scholarly journals A Novel Method in Predicting Hypertension Using Facial Images

2021 ◽  
Vol 11 (5) ◽  
pp. 2414
Author(s):  
Lin Ang ◽  
Mi Hong Yim ◽  
Jun-Hyeong Do ◽  
Sanghun Lee
Keyword(s):  
Facial Feature ◽  
Analysis Of Covariance ◽  
Facial Shape ◽  
Facial Characteristic ◽  
Normal Individuals ◽  
Nose Shape ◽  
Novel Method ◽  
Selection Operator ◽  
Processing Techniques ◽  
Facial Images

Hypertension has been a crucial public health challenge among adults. This study aimed to develop a novel method for non-contact prediction of hypertension using facial characteristics such as facial features and facial color. The data of 1099 subjects (376 men and 723 women) analyzed in this study were obtained from the Korean Constitutional Multicenter Study of Korean medicine Data Center (KDC) at the Korea Institute of Oriental Medicine (KIOM). Facial images were collected and facial variables were extracted using image processing techniques. Analysis of covariance (ANCOVA) and Least Absolute Shrinkage and Selection Operator (LASSO) were performed to compare and identify the facial characteristic variables between the hypertension group and normal group. We found that the most distinct facial feature differences between hypertension patients and normal individuals were facial shape and nose shape for men in addition to eye shape and nose shape for women. In terms of facial colors, cheek color in men, as well as forehead and nose color in women, were the most distinct facial colors between the hypertension groups and normal individuals. Looking at the AUC value, the prediction power for women is better than men. In conclusion, we managed to explore and identify the facial characteristics variables related to hypertension. This study may provide new evidence in the validity of predicting hypertension using facial characteristics.

HUMAN FACE IMAGE PROCESSING TECHNIQUES

2001 ◽  
Vol 01 (02) ◽  
pp. 197-215 ◽  
Author(s):  
HONG YAN
Keyword(s):  
Image Processing ◽  
Research Work ◽  
Facial Feature ◽  
Face Image ◽  
Computer Algorithms ◽  
Human Face ◽  
Feature Location ◽  
Image Processing Techniques ◽  
Face Image Processing ◽  
Processing Techniques

Human face image processing techniques have many applications, such as in security operations, entertainment, medical imaging and telecommunications. In this paper, we provide an overview of existing computer algorithms for face detection and facial feature location, face recognition, image compression and animation. We also discuss limitations of current methods and research work needed in the future.

scholarly journals Importance and Significance of Knee-Chest Decubitus Position of the Pregnant Women, in the Delineation of Fetal Facial Imaging with Four-Dimensional Color Doppler Ultrasound

2018 ◽  
Vol 10 (1) ◽  
pp. 9-16
Author(s):  
Homagni Sikha Roy ◽  
Chunxia Cheng
Keyword(s):  
Pregnant Women ◽  
Doppler Ultrasound ◽  
Color Doppler ◽  
Facial Feature ◽  
Color Doppler Ultrasound ◽  
Free Movement ◽  
Facial Images ◽  
Fetal Position ◽  
4D Ultrasonography

Objective: To investigate and validate the role of the Knee-chest Decubitus position in fetal facial feature delineation by 4D Ultrasonography. Methods: Pregnant women were randomly divided into two groups: One group underwent knee-chest decubitus position prior to re-examination, and the second group underwent free activities like walking for 5, 15 or 30 minutes followed by a re-examination of the fetus. The acceptability of the fetal facial images following the two above mentioned activities was compared. Results: The Knee-chest Decubitus position was identified to be a more successful procedure for obtaining acceptable images. Additionally, it improved the fetal position and the resulting images were achieved significantly rapidly by this maneuver compared to free movement. Conclusion: The knee-chest decubitus position is simple, easy, safe and fast and thus of great convenience and promising for pregnant women.

scholarly journals Examination of Facial Shape Changes Associated with Cardiovascular Disease Using Geometric Morphometrics

2018 ◽  
Vol 3 (1) ◽  
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Diseases ◽  
Geometric Morphometrics ◽  
Directional Asymmetry ◽  
Facial Morphology ◽  
Geometric Morphometric ◽  
Facial Shape ◽  
Normal Individuals ◽  
The Individual ◽  
Shape Changes

Facial investigations using geometric morphometrics has been used in many studies to affirm that a particular disease can attribute to an individual’s facial morphology. A landmark based geometric morphometric analysis was used in this study to asses if facial shape changes are associated with cardiovascular diseases (CVD) and if facial morphology of the CVD individuals differs from the normal ones. In the Municipality of Cantilan, Surigao del Sur, frontal face images taken from 32 cardiovascular disease patients and 32 normal individuals were examined using forty-one manually positioned landmarks. Result showed that facial morphology of the CVD group differs from non-CVD group. Procrustes ANOVA showed significant values for the individual symmetry and directional asymmetry. The analysis of structure by the Principal Components reveals particular variations and the scatter plot of the residual asymmetry shows distinct differences between CVD and non-CVD. Therefore, cardiovascular diseases contribute to facial shape changes and that development of facial morphology differs between CVD and non-CVD group.

scholarly journals A Method for Detecting and Analyzing Facial Features of People with Drug Use Disorders

