U Snekhalatha | SRM UNIVERSITY (original) (raw)

Papers by U Snekhalatha

Research paper thumbnail of Automated Detection of Cystitis in Ultrasound Images Using Deep Learning Techniques

IEEE Access

The proposed method aims to estimate the urinary Bladder Wall Thickness (BWT) from ultrasound (US... more The proposed method aims to estimate the urinary Bladder Wall Thickness (BWT) from ultrasound (US) images to detect cystitis. Our method proposes a novel deep learning algorithm that segments the Bladder Wall from the ultrasound images of the urinary bladder, following which feature extraction and classification are performed to categorize the images as presence or absence of cystitis. The proposed study focused on a CYSNET CNN (Convolutional Neural Network) model for detecting cystitis in the urinary bladder and compares its accuracy with a transfer learning-based pre-trained model like ResNet50 and stateof-the-art Vision Transformer. Among the total population studied (N=250), 125 subjects with cystitis and 125 normal subjects, were included. The bladder wall thickness of cystitis was segmented using the U-Net semantic segmentation model. Eight features constituting contour and thickness were extracted from the segmented bladder wall. The best five features were selected using the Univariate feature selection method based on ANOVA F statistics as the scoring scale. The selected five features were classified into cystitis and normal using three different Machine Learning (ML) Classifiers such as AdaBoost, RepTree, and NaÏve Bayes. Three different CYSNET models with varying convolution layers were developed to detect cystitis in ultrasound images. The performance of the CYSNET models is compared with the ML classifiers, ResNet 50 model, and Vision Transformer. The CYSNET model 3 outperformed with the classification accuracy of 95% compared to the Adaboost network (90%), ResNet50 model (88.7%) and Vision Transformer (92.1%). Hence, the developed CYSNET model could be used as a computer-aided diagnostic tool for the detection of cystitis in ultrasound images. INDEX TERMS ResNet50, CYSNET CNN model, cystitis, bladder wall segmentation, U-Net, vision transformer.

Research paper thumbnail of Design of Patient Specific Hip Prosthesis Based on Finite Element Analysis: A Comparative Study

Biomedical Engineering: Applications, Basis and Communications

This study aims to develop a patient-specific hip implant for osteoarthritis conditions and to co... more This study aims to develop a patient-specific hip implant for osteoarthritis conditions and to compare with intact and conventional implant. The femoral bone with head and shaft region was segmented from the pelvic griddle and converted into 3D model. The parameters such as femoral ball diameter, shaft length, acetabular cup diameter, and neck angle were measured from the segmented 3D model. In this study, designed part of hip implant was assembled together to form a customized hip implant. The von Mises stress was measured by means of Finite element analysis (FEA) method by applying various forces applied at the distal end of hip implant. The forces applied at hip implant were based on the assumption of 500 N force for standing, 2000 N force for walking, and 3000 N force for jogging condition. The minimum stress attained at the femur bone of custom-model is 1.32 MPa for 500 N loading condition, 5.3 MPa for 2000 N and 7.96 MPa for the maximum load of 3000 N. Thus the customized mode...

Research paper thumbnail of Ultrasound-Based Machine Learning-Aided Detection of Uterine Fibroids: Integrating Vision Transformer for Improved Analysis

Biomedical Engineering: Applications, Basis and Communications

The primary objective of this study is to segment the uterine fibroids (leiomyoma) from the ultra... more The primary objective of this study is to segment the uterine fibroids (leiomyoma) from the ultrasound images of the uterus through semantic segmentation, followed by second-order statistical feature extraction using the Gray-level Co-occurrence Matrix (GLCM). The next objective of the study is to compare the performance of the state-of-the-art method namely Vision Transformer (ViT) with three different machine learning (ML) classifiers such as the Support Vector Machine (SVM), Logistic Regression (LR) and [Formula: see text]-Nearest Neighbor ([Formula: see text]-NN) to classify the images into uterine fibroid and normal. The dataset consists of 50 ultrasound images of uterine fibroids and 50 normal images. Then the images are segmented using region-growing-based semantic segmentation followed by feature extraction and classification using the ML and deep learning (DL) classifiers. Among the ML classifiers, SVM produced a good accuracy of 93.1% compared to the other classifiers. ViT...

