Breast thermography: a non-invasive technique for the detection of lesions

Alvarado-Cruz, L. B.; Toxqui-Quitl, C.; Hernández-Tapia, Jorge; Padilla-Vivanco, Alfonso

doi:10.1117/12.2321969

Cited by 1 publication

(1 citation statement)

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“…A semi-automated segmentation algorithm is implemented employing a cubic degree polynomial curve fitting [19,20]. A binary image mask of size M × N pixels is created to eliminate the area under the inframammary curves detected by the polynomial curve fitting [21]. Neither image enhancements nor normalization of input data is done.…”

Section: Segmentation Of Breast Thermogramsmentioning

confidence: 99%

Highly Discriminative Physiological Parameters for Thermal Pattern Classification

Alvarado-Cruz

Toxqui-Quitl

Castro-Ortega

et al. 2021

Sensors

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Infrared Thermography (IRT) is a non-contact, non-intrusive, and non-ionizing radiation tool used for detecting breast lesions. This paper analyzes the surface temperature distribution (STD) on an optimal Region of Interest (RoI) for extraction of suitable internal heat source parameters. The physiological parameters are estimated through the inverse solution of the bio-heat equation and the STD of suspicious areas related to the hottest spots of the RoI. To reach these values, the STD is analyzed by means: the Depth-Intensity-Radius (D-I-R) measurement model and the fitting method of Lorentz curve. A highly discriminative pattern vector composed of the extracted physiological parameters is proposed to classify normal and abnormal breast thermograms. A well-defined RoI is delimited at a radial distance, determined by the Support Vector Machines (SVM). Nevertheless, this distance is less than or equal to 1.8 cm due to the maximum temperature location close to the boundary image. The methodology is applied to 87 breast thermograms that belong to the Database for Mastology Research with Infrared Image (DMR-IR). This methodology does not apply any image enhancements or normalization of input data. At an optimal position, the three-dimensional scattergrams show a correct separation between normal and abnormal thermograms. In other cases, the feature vectors are highly correlated. According to our experimental results, the proposed pattern vector extracted at optimal position a=1.6 cm reaches the highest sensitivity, specificity, and accuracy. Even more, the proposed technique utilizes a reduced number of physiological parameters to obtain a Correct Rate Classification (CRC) of 100%. The precision assessment confirms the performance superiority of the proposed method compared with other techniques for the breast thermogram classification of the DMR-IR.

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Section: Segmentation Of Breast Thermogramsmentioning

confidence: 99%