scholarly journals Automatic Extraction of Two Regions of Creases from Palmprint Images for Biometric Identification

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Roszaharah Yaacob ◽  
Chok Dong Ooi ◽  
Haidi Ibrahim ◽  
Nik Fakhuruddin Nik Hassan ◽  
Puwira Jaya Othman ◽  
...  
Keyword(s):  
Region Of Interest ◽  
Salient Feature ◽  
Personal Identification ◽  
Experimental Results ◽  
Biometric Identification ◽  
Automatic Extraction ◽  
Morphological Operations ◽  
Average Sensitivity ◽  
Speed Up ◽  
Sensitivity Specificity

Palmprint has become one of the biometric modalities that can be used for personal identification. This modality contains critical identification features such as minutiae, ridges, wrinkles, and creases. In this research, feature from creases will be our focus. Feature from creases is a special salient feature of palmprint. It is worth noting that currently, the creases-based identification is still not common. In this research, we proposed a method to extract crease features from two regions. The first region of interest (ROI) is in the hypothenar region, whereas another ROI is in the interdigital region. To speed up the extraction, most of the processes involved are based on the processing of the image that has been a downsampled image by using a factor of 10. The method involved segmentations through thresholding, morphological operations, and the usage of the Hough line transform. Based on 101 palmprint input images, experimental results show that the proposed method successfully extracts the ROIs from both regions. The method has achieved an average sensitivity, specificity, and accuracy of 0.8159, 0.9975, and 0.9951, respectively.

scholarly journals Automatic Detection and Extraction of Lungs Cancer Nodules Using Connected Components Labeling and Distance Measure Based Classification

2021 ◽  
Author(s):  
Mamdouh Monif ◽  
Kinan Mansour ◽  
Waad Ammar ◽  
Maan Ammar
Keyword(s):  
Distance Measure ◽  
Automatic Detection ◽  
Experimental Results ◽  
Connected Components ◽  
Automatic Extraction ◽  
Morphological Operations ◽  
Consistency Check ◽  
Efficient Detection ◽  
Lung Area ◽  
Healthy Persons

We introduce in this paper a method for reliable automatic extraction of lung area from CT chest images with a wide variety of lungs image shapes by using Connected Components Labeling (CCL) technique with some morphological operations. The paper introduces also a method using the CCL technique with distance measure based classification for the efficient detection of lungs nodules from extracted lung area. We further tested our complete detection and extraction approach using a performance consistency check by applying it to lungs CT images of healthy persons (contain no nodules). The experimental results have shown that the performance of the method in all stages is high.

Personal Identification Using Finger-Knuckle-Print Based on Local Binary Pattern

2013 ◽  
Vol 441 ◽  
pp. 703-706 ◽  
Author(s):  
Peng Fei Yu ◽  
Hao Zhou ◽  
Hai Yan Li
Keyword(s):  
Local Binary Pattern ◽  
Feature Vector ◽  
Region Of Interest ◽  
Local Features ◽  
Personal Identification ◽  
Experimental Results ◽  
Considerable Progress ◽  
Similarity Measurement ◽  
Biometric Recognition ◽  
Histogram Intersection

Over the last ten years, considerable progress has been made on the new hand-based biometric recognition, such as palmprint and hand vein. During this period, it has been proved that Finger-Knuckle-Print (FKP) can be used as a biometric identifier. In this paper, we present an effective FKP identification method based on Local Binary Pattern (LBP), whose idea is to divide the region of interest (ROI) of FKP into a set of sub-image blocks, which can be applied to extract the local features of the FKP. After that, LBP histograms of image blocks in a FKP ROI image are connected together to build the feature vector of the FKP ROI image. In the match stage, histogram intersection distance is applied as the similarity measurement between sample and template. Experimental results conducted on a database of 165 persons (4 fingers per person) show that the proposed method is effective.

scholarly journals Thermochemical Heat Storage in a Lab-Scale Indirectly Operated CaO/Ca(OH)2 Reactor—Numerical Modeling and Model Validation through Inverse Parameter Estimation

2021 ◽  
Vol 11 (2) ◽  
pp. 682
Author(s):  
Gabriele Seitz ◽  
Farid Mohammadi ◽  
Holger Class
Keyword(s):  
Numerical Model ◽  
Gas Flow ◽  
Reaction Rate ◽  
Heat Storage ◽  
Bulk Material ◽  
Fixed Bed ◽  
Experimental Results ◽  
Fixed Bed Reactor ◽  
Thermochemical Heat Storage ◽  
Speed Up

