusted to generate embedded, truncatable bit streams. Despite the location of truncation, the image information and ROI mask information required by the decoder can be guaranteed to achieve high-quality compression and reconstruction of the image ROI. The results indicated that under the same bit rate, the larger the r value is, the larger the peak-signal-to-noise ratio (PSNR) for the ROI reconstructed image and the smaller the PSNR for the BG reconstructed image. In the range of 0.07-1.09 bpp, the PSNR of the ROI reconstructed image was 42.65% higher on average than that of the BG reconstructed image, 43.95% higher on average than that of the composite image of the ROI and BG (ALL), and 16.84% higher on average than that of the standard SPIHT reconstructed image. Additionally, the mean square error of the quality evaluation index and similarity for the ROI reconstructed image were both better than those for the BG, ALL, and standard SPIHT reconstructed images. The texture distortion of the ALL image was smaller than that of the SPIHT reconstructed image, indicating that the image compression algorithm based on the mask hybrid coding for ROI (ROI-MHC) is capable of improving the reconstruction quality of an ROI image. When the weighing factor r is a fixed value, as the proportion of ROI (a) increases, the quality of ROI image reconstruction gradually decreases. Therefore, upon the application of the ROI-MHC image compression algorithm, high-quality reconstruction of the ROI image can be achieved through dynamically configuring r according to a. Under the same bit rate, the quality of the ROI-MHC image compression is higher than that of current compression algorithms of same classes and offers promising application opportunities. Keywords: floral canopy, ROI, mask hybrid coding, image compression, algorithm, wavelet transform DOI: 10.25165/j.ijabe.20171005.2772 Citation: Sun G X, Wang X C, Ding Y Q, Li Y H, Zhang B H, Li Y B, et al. Image compression algorithm of floral canopy based on mask hybrid coding for ROI. Int J Agric & Biol Eng, 2017; 10(5): 166–176.

Computer aided thyroid nodule detection system using medical ultrasound images

Abstract Thyroid nodule is one of the endocrine problem caused due to abnormal growth of cells. This survival rate can be enhanced by earlier detection of nodules. Thus, the accurate detection of nodule is of utmost importance in providing effective diagnosis to increase the survival rate. However, accuracy of nodule detection from ultrasound images is suffered due to speckle noise. It considerably deteriorates the image quality and makes the differentiation of fine details quite difficult. Most of the detection systems for the thyroid nodules are semi-automated entailing manual intervention to draw rough outline of the nodule at some level or require manual segmentation in training or testing phases that increase the inaccuracies and evaluation time. To handle this, a fully Computer-Aided Detection system is presented for speckle reduction and segmentation of nodules from thyroid ultrasound images. The proposed system has three components: speckle reduction to reduce speckle noise and preserve the diagnostic features of ultrasound image, automatic generation of Region of interest (ROI) that identifies suspicious regions and fully automatic segmentation of nodule in processed ROI image. The proposed segmentation method outperformed other methods by gaining high True Positive (TP) value (95.92 ± 3.70%), False Positive (FP) value (7.04 ± 4.21%), Dice Coefficient (DC) value (93.88 ± 2.59%), Overlap Metric (OM) (91.18 ± 7.04 pixels) and Hausdroff Distance (HD) (0.52 ± 0.20 pixels). This system can facilitate the endocrinologists by providing second opinion to improve diagnosis of nodules as benign or malignant.

Automated framework for accurate segmentation of pressure ulcer images

Ulcer tissue segmentation is of immense importance in helping medical personnel to assess wounds. This paper introduces a new computational framework employing state-of-the-art image processing techniques to segment pressure ulcer tissue structures from color images. The framework integrates a visual appearance model of an observed input image with prior color information from an available database of previously stored color RGB images. The following four processing steps are performed. First, to minimize the execution time and enhance the segmentation accuracy, a region-of-interest (ROI) of the whole ulcer area is automatically identified based on contrast changes. This step exploits synthetic frequencies of pixelwise intensities, which are calculated by using an electric field energy model to describe relations between the pixelwise intensities. Secondly, visual appearance of the observed image is modeled by a linear combination of discrete Gaussians (LCDG) model in order to estimate marginal probability distributions of three main tissue classes for the grayscale ROI image. Next, the pixel-wise probabilities of these classes for the color ROI image are calculated using the available prior information about the RGB colors on manually segmented database images. Initial labeling is obtained based on both the observed and prior probabilities of pixelwise colors. Finally, to preserve continuity, the labels are refined and normalized using the generalized Gauss-Markov random field (GGMRF) model. Experimental validation on 24 clinical images of pressure ulcers, provided by the Centre IGURCO, showed the high segmentation accuracy of 90.4%.