Rita de Cássia Fernandes de Lima

Breast thermography from an image processing viewpoint: A survey

Abstract Breast cancer is the leading cause of death among women. This fact justifies researches to reach early diagnosis, improving patients’ life expectancy. Moreover, there are other pathologies, such as cysts and benign neoplasms that deserve investigation. In the last ten years, the infrared thermography has shown to be a promising technique to early diagnosis of breast pathologies. Works on this subject presented results that justify the thermography as a complementary exam to detect breast diseases. Several papers on the use of infrared imaging for breast screening can be found in the current medical literature. This survey explores and analyses these works in the light of their applications in computer vision. Consequently, the comments are organized according to the main steps of pattern recognition systems. These include: image acquisition protocols, exams storage, segmentation methods, feature extraction, classification or diagnostic and computer modelling. Main contributions of discussed papers are summarized in tables to provide a structured vision of the aspects involved in breast thermography.

Estimation of breast tumor thermal properties using infrared images

Abstract The major goal of this paper is to help detect breast cancer early based on infrared images. Some procedures, protocols and numerical simulations were developed or performed. Two different issues are presented. The first is the development of a standardized protocol for the acquisition of breast thermal images including the design, construction and installation of mechanical apparatus. The second part is related to the greatest difficulty for the numerical computation of breast temperature profiles that is caused by the uncertainty of the real values of the thermophysical parameters of some tissues. Then, a methodology for estimating thermal properties based on these infrared images is presented. The commercial software FLUENTTM was used for the numerical simulation. A Sequential Quadratic Programming (SQP) method was used to solve the inverse problem and to estimate the thermal conductivity and blood perfusion of breast tissues. The results showed that it is possible to estimate the thermophysical properties using the thermography. The next stage will be to use the geometry of a real breast for the numerical simulation in conjunction with a linear mapping of the temperatures measured over the breast volume.

Interval symbolic feature extraction for thermography breast cancer detection

Breast cancer is one of the leading causes of death in women. Recent studies involving the use of thermal imaging as a screening technique have generated a growing interest especially in cases where the mammography is limited, as in young patients who have dense breast tissue. The aim of this work is to evaluate the feasibility of using interval data in the symbolic data analysis (SDA) framework to model breast abnormalities (malignant, benign and cyst) in order to detect breast cancer. SDA allows a more realistic description of the input units by taking into consideration their internal variation. In this direction, a three-stage feature extraction approach is proposed. In the first stage four intervals variables are obtained by the minimum and maximum temperature values from the morphological and thermal matrices. In the second one, operators based on dissimilarities for intervals are considered and then continuous features are obtained. In the last one, these continuous features are transformed by the Fishers criterion, giving the input data to the classification process. This three-stage approach is applied to a Brazilians thermography breast database and it is compared with a statistical feature extraction and a texture feature extraction approach widely used in thermal imaging studies. Different classifiers are considered to detect breast cancer, achieving 16% of misclassification rate, 85.7% of sensitivity and 86.5% of specificity to the malignant class.

Improved solutions for temperature and thermal power delivery profiles in linear solar collectors

For the conversion of absorbed sunlight into useful thermal power, we demonstrate that the profiles of absorber temperature, fluid temperature and thermal power delivery along linear solar collectors can be solved in closed form even when the collector heat-loss coefficient is far from constant over the collector operating range. This analytic solution eliminates the errors inherent in earlier approximate solutions, and makes the dependence of collector performance on component properties transparent. An example for a realistic solar concentrator illustrates the improvement in prediction accuracy.

Combining approaches for early diagnosis of breast diseases using thermal imaging

This paper presents a combination of efforts on the construction of a tool to help in the diagnosis by infrared breast images. It considers from the basic problem of image acquisition and storage in a database to the automatic extraction of the region of interest of each breast (left and right), the feature extraction, the decision and the limits of the diagnosis as well. The main objective of this study is the analysis of the viability of the use of the IR images for automatic detection of pathologies by texture symmetric analysis. Moreover numerical simulations and experimentations are developed in order to analyse the relation between the internal temperature of the breast and the temperature on the breast surface during the image acquisition.

On Using Lacunarity for Diagnosis of Breast Diseases Considering Thermal Images

A experimental scheme for identification of breast diseases based on thermal images is presented. A set of infrared images from a database that is being developed were used for an experimental investigation considering the image Lacunarity measures by the gliding box algorithm. This approach generates a parameter to distinguish from normal to abnormal breast diagnostics. We propose two interpretations based in similarity of the breast anatomic aspects. The original contributions of this work are the use of thermal image on diagnosis of breast disease and an approach that classifies images based on lacunarity indexes.

Breast cancer diagnosis based on mammary thermography and extreme learning machines

Introduction: Breast cancer is the most common cancer in women and one of the major causes of death from cancer among female around the world. The early detection and treatment are the major way to healing. The use of mammary thermography in Mastology is increasing as a complementary imaging technique to early detect lesions. Its use as a screening exam to identify breast disorders has been investigated. The aim of this study is to investigate the behavior of different classification methods while grouping the thermographic images into specific types of lesions. Methods: To evaluate our proposal, we built classifiers based on artificial neural networks, decision trees, Bayesian classifiers, and Haralick and Zernike attributes. The image database is composed by thermographic images acquired at the University Hospital of the Federal University of Pernambuco. These images are clinically classified into the classes cyst, malignant and benign. Moments of Zernike and Haralick were used as attributes. Results: Extreme Learning Machines (ELM) and Multilayer Perceptron networks (MLP) proved to be quite efficient classifiers for classification of breast lesions in thermographic images. Using 75% of the database for training, the maximum value obtained for accuracy was 73.38%, with a Kappa index of 0.6007. This result indicated to a sensitivity of 78% and specificity of 88%. The overall efficiency of the system was 83%. Conclusion: ELM showed to be a promising classifier to be used in the differentiation of breast lesions in thermographic images, due to its low computational cost and robustness.

Thermalhydraulic behavior of electrically heated rod during a critical heat flux transient

In nuclear reactors, the occurrence of critical heat flux leads to fuel rod overheating with clad fusion and radioactive products leakage. To predict the effects of such phenomenon, experiments are performed using electrically heated rods to simulate operational and accidental conditions of nuclear fuel rods. In the present work, it is performed a theoretical analysis of the drying and rewetting front propagation during a critical heat flux experiment, starting with the application of an electrical power step from steady state condition. After the occurrence of critical heat flux, the drying front propagation is predicted. After a few seconds, a power cut is considered and the rewetting front behavior is analytically observed. Studies performed with various values of coolant mass flow rate show that this variable has more influence on the drying front velocity than on the rewetting one.