Higher order spectra analysis of breast thermograms for the automated identification of breast cancer

Acharya, U. Rajendra; Ng, E. Y. K.; Sree, S. Vinitha; Chua, Chua Kuang; Chattopadhyay, Subhagata

doi:10.1111/j.1468-0394.2012.00654.x

Cited by 47 publications

(10 citation statements)

References 36 publications

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“…In [30], On the other hand, the researchers used the Ti20, which is considered the same faction of thermal cameras as Ti40FT, but of lower quality as the sensitivity is only 0.2 and a resolution of 128x96 pixels. The researchers [31] extracted five Higher-order Spectral features to evaluate their use in screening for breast cancer. Two classifications used to classify normal and abnormal breast thermograms which are Artificial Neural Network (ANN) and Support Vector Machine (SVM).…”

Section: Related Workmentioning

confidence: 99%

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

et al. 2020

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Section: Related Workmentioning

confidence: 99%

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

et al. 2020

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“…with a constant temperature, humidity, no direct sunlight etc.). 6 During the measurements each patient was instructed to sit up straight, and the head was positioned in front of an infrared thermal camera. Under the conditions of this study, the camera was placed 1 m from the patient's face.…”

Section: Data Acquisitionmentioning

confidence: 99%

Comparison of segmented thermal images versus a CT scanning for detection of maxillofacial pathology

Mačianskytė

Monastyreckienė

Basevičius

et al. 2019

Dentomaxillofacial Radiology

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Objectives: To evaluate thermal images (TIs) by using an algorithm for optimized region of interest (ROI) and image segmentation, in order to find zones of the facial skin surface with asymmetrical temperature, and to test consistency with CT findings, to detect maxillofacial pathologies (i.e. tumours). Methods: The following steps for the TI evaluation were applied: data acquisition/pre-processing of frontal face and mouth projection, detection of face and mouth external contour, finding face and mouth symmetry axis, calculation of differences in average and maximal temperatures between left and right face and mouth sides, image segmentation of the selected ROI, and evaluation of diagnostic accuracy by comparing the TI results with CT findings. Results: In healthy subjects, the average temperature difference between left/right sides of facial and mouth ROI was negligible (0.02 ± 0.21 °C and 0.05 ± 0.19 °C, respectively; n = 23). In the presence of tumour, the average temperature difference was higher in corresponding TIs (0.47 ± 0.1 °C and 0.66 ± 0.1 °C, for facial and mouth ROI, respectively; n = 19, p < 0.05). For large tumours, thermal asymmetry in the corresponding TI is easily detected, and image segmentation is optional for finding the affected zone. For small or deeply localized tumours, segmentation of the mouth cavity of the ROI was required for the detection of hot and cold spots. conclusions: Asymmetrical temperature zones and their location as detected from thermal images coincide well with the presence and localization of maxillofacial pathologies (i.e. tumours) established by CT. However, accurate information could often be obtained only after application of image segmentation algorithm to the selected ROI.

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“…To make our proposed approach, in this paper comparable with its related work, we have limited this related work to the efforts done using the DMR-IR database [15]. These efforts can be classified into two classes: automatic segmentation of breast regions [7]] [8] and classification based on the asymmetry analysis to normal and abnormal cases [16], [17], [18].…”

Section: Literature Reviewmentioning

confidence: 99%

Detection of Breast Abnormalities of Thermograms based on a New Segmentation Method

Ali

Sayed

Gaber

et al. 2015

Annals of Computer Science and Information Systems

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Abstract-Breast cancer is one from various diseases that has got great attention in the last decades. This due to the number of women who died because of this disease. Segmentation is always an important step in developing a CAD system. This paper proposed an automatic segmentation method for the Region of Interest (ROI) from breast thermograms. This method is based on the data acquisition protocol parameter (the distance from the patient to the camera) and the image statistics of DMR-IR database. To evaluated the results of this method, an approach for the detection of breast abnormalities of thermograms was also proposed. Statistical and texture features from the segmented ROI were extracted and the SVM with its kernel function was used to detect the normal and abnormal breasts based on these features. The experimental results, using the benchmark database, DMR-IR, shown that the classification accuracy reached (100%). Also, using the measurements of the recall and the precision, the classification results reached 100%. This means that the proposed segmentation method is a promising technique for extracting the ROI of breast thermograms.

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Higher order spectra analysis of breast thermograms for the automated identification of breast cancer

Cited by 47 publications

References 36 publications

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

A Systematic Review of Breast Cancer Detection Using Thermography and Neural Networks

Comparison of segmented thermal images versus a CT scanning for detection of maxillofacial pathology

Detection of Breast Abnormalities of Thermograms based on a New Segmentation Method

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