Research paper thumbnail of Facial emotion detection using thermal and visual images based on deep learning techniques

The Imaging Science Journal, Apr 18, 2023

Research paper thumbnail of Automated audiometer for home based health care based on mobile app

PROCEEDING OF INTERNATIONAL CONFERENCE ON ENERGY, MANUFACTURE, ADVANCED MATERIAL AND MECHATRONICS 2021

Research paper thumbnail of Fundamentals of Infrared Thermal Imaging

Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications

Research paper thumbnail of Comparative Study on the Preparation and Gas Sensing Properties of Reduced Graphene Oxide/SnO2 Binary Nanocomposite for Detection of Acetone in Exhaled Breath

Analytical Chemistry, 2019

Research paper thumbnail of Detection of face skin cancer using deep convoluted neural network

EIGHTH INTERNATIONAL CONFERENCE NEW TRENDS IN THE APPLICATIONS OF DIFFERENTIAL EQUATIONS IN SCIENCES (NTADES2021)

Research paper thumbnail of Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques

Scientific Reports

The objectives of our proposed study were as follows: First objective is to segment the CT images... more The objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM). Second objective is to implement machine learning classifiers such as Naïve bayes, bagging and Reptree to classify the images into two image classes namely COVID and non-COVID and to compare the performance of the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet with that of the proposed machine learning classifiers. Our dataset consists of 100 COVID and non-COVID images which are pre-processed and segmented with our proposed algorithm. Following the feature extraction process, three machine learning classifiers (Naive Bayes, Bagging, and REPTree) were used to classify the normal and covid patients. We had implemented the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet for comparing their perf...

Research paper thumbnail of Thermal Imaging for Inflammatory Arthritis Evaluation

Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications

Research paper thumbnail of Thermal Imaging for Arthritis Evaluation in a Small Animal Model

Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications

Research paper thumbnail of A lung tumor detection technique using gradient vector flow algorithm

EIGHTH INTERNATIONAL CONFERENCE NEW TRENDS IN THE APPLICATIONS OF DIFFERENTIAL EQUATIONS IN SCIENCES (NTADES2021), 2022

Research paper thumbnail of Automated detection of orofacial pain from thermograms using machine learning and deep learning approaches

Expert Systems, 2021

The main objectives of this study are (i) to perform automated segmentation of facial regions fro... more The main objectives of this study are (i) to perform automated segmentation of facial regions from thermograms using k‐means clustering algorithm and to classify the data into normal and orofacial pain (OFP) categories using various machine learning classifiers (ii) to implement the convolutional neural network (CNN) for classification of normal and OFP subjects which involves automated feature extraction and feature selection process. Fifty normal and 50 diseased cases suffering from orofacial pain were included in the study. Facial thermograms were segmented using k‐means algorithm, then statistical features were extracted and classified into normal and OFP using various machine learning classifier. Further, the deep learning networks such as VGG‐16 and DenseNet‐121 were used for automated feature extraction and classification of facial thermograms. The facial temperature variations of 3.46%, 3.4%, and 3.27% were observed in the front, right and left side facial regions respectively between the normal and the OFP subjects. Machine learning classifiers such as support vector machine (SVM) and random forest (RF) classifier provided the highest accuracy of 99%. On the other hand, deep learning models such as modified VGG‐16 achieved an average accuracy of 97% compared to modified DenseNet‐121 which produced an average accuracy of 68% in classification of normal and OFP thermograms. Thus, computer aided diagnosis of facial thermography could be used as a viable screening device for a reliable identification of tooth pathology before the occurrence of structural changes and complications.