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1562
Author(s):  
Yongjie Li ◽  
Xiangyu Yan ◽  
Bo Zhang ◽  
Zekun Wang ◽  
Hexuan Su ◽  
...  
Keyword(s):  
Drug Use ◽  
High Performance ◽  
Large Scale ◽  
Facial Features ◽  
Clinical Workflow ◽  
Performance Accuracy ◽  
Drug Use Disorders ◽  
Large Scale Dataset ◽  
Novel Method ◽  
Facial Images

Drug use disorders caused by illicit drug use are significant contributors to the global burden of disease, and it is vital to conduct early detection of people with drug use disorders (PDUD). However, the primary care clinics and emergency departments lack simple and effective tools for screening PDUD. This study proposes a novel method to detect PDUD using facial images. Various experiments are designed to obtain the convolutional neural network (CNN) model by transfer learning based on a large-scale dataset (9870 images from PDUD and 19,567 images from GP (the general population)). Our results show that the model achieved 84.68%, 87.93%, and 83.01% in accuracy, sensitivity, and specificity in the dataset, respectively. To verify its effectiveness, the model is evaluated on external datasets based on real scenarios, and we found it still achieved high performance (accuracy > 83.69%, specificity > 90.10%, sensitivity > 80.00%). Our results also show differences between PDUD and GP in different facial areas. Compared with GP, the facial features of PDUD were mainly concentrated in the left cheek, right cheek, and nose areas (p < 0.001), which also reveals the potential relationship between mechanisms of drugs action and changes in facial tissues. This is the first study to apply the CNN model to screen PDUD in clinical practice and is also the first attempt to quantitatively analyze the facial features of PDUD. This model could be quickly integrated into the existing clinical workflow and medical care to provide capabilities.

scholarly journals Local Directional Threshold based Binary Patterns for Facial Expression Recognition and Analysis

2018 ◽  
Vol 7 (4.6) ◽  
pp. 17
Author(s):  
V Uma Maheswari ◽  
Vara Prasad ◽  
S Viswanadha Raju
Keyword(s):  
Facial Expression ◽  
Dynamic Threshold ◽  
Svm Classifier ◽  
Local Dynamic ◽  
Expression Recognition ◽  
Average Precision ◽  
Large Intensity ◽  
Novel Method ◽  
Texture Extraction ◽  
Facial Images

In this paper, proposing a novel method to retrieve the edge and texture information from facial images named local directional standard matrix (LDSM) and local dynamic threshold based binary pattern (LDTBP). LBP and LTP operators are used for texture extraction of an image by finding difference between center and surrounding pixels but they failed to detect edges and large intensity variations. Thus addressed such problems in proposed method firstly, calculated the LDSM matrix with standard deviation of horizontal and vertical pixels of each pixel. Therefore, values are encoded based on the dynamic threshold which is calculated from median of LDSM values of each pixel called LDTBP. In experiments used LFW facial expression dataset so used SVM classifier to classify the images and retrieved relevant images then measured in terms of average precision and average recall. 

A Novel Method of Extracting Facial Feature Points Based on 3D Rotation and ASM

2011 ◽  
Vol 58-60 ◽  
pp. 1466-1470
Author(s):  
Jie Wu ◽  
Qing He ◽  
Ran Zhou ◽  
Chao Hu ◽  
Q. H. Meng
Keyword(s):  
Face Processing ◽  
Facial Feature ◽  
Face Image ◽  
Human Machine Interface ◽  
Active Shape Model ◽  
Final Position ◽  
Feature Points ◽  
Shape Model ◽  
Novel Method ◽  
Machine Interface

Precise extraction of facial feature points is fundamental to a variety of applications including face processing and human-machine interface. In this paper, a novel method of extracting facial feature points for profile faces is presented. This program is mainly based on a 3D rotation model of head and Active Shape Model (ASM). First we transform a profile face to a corresponding frontal face. Then, we implement the ASM program on the frontal face image. According to the relation between the profile face and frontal face, the final position of feature points on the profile face is obtained. We take limited facial feature points to do experiments and results show this kind of method is pretty effective.

scholarly journals Blob Detection and Deep Learning for Leukemic Blood Image Analysis

2020 ◽  
Vol 10 (3) ◽  
pp. 1176
Author(s):  
Cecilia Di Ruberto ◽  
Andrea Loddo ◽  
Giovanni Puglisi
Keyword(s):  
Blood Cells ◽  
Complete Blood Count ◽  
State Of The Art ◽  
White Blood Cells ◽  
Blob Detection ◽  
Manual Inspection ◽  
Blood Smear Examination ◽  
Novel Method ◽  
Public Datasets ◽  
Processing Techniques

In microscopy, laboratory tests make use of cell counters or flow cytometers to perform tests on blood cells, like the complete blood count, rapidly. However, a manual blood smear examination is still needed to verify the counter results and to monitor patients under therapy. Moreover, the manual inspection permits the description of the cells’ appearance, as well as any abnormalities. Unfortunately, manual analysis is long and tedious, and its result can be subjective and error-prone. Nevertheless, using image processing techniques, it is possible to automate the entire workflow, both reducing the operators’ workload and improving the diagnosis results. In this paper, we propose a novel method for recognizing white blood cells from microscopic blood images and classify them as healthy or affected by leukemia. The presented system is tested on public datasets for leukemia detection, the SMC-IDB, the IUMS-IDB, and the ALL-IDB. The results are promising, achieving 100% accuracy for the first two datasets and 99.7% for the ALL-IDB in white cells detection and 94.1% in leukemia classification, outperforming the state-of-the-art.

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