Calcium oxide/Calcium hydroxide can be utilized as a reaction system for thermochemical heat storage. It features a high storage capacity, is cheap, and does not involve major environmental concerns. Operationally, different fixed-bed reactor concepts can be distinguished; direct reactor are characterized by gas flow through the reactive bulk material, while in indirect reactors, the heat-carrying gas flow is separated from the bulk material. This study puts a focus on the indirectly operated fixed-bed reactor setup. The fluxes of the reaction fluid and the heat-carrying flow are decoupled in order to overcome limitations due to heat conduction in the reactive bulk material. The fixed bed represents a porous medium where Darcy-type flow conditions can be assumed. Here, a numerical model for such a reactor concept is presented, which has been implemented in the software DuMux. An attempt to calibrate and validate it with experimental results from the literature is discussed in detail. This allows for the identification of a deficient insulation of the experimental setup. Accordingly, heat-loss mechanisms are included in the model. However, it can be shown that heat losses alone are not sufficient to explain the experimental results. It is evident that another effect plays a role here. Using Bayesian inference, this effect is identified as the reaction rate decreasing with progressing conversion of reactive material. The calibrated model reveals that more heat is lost over the reactor surface than transported in the heat transfer channel, which causes a considerable speed-up of the discharge reaction. An observed deceleration of the reaction rate at progressed conversion is attributed to the presence of agglomerates of the bulk material in the fixed bed. This retardation is represented phenomenologically by mofifying the reaction kinetics. After the calibration, the model is validated with a second set of experimental results. To speed up the calculations for the calibration, the numerical model is replaced by a surrogate model based on Polynomial Chaos Expansion and Principal Component Analysis.

GUI-CAD Tool for Segmentation and Classification of Abnormalities in Lung CT Image

2019 ◽  
Vol 8 (1) ◽  
pp. 9-31
Author(s):  
V. Vijaya Kishore ◽  
R.V.S. Satyanarayana
Keyword(s):  
Region Of Interest ◽  
Performance Measure ◽  
Morphological Operations ◽  
Interactive 3D ◽  
And Performance ◽  
Aided Diagnosis ◽  
Lung Ct ◽  
Clinical Expert ◽  
Clinical Determination

A vital necessity for clinical determination and treatment is an opportunity to prepare a procedure that is universally adaptable. Computer aided diagnosis (CAD) of various medical conditions has seen a tremendous growth in recent years. The frameworks combined with expanding capacity, the coliseum of CAD is touching new spaces. The goal of proposed work is to build an easy to understand multifunctional GUI Device for CAD that performs intelligent preparing of lung CT images. Functions implemented are to achieve region of interest (ROI) segmentation for nodule detection. The nodule extraction from ROI is implemented by morphological operations, reducing the complexity and making the system suitable for real-time applications. In addition, an interactive 3D viewer and performance measure tool that quantifies and measures the nodules is integrated. The results are validated through clinical expert. This serves as a foundation to determine, the decision of treatment and the prospect of recovery.

scholarly journals The vibro-impact capsule system in millimetre scale: numerical optimisation and experimental verification

Meccanica ◽  
2020 ◽  
Vol 55 (10) ◽  
pp. 1885-1902
Author(s):  
Yang Liu ◽  
Joseph Páez Chávez ◽  
Jiajia Zhang ◽  
Jiyuan Tian ◽  
Bingyong Guo ◽  
...  
Keyword(s):  
Dynamical System ◽  
Mathematical Formulation ◽  
Path Following ◽  
Piecewise Smooth ◽  
Experimental Results ◽  
Scale Down ◽  
Speed Up ◽  
Small Bowel Endoscopy ◽  
Numerical Optimisation ◽  
Smooth Dynamical System

Abstract The vibro-impact capsule system has been studied extensively in the past decade because of its research challenges as a piecewise-smooth dynamical system and broad applications in engineering and healthcare technologies. This paper reports our team’s first attempt to scale down the prototype of the vibro-impact capsule to millimetre size, which is 26 mm in length and 11 mm in diameter, aiming for small-bowel endoscopy. Firstly, an existing mathematical model of the prototype and its mathematical formulation as a piecewise-smooth dynamical system are reviewed in order to carry out numerical optimisation for the prototype by means of path-following techniques. Our numerical analysis shows that the prototype can achieve a high progression speed up to 14.4 mm/s while avoiding the collision between the inner mass and the capsule which could lead to less propulsive force on the capsule so causing less discomfort on the patient. Secondly, the experimental rig and procedure for testing the prototype are introduced, and some preliminary experimental results are presented. Finally, experimental results are compared with the numerical results to validate the optimisation as well as the feasibility of the vibro-impact technique for the potential of a controllable endoscopic procedure.

Pedestrian Detection Based on Histograms of Oriented Gradients in ROI

2012 ◽  
Vol 542-543 ◽  
pp. 937-940
Author(s):  
Ping Shu Ge ◽  
Guo Kai Xu ◽  
Xiu Chun Zhao ◽  
Peng Song ◽  
Lie Guo
Keyword(s):  
Traditional Method ◽  
Detection Method ◽  
Feature Vector ◽  
Pedestrian Detection ◽  
Region Of Interest ◽  
Experimental Results ◽  
Process Time ◽  
Vertical Edge ◽  
Histograms Of Oriented Gradients

To locate pedestrian faster and more accurately, a pedestrian detection method based on histograms of oriented gradients (HOG) in region of interest (ROI) is introduced. The features are extracted in the ROI where the pedestrian's legs may exist, which is helpful to decrease the dimension of feature vector and simplify the calculation. Then the vertical edge symmetry of pedestrian's legs is fused to confirm the detection. Experimental results indicate that this method can achieve an ideal accuracy with lower process time compared to traditional method.