Research paper thumbnail of A computer aided diagnostic method for the evaluation of type II diabetes mellitus in facial thermograms

Physical and Engineering Sciences in Medicine, 2020

Almost 50% of individuals around the globe are unaware of diabetes and its complications. So, an ... more Almost 50% of individuals around the globe are unaware of diabetes and its complications. So, an early screening of diabetes is very important at this current situation. To overcome the difficulties such as pain and discomfort to the subjects obtained from the biochemical diagnostic procedures; an infrared thermography is the diagnostic technique which measures the skin surface temperature noninvasively. Thus, the aim of our proposed study was to evaluate the type II diabetes in facial thermograms and to develop a computer aided diagnosis (CAD) system to classify the normal and diabetes. The facial thermograms (n = 160) including male (n = 79) and female (n = 81) were captured using FLIR A 305sc infrared thermal camera. The Haralick textural features were extracted from the facial thermograms based on gray level co-occurrence matrix algorithm. The T ROI , T MAX , and T TOT are the statistical temperature parameters exhibited a significant negative correlation with HbA1c (r = − 0.421, − 0.411, − 0.242, p < 0.01 (T ROI); r = − 0.259, p < 0.01(T MAX) and − 0.173, p < 0.05 (T TOT)). An optimal regression equation has been constructed by using the significant facial variables and standard HbA1c values. The model has achieved sensitivity, specificity, and accuracy rate as 91.42%, 88.57%, and 90% respectively. The anthropometrical variables, extracted textural features and temperature parameters were fed into the classifiers and their performances were compared. The Support Vector Machine outperformed the Linear Discriminant Analysis (84.37%) and k-Nearest Neighbor (81.25%) classifiers with the maximum accuracy rate of 89.37%. The developed CAD system has achieved 89.37% of accuracy rate for the classification of diabetes. Thus, the facial thermography could be used as the basic non-invasive prognostic tool for the evaluation of type II diabetes mellitus.

Research paper thumbnail of Automated speech signal analysis based on feature extraction and classification of spasmodic dysphonia: a performance comparison of different classifiers

International Journal of Speech Technology, 2017

Research paper thumbnail of Evaluation of low bone mass and prediction of fracture risk using metacarpal radiogrammetry method: a comparative study with DXA and X-ray phantom

International journal of rheumatic diseases, 2018

Objectives: (i) To predict the future risk of osteoporotic fracture in women using a simple forea... more Objectives: (i) To predict the future risk of osteoporotic fracture in women using a simple forearm radiograph. (ii) To assess osteoporosis in southern Indian women by using radiogrammetric technique in comparison with dual-energy X-ray absorptiometry (DXA) and X-ray phantom study. Methods: The bone mineral density (BMD) of the right proximal femur by DXA and the X-ray measurements were acquired from the right forearm. The combined cortical thickness at the second to fourth metacarpal region (M-CCT), radius (R-CCT) and ulna (U-CCT) were derived in all the studied population. The aluminium phantom study was conducted by varying the X-ray source to film distance at 100 cm and 150 cm, respectively. The feed forward back propagation neural network was used for classification of low bone mass group and normal. Results: The combined cortical thickness of M-CCT, R-CCT and U-CCT of the total studied population was strongly correlated with DXA femur Th.BMD measurements (r = 0.77, r = 0.61 and r = 0.59 [P < 0.01]). The predicted future osteoporotic fracture risk for the low bone mass group, post-menopausal women and old-aged women population was found to be 92%, 62.8%, and 64.7%, respectively. The accuracy of neural network classifier for training set, testing set was found to be 97.5% and 87.5% in the studied population. Conclusion: The results suggested that M-CCT and M-CCT (%) at the second metacarpal region are useful in predicting the future risk of osteoporotic fracture in women. The aluminium phantom study with an X-ray tube to film distance of 100 cm mimics an exact condition of forearm radiogrammetry.

Research paper thumbnail of Automated Segmentation and Classification of High Throughput Yeast Assay Spots

