A Texture-Based Analysis and Classification of Fruits Using Digital and Thermal Images

2020 ◽  
pp. 333-343 ◽  
Author(s):  
Varsha Bhole ◽  
Arun Kumar ◽  
Divya Bhatnagar
Keyword(s):  
Thermal Images

Classification of Breast Thermograms Using Statistical Moments and Entropy Features with Probabilistic Neural Networks

2020 ◽  
pp. 1328-1340
Author(s):  
Natarajan Sriraam ◽  
Leema Murali ◽  
Amoolya Girish ◽  
Manjunath Sirur ◽  
Sushmitha Srinivas ◽  
...  
Keyword(s):  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Histogram Equalization ◽  
Developed Countries ◽  
Left Breast ◽  
Statistical Moments ◽  
Thermal Images ◽  
Life Threatening ◽  
The Right

Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applying bilateral ratio. A total of 95 test images (normal = 53, abnormal = 42) was considered. Simulation study shows that CLAHE -RD with wavelet entropy features confirms the existence of symmetry on the right and left breast thermal images. An overall classification accuracy of 92.5% was achieved using the proposed multifeatures with PNN classifier. The proposed technique thus confirms the suitability as a screening tool for asymmetry detection as well as classification of breast thermograms.

Classification of Breast Thermograms Using Statistical Moments and Entropy Features with Probabilistic Neural Networks

2020 ◽  
pp. 1175-1187
Author(s):  
Natarajan Sriraam ◽  
Leema Murali ◽  
Amoolya Girish ◽  
Manjunath Sirur ◽  
Sushmitha Srinivas ◽  
...  
Keyword(s):  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Histogram Equalization ◽  
Developed Countries ◽  
Left Breast ◽  
Statistical Moments ◽  
Thermal Images ◽  
Life Threatening ◽  
The Right

Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applying bilateral ratio. A total of 95 test images (normal = 53, abnormal = 42) was considered. Simulation study shows that CLAHE -RD with wavelet entropy features confirms the existence of symmetry on the right and left breast thermal images. An overall classification accuracy of 92.5% was achieved using the proposed multifeatures with PNN classifier. The proposed technique thus confirms the suitability as a screening tool for asymmetry detection as well as classification of breast thermograms.

scholarly journals Clinical Trial on the Characteristics of Zheng Classification of Pulmonary Diseases Based on Infrared Thermal Imaging Technology

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Jin-xia Ni ◽  
Si-hua Gao ◽  
Yu-hang Li ◽  
Shi-lei Ma ◽  
Tian Tian ◽  
...  
Keyword(s):  
Clinical Trial ◽  
Pulmonary Disease ◽  
Thermal Imaging ◽  
Relative Temperature ◽  
Infrared Thermal Imaging ◽  
Imaging Technology ◽  
Thermal Images ◽  
Diagnosis Rate ◽  
Infrared Thermal Images

Zheng classification study based on infrared thermal imaging technology has not been reported before. To detect the relative temperature of viscera and bowels of different syndromes patients with pulmonary disease and to summarize the characteristics of different Zheng classifications, the infrared thermal imaging technology was used in the clinical trial. The results showed that the infrared thermal images characteristics of different Zheng classifications of pulmonary disease were distinctly different. The influence on viscera and bowels was deeper in phlegm-heat obstructing lung syndrome group than in cold-phlegm obstructing lung syndrome group. It is helpful to diagnose Zheng classification and to improve the diagnosis rate by analyzing the infrared thermal images of patients. The application of infrared thermal imaging technology provided objective measures for medical diagnosis and treatment in the field of Zheng studies and provided a new methodology for Zheng classification.

Classification of Breast Thermograms Using Statistical Moments and Entropy Features with Probabilistic Neural Networks

2017 ◽  
Vol 6 (2) ◽  
pp. 18-32
Author(s):  
Natarajan Sriraam ◽  
Leema Murali ◽  
Amoolya Girish ◽  
Manjunath Sirur ◽  
Sushmitha Srinivas ◽  
...  
Keyword(s):  
Probabilistic Neural Network ◽  
Wavelet Packet ◽  
Histogram Equalization ◽  
Developed Countries ◽  
Left Breast ◽  
Statistical Moments ◽  
Thermal Images ◽  
Life Threatening ◽  
The Right

Breast cancer is considered as one of the life-threatening disease among woman population in developing as well as developed countries. This specific study reports on classification of breast thermograms using probabilistic neural network (PNN) with four statistical moments features mean, standard deviation, skewness and kurtosis and two entropy features, Shannon entropy and Wavelet packet entropy. The CLAHE histogram equalization algorithm with uniform and Rayleigh distributions were considered for contrast enhancement of breast thermal images. The asymmetry detection was performed by applying bilateral ratio. A total of 95 test images (normal = 53, abnormal = 42) was considered. Simulation study shows that CLAHE -RD with wavelet entropy features confirms the existence of symmetry on the right and left breast thermal images. An overall classification accuracy of 92.5% was achieved using the proposed multifeatures with PNN classifier. The proposed technique thus confirms the suitability as a screening tool for asymmetry detection as well as classification of breast thermograms.

Early detection and classification of powdery mildew-infected rose leaves using ANFIS based on extracted features of thermal images

2016 ◽  
Vol 76 ◽  
pp. 338-345 ◽  
Author(s):  
Mehrnoosh Jafari ◽  
Saeid Minaei ◽  
Naser Safaie ◽  
Farah Torkamani-Azar
Keyword(s):  
Powdery Mildew ◽  
Early Detection ◽  
Thermal Images

scholarly journals Digital technologies in the prevention of diabetic foot: a review on mobile applications

2020 ◽  
Author(s):  
Sabado Gomes Dabó ◽  
Maria Girlane Sousa Albuquerque Brandão ◽  
Thiago Moura de Araújo ◽  
Natasha Marques Frota ◽  
Vivian Saraiva Veras
Keyword(s):  
Diabetic Foot ◽  
Mobile Applications ◽  
Mobile Application ◽  
Web Of Science ◽  
Diabetic Foot Ulcers ◽  
Eligibility Criteria ◽  
Thermal Images ◽  
Prevention And Early Detection ◽  
Good Prevention

Analyze mobile applications developed for prevention of diabetic foot. Method: Integrative review, with searches in LILACS, BDENF, Scopus, Web of Science and PubMed databases, from 2000 to 2019. After eligibility criteria, the sample consisted of nine articles. Results: The mobile applications for prevention of diabetic foot are based on online foot monitoring through images, evaluation of thermal images of the feet, capture of images of the sole of the foot, recommendations for self-care with the feet and classification of the risk of diabetic foot. The analysis of the articles shows that the applications were considered a good prevention strategy. Conclusion: This study enabled the identification of nine mobile applications developed for prevention of diabetic foot, with predominance in the use of thermometry as the main measure for prevention and early detection of diabetic foot ulcers, with the use of thermal images and sensors associated with the mobile application.

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