ALGORITHMS FOR FINGERPRINT CLASSIFICATION

2021 ◽  
Vol 335 (1) ◽  
pp. 39-44
Author(s):  
D. Lebedev ◽  
A. Abzhalilova
Keyword(s):  
Neural Network ◽  
Gabor Filter ◽  
Recognition System ◽  
Personal Identification ◽  
External Object ◽  
Biometric Identification ◽  
Fingerprint Classification ◽  
Combined Application ◽  
Traditional Approaches

Currently, biometric methods of personality are becoming more and more relevant recognition technology. The advantage of biometric identification systems, in comparison with traditional approaches, lies in the fact that not an external object belonging to a person is identified, but the person himself. The most widespread technology of personal identification by fingerprints, which is based on the uniqueness for each person of the pattern of papillary patterns. In recent years, many algorithms and models have appeared to improve the accuracy of the recognition system. The modern algorithms (methods) for the classification of fingerprints are analyzed. Algorithms for the classification of fingerprint images by the types of fingerprints based on the Gabor filter, wavelet - Haar, Daubechies transforms and multilayer neural network are proposed. Numerical and results of the proposed experiments of algorithms are carried out. It is shown that the use of an algorithm based on the combined application of the Gabor filter, a five-level wavelet-Daubechies transform and a multilayer neural network makes it possible to effectively classify fingerprints.

scholarly journals UNRAVELLING DIABETIC RETINOPATHY THROUGH IMAGE PROCESSING, NEURAL NETWORKS AND FUZZY LOGIC – A REVIEW

2017 ◽  
Vol 10 (4) ◽  
pp. 32
Author(s):  
Srinivasan A ◽  
Sudha S
Keyword(s):  
Image Processing ◽  
Fuzzy Logic ◽  
Diabetic Retinopathy ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Computation Time ◽  
Automated System ◽  
Morphological Operations ◽  
Advanced Stage ◽  
Sensitivity Specificity

One of the main causes of blindness is diabetic retinopathy (DR) and it may affect people of any ages. In these days, both young and old ages are affected by diabetes, and the di abetes is the main cause of DR. Hence, it is necessary to have an automated system with good accuracy and less computation time to diagnose and treat DR, and the automated system can simplify the work of ophthalmologists. The objective is to present an overview of various works recently in detecting and segmenting the various lesions of DR. Papers were categorized based on the diagnosing tools and the methods used for detecting early and advanced stage lesions. The early lesions of DR are microaneurysms, hemorrhages, exudates, and cotton wool spots and in the advanced stage, new and fragile blood vessels can be grown. Results have been evaluated in terms of sensitivity, specificity, accuracy and receiver operating characteristic curve. This paper analyzed the various steps and different algorithms used recently for the detection and classification of DR lesions. A comparison of performances has been made in terms of sensitivity, specificity, area under the curve, and accuracy. Suggestions, future workand the area to be improved were also discussed.Keywords: Diabetic retinopathy, Image processing, Morphological operations, Neural network, Fuzzy logic. 

Incorporating diffusion-weighted imaging in a diagnostic algorithm for multiparametric MR mammography

2021 ◽  
pp. 028418512110418
Author(s):  
Katerina Vassiou ◽  
Michael Fanariotis ◽  
Ioannis Tsougos ◽  
Ioannis Fezoulidis
Keyword(s):  
Sensitivity And Specificity ◽  
Predictive Value ◽  
Region Of Interest ◽  
Diagnostic Algorithm ◽  
Breast Lesions ◽  
Adc Value ◽  
Diffusion Weighted ◽  
Adc Values ◽  
Single Formula ◽  
Sensitivity Specificity

Background Apparent diffusion coefficient (ADC) measurements are not incorporated in BI-RADS classification. Purpose To assess the probability of malignancy of breast lesions at magnetic resonance mammography (MRM) at 3 T, by combining ADC measurements with the BI-RADS score, in order to improve the specificity of MRM. Material and Methods A total of 296 biopsy-proven breast lesions were included in this prospective study. MRM was performed at 3 T, using a standard protocol with dynamic sequence (DCE-MRI) and an extra echo-planar diffusion-weighted sequence. A freehand region of interest was drawn inside the lesion, and ADC values were calculated. Each lesion was categorized according to the BI-RADS classification. Logistic regression analysis was employed to predict the probability of malignancy of a lesion. The model combined the BI-RADS classification and the ADC value. Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy were calculated. Results In total, 153 malignant and 143 benign lesions were analyzed; 257 lesions were masses and 39 lesions were non-mass-like enhancements. The sensitivity and specificity of the combined method were 96% and 86%, respectively, in contrast to 95% and 81% with BI-RADS classification alone. Conclusion We propose a method of assessing the probability of malignancy in breast lesions by combining BI-RADS score and ADC values into a single formula, increasing sensitivity and specificity compared to BI-RADS classification alone.

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