IEEE Transactions on Medical Imaging, 2007

Several technologies for characterizing genes and proteins from humans and other organisms use ye... more Several technologies for characterizing genes and proteins from humans and other organisms use yeast growth or color development as read outs. The yeast two-hybrid assay, for example, detects protein-protein interactions by measuring the growth of yeast on a specific solid medium, or the ability of the yeast to change color when grown on a medium containing a chromogenic substrate. Current systems for analyzing the results of these types of assays rely on subjective and inefficient scoring of growth or color by human experts. Here an image analysis system is described for scoring yeast growth and color development in high throughput biological assays. The goal is to locate the spots and score them in color images of two types of plates named "X-Gal" and "growth assay" plates, with uniformly placed spots (cell areas) on each plate (both plates in one image). The scoring system relies on color for the X-Gal spots, and texture properties for the growth assay spots. A maximum likelihood projection-based segmentation is developed to automatically locate spots of yeast on each plate. Then color histogram and wavelet texture features are extracted for scoring using an optimal linear transformation. Finally an artificial neural network is used to score the X-Gal and growth assay spots using the extracted features. The performance of the system is evaluated using spots of 60 images. After training the networks using training and validation sets, the system was assessed on the test set. The overall accuracies of 95.4% and 88.2% are achieved respectively for scoring the X-Gal and growth assay spots.

Research paper thumbnail of Potential of Thermal Imaging to Detect Complications in Diabetes

Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications

Research paper thumbnail of Thermal imaging method to evaluate childhood obesity based on machine learning techniques

International Journal of Imaging Systems and Technology, 2021

The purposes of the study were (i) to determine the potential of thermal imaging to assess the di... more The purposes of the study were (i) to determine the potential of thermal imaging to assess the difference in the thermal pattern in various body regions of studied population; (ii) to compare the performance of feature extraction, feature fusion, feature ranking and feature dimension reduction (PCA) in classification of obese and normal children using different Machine learning algorithms. About 600 thermograms were obtained from various regions such as abdomen, finger bed, forearm, neck, shank and gluteal region for the studied population. Fifteen statistical textual features were extracted from the six regional thermograms followed by implementing feature fusion with SIFT and SURF algorithm. The PCA method provides the best classification accuracy for SVM (98%) followed by Naïve Bayes and Random Forest (97%). Thus, the regional thermography and computer aided diagnostic tool with machine learning classifier could be used as a basic non‐invasive prognostic tool for the evaluation of obesity in children.

Research paper thumbnail of Thermal Imaging in Detection of Fever for Infectious Diseases

Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications

Research paper thumbnail of Automated Detection of Cystitis in Ultrasound Images Using Deep Learning Techniques

IEEE Access

The proposed method aims to estimate the urinary Bladder Wall Thickness (BWT) from ultrasound (US... more The proposed method aims to estimate the urinary Bladder Wall Thickness (BWT) from ultrasound (US) images to detect cystitis. Our method proposes a novel deep learning algorithm that segments the Bladder Wall from the ultrasound images of the urinary bladder, following which feature extraction and classification are performed to categorize the images as presence or absence of cystitis. The proposed study focused on a CYSNET CNN (Convolutional Neural Network) model for detecting cystitis in the urinary bladder and compares its accuracy with a transfer learning-based pre-trained model like ResNet50 and stateof-the-art Vision Transformer. Among the total population studied (N=250), 125 subjects with cystitis and 125 normal subjects, were included. The bladder wall thickness of cystitis was segmented using the U-Net semantic segmentation model. Eight features constituting contour and thickness were extracted from the segmented bladder wall. The best five features were selected using the Univariate feature selection method based on ANOVA F statistics as the scoring scale. The selected five features were classified into cystitis and normal using three different Machine Learning (ML) Classifiers such as AdaBoost, RepTree, and NaÏve Bayes. Three different CYSNET models with varying convolution layers were developed to detect cystitis in ultrasound images. The performance of the CYSNET models is compared with the ML classifiers, ResNet 50 model, and Vision Transformer. The CYSNET model 3 outperformed with the classification accuracy of 95% compared to the Adaboost network (90%), ResNet50 model (88.7%) and Vision Transformer (92.1%). Hence, the developed CYSNET model could be used as a computer-aided diagnostic tool for the detection of cystitis in ultrasound images. INDEX TERMS ResNet50, CYSNET CNN model, cystitis, bladder wall segmentation, U-Net, vision transformer.

Research paper thumbnail of Design of Patient Specific Hip Prosthesis Based on Finite Element Analysis: A Comparative Study

Biomedical Engineering: Applications, Basis and Communications

This study aims to develop a patient-specific hip implant for osteoarthritis conditions and to co... more This study aims to develop a patient-specific hip implant for osteoarthritis conditions and to compare with intact and conventional implant. The femoral bone with head and shaft region was segmented from the pelvic griddle and converted into 3D model. The parameters such as femoral ball diameter, shaft length, acetabular cup diameter, and neck angle were measured from the segmented 3D model. In this study, designed part of hip implant was assembled together to form a customized hip implant. The von Mises stress was measured by means of Finite element analysis (FEA) method by applying various forces applied at the distal end of hip implant. The forces applied at hip implant were based on the assumption of 500 N force for standing, 2000 N force for walking, and 3000 N force for jogging condition. The minimum stress attained at the femur bone of custom-model is 1.32 MPa for 500 N loading condition, 5.3 MPa for 2000 N and 7.96 MPa for the maximum load of 3000 N. Thus the customized mode...

Research paper thumbnail of Ultrasound-Based Machine Learning-Aided Detection of Uterine Fibroids: Integrating Vision Transformer for Improved Analysis

Biomedical Engineering: Applications, Basis and Communications

The primary objective of this study is to segment the uterine fibroids (leiomyoma) from the ultra... more The primary objective of this study is to segment the uterine fibroids (leiomyoma) from the ultrasound images of the uterus through semantic segmentation, followed by second-order statistical feature extraction using the Gray-level Co-occurrence Matrix (GLCM). The next objective of the study is to compare the performance of the state-of-the-art method namely Vision Transformer (ViT) with three different machine learning (ML) classifiers such as the Support Vector Machine (SVM), Logistic Regression (LR) and [Formula: see text]-Nearest Neighbor ([Formula: see text]-NN) to classify the images into uterine fibroid and normal. The dataset consists of 50 ultrasound images of uterine fibroids and 50 normal images. Then the images are segmented using region-growing-based semantic segmentation followed by feature extraction and classification using the ML and deep learning (DL) classifiers. Among the ML classifiers, SVM produced a good accuracy of 93.1% compared to the other classifiers. ViT...

Research paper thumbnail of Facial emotion detection using thermal and visual images based on deep learning techniques

The Imaging Science Journal, Apr 18, 2023

Research paper thumbnail of Automated audiometer for home based health care based on mobile app

PROCEEDING OF INTERNATIONAL CONFERENCE ON ENERGY, MANUFACTURE, ADVANCED MATERIAL AND MECHATRONICS 2021

Research paper thumbnail of Fundamentals of Infrared Thermal Imaging

Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications

Research paper thumbnail of Comparative Study on the Preparation and Gas Sensing Properties of Reduced Graphene Oxide/SnO2 Binary Nanocomposite for Detection of Acetone in Exhaled Breath

Analytical Chemistry, 2019

Research paper thumbnail of Detection of face skin cancer using deep convoluted neural network

EIGHTH INTERNATIONAL CONFERENCE NEW TRENDS IN THE APPLICATIONS OF DIFFERENTIAL EQUATIONS IN SCIENCES (NTADES2021)

Research paper thumbnail of Automated system for classification of COVID-19 infection from lung CT images based on machine learning and deep learning techniques

Scientific Reports

The objectives of our proposed study were as follows: First objective is to segment the CT images... more The objectives of our proposed study were as follows: First objective is to segment the CT images using a k-means clustering algorithm for extracting the region of interest and to extract textural features using gray level co-occurrence matrix (GLCM). Second objective is to implement machine learning classifiers such as Naïve bayes, bagging and Reptree to classify the images into two image classes namely COVID and non-COVID and to compare the performance of the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet with that of the proposed machine learning classifiers. Our dataset consists of 100 COVID and non-COVID images which are pre-processed and segmented with our proposed algorithm. Following the feature extraction process, three machine learning classifiers (Naive Bayes, Bagging, and REPTree) were used to classify the normal and covid patients. We had implemented the three pre-trained CNN models such as AlexNet, ResNet50 and SqueezeNet for comparing their perf...

Research paper thumbnail of Thermal Imaging for Inflammatory Arthritis Evaluation

Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications

Research paper thumbnail of Thermal Imaging for Arthritis Evaluation in a Small Animal Model

Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications

Research paper thumbnail of A lung tumor detection technique using gradient vector flow algorithm

EIGHTH INTERNATIONAL CONFERENCE NEW TRENDS IN THE APPLICATIONS OF DIFFERENTIAL EQUATIONS IN SCIENCES (NTADES2021), 2022

Research paper thumbnail of Automated detection of orofacial pain from thermograms using machine learning and deep learning approaches

Expert Systems, 2021

The main objectives of this study are (i) to perform automated segmentation of facial regions fro... more The main objectives of this study are (i) to perform automated segmentation of facial regions from thermograms using k‐means clustering algorithm and to classify the data into normal and orofacial pain (OFP) categories using various machine learning classifiers (ii) to implement the convolutional neural network (CNN) for classification of normal and OFP subjects which involves automated feature extraction and feature selection process. Fifty normal and 50 diseased cases suffering from orofacial pain were included in the study. Facial thermograms were segmented using k‐means algorithm, then statistical features were extracted and classified into normal and OFP using various machine learning classifier. Further, the deep learning networks such as VGG‐16 and DenseNet‐121 were used for automated feature extraction and classification of facial thermograms. The facial temperature variations of 3.46%, 3.4%, and 3.27% were observed in the front, right and left side facial regions respectively between the normal and the OFP subjects. Machine learning classifiers such as support vector machine (SVM) and random forest (RF) classifier provided the highest accuracy of 99%. On the other hand, deep learning models such as modified VGG‐16 achieved an average accuracy of 97% compared to modified DenseNet‐121 which produced an average accuracy of 68% in classification of normal and OFP thermograms. Thus, computer aided diagnosis of facial thermography could be used as a viable screening device for a reliable identification of tooth pathology before the occurrence of structural changes and complications.

Research paper thumbnail of A computer aided diagnostic method for the evaluation of type II diabetes mellitus in facial thermograms

Physical and Engineering Sciences in Medicine, 2020

Almost 50% of individuals around the globe are unaware of diabetes and its complications. So, an ... more Almost 50% of individuals around the globe are unaware of diabetes and its complications. So, an early screening of diabetes is very important at this current situation. To overcome the difficulties such as pain and discomfort to the subjects obtained from the biochemical diagnostic procedures; an infrared thermography is the diagnostic technique which measures the skin surface temperature noninvasively. Thus, the aim of our proposed study was to evaluate the type II diabetes in facial thermograms and to develop a computer aided diagnosis (CAD) system to classify the normal and diabetes. The facial thermograms (n = 160) including male (n = 79) and female (n = 81) were captured using FLIR A 305sc infrared thermal camera. The Haralick textural features were extracted from the facial thermograms based on gray level co-occurrence matrix algorithm. The T ROI , T MAX , and T TOT are the statistical temperature parameters exhibited a significant negative correlation with HbA1c (r = − 0.421, − 0.411, − 0.242, p < 0.01 (T ROI); r = − 0.259, p < 0.01(T MAX) and − 0.173, p < 0.05 (T TOT)). An optimal regression equation has been constructed by using the significant facial variables and standard HbA1c values. The model has achieved sensitivity, specificity, and accuracy rate as 91.42%, 88.57%, and 90% respectively. The anthropometrical variables, extracted textural features and temperature parameters were fed into the classifiers and their performances were compared. The Support Vector Machine outperformed the Linear Discriminant Analysis (84.37%) and k-Nearest Neighbor (81.25%) classifiers with the maximum accuracy rate of 89.37%. The developed CAD system has achieved 89.37% of accuracy rate for the classification of diabetes. Thus, the facial thermography could be used as the basic non-invasive prognostic tool for the evaluation of type II diabetes mellitus.

Research paper thumbnail of Automated speech signal analysis based on feature extraction and classification of spasmodic dysphonia: a performance comparison of different classifiers

International Journal of Speech Technology, 2017

Research paper thumbnail of Evaluation of low bone mass and prediction of fracture risk using metacarpal radiogrammetry method: a comparative study with DXA and X-ray phantom

International journal of rheumatic diseases, 2018

Objectives: (i) To predict the future risk of osteoporotic fracture in women using a simple forea... more Objectives: (i) To predict the future risk of osteoporotic fracture in women using a simple forearm radiograph. (ii) To assess osteoporosis in southern Indian women by using radiogrammetric technique in comparison with dual-energy X-ray absorptiometry (DXA) and X-ray phantom study. Methods: The bone mineral density (BMD) of the right proximal femur by DXA and the X-ray measurements were acquired from the right forearm. The combined cortical thickness at the second to fourth metacarpal region (M-CCT), radius (R-CCT) and ulna (U-CCT) were derived in all the studied population. The aluminium phantom study was conducted by varying the X-ray source to film distance at 100 cm and 150 cm, respectively. The feed forward back propagation neural network was used for classification of low bone mass group and normal. Results: The combined cortical thickness of M-CCT, R-CCT and U-CCT of the total studied population was strongly correlated with DXA femur Th.BMD measurements (r = 0.77, r = 0.61 and r = 0.59 [P < 0.01]). The predicted future osteoporotic fracture risk for the low bone mass group, post-menopausal women and old-aged women population was found to be 92%, 62.8%, and 64.7%, respectively. The accuracy of neural network classifier for training set, testing set was found to be 97.5% and 87.5% in the studied population. Conclusion: The results suggested that M-CCT and M-CCT (%) at the second metacarpal region are useful in predicting the future risk of osteoporotic fracture in women. The aluminium phantom study with an X-ray tube to film distance of 100 cm mimics an exact condition of forearm radiogrammetry.

Research paper thumbnail of Automated Segmentation and Classification of High Throughput Yeast Assay Spots

IEEE Transactions on Medical Imaging, 2007

Several technologies for characterizing genes and proteins from humans and other organisms use ye... more Several technologies for characterizing genes and proteins from humans and other organisms use yeast growth or color development as read outs. The yeast two-hybrid assay, for example, detects protein-protein interactions by measuring the growth of yeast on a specific solid medium, or the ability of the yeast to change color when grown on a medium containing a chromogenic substrate. Current systems for analyzing the results of these types of assays rely on subjective and inefficient scoring of growth or color by human experts. Here an image analysis system is described for scoring yeast growth and color development in high throughput biological assays. The goal is to locate the spots and score them in color images of two types of plates named "X-Gal" and "growth assay" plates, with uniformly placed spots (cell areas) on each plate (both plates in one image). The scoring system relies on color for the X-Gal spots, and texture properties for the growth assay spots. A maximum likelihood projection-based segmentation is developed to automatically locate spots of yeast on each plate. Then color histogram and wavelet texture features are extracted for scoring using an optimal linear transformation. Finally an artificial neural network is used to score the X-Gal and growth assay spots using the extracted features. The performance of the system is evaluated using spots of 60 images. After training the networks using training and validation sets, the system was assessed on the test set. The overall accuracies of 95.4% and 88.2% are achieved respectively for scoring the X-Gal and growth assay spots.

Research paper thumbnail of Potential of Thermal Imaging to Detect Complications in Diabetes

Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications

Research paper thumbnail of Thermal imaging method to evaluate childhood obesity based on machine learning techniques

International Journal of Imaging Systems and Technology, 2021

The purposes of the study were (i) to determine the potential of thermal imaging to assess the di... more The purposes of the study were (i) to determine the potential of thermal imaging to assess the difference in the thermal pattern in various body regions of studied population; (ii) to compare the performance of feature extraction, feature fusion, feature ranking and feature dimension reduction (PCA) in classification of obese and normal children using different Machine learning algorithms. About 600 thermograms were obtained from various regions such as abdomen, finger bed, forearm, neck, shank and gluteal region for the studied population. Fifteen statistical textual features were extracted from the six regional thermograms followed by implementing feature fusion with SIFT and SURF algorithm. The PCA method provides the best classification accuracy for SVM (98%) followed by Naïve Bayes and Random Forest (97%). Thus, the regional thermography and computer aided diagnostic tool with machine learning classifier could be used as a basic non‐invasive prognostic tool for the evaluation of obesity in children.

Research paper thumbnail of Thermal Imaging in Detection of Fever for Infectious Diseases

Artificial Intelligence-Based Infrared Thermal Image Processing and Its Applications