Automated Detection of Breast Cancer in Thermal Infrared Images, Based on Independent Component Analysis

Abstract

Breast cancer, among women, is the second-most common cancer and the leading cause of cancer death. It has become a major health issue in the world over the past decades and its incidence has increased in recent years mostly due to increased awareness of the importance of screening and population ageing. Early detection is crucial in the effective treatment of breast cancer. Current mammogram screening may turn up many tiny abnormalities that are either not cancerous or are slow-growing cancers that would never progress to the point of killing a woman and might never even become known to her. Ideally a better screening method should find a way of distinguishing the dangerous, aggressive tumors that need to be excised from the more languorous ones that do not. This paper therefore proposes a new method of thermographic image analysis for automated detection of high tumor risk areas, based on independent component analysis (ICA) and on post-processing of the images resulting from this algorithm. Tests carried out on a database enable tumor areas of 4 × 4 pixels on an original thermographic image to be detected. The proposed method has shown that the appearance of a heat anomaly indicating a potentially cancerous zone is reflected as an independent source by ICA analysis of the YCrCb components; the set of available images in our small series is giving us a sensitivity of 100% and a specificity of 94.7%.

Automatic Intelligent Asymmetry Detection Using Digital Infrared Imaging with K-Means Clustering

“…Over several decades, with rapidly developing computer technology and artificial intelligence, DITI has been extensively studied in many fields of medical science, such as diabetic neuropathy [6], vascular disorders [7], fever screening [8], dry eye syndrome diagnosis [9], and breast cancer detection [10][11][12][13], and even as a pre-screening tool for the detection of canine bone cancer [14]. For example, it has been reported that when state-of-the-art infrared technology was combined with advanced computer hardware and software technology, the sensitivity for breast cancer detection was 97% in 92 patients undergoing breast biopsy [15].…”

“…Since cancer cells with their higher metabolic rate are hotter than normal cells, which makes cancerous tumors appear as hotspots in DIT images, this technique is particularly useful in female breast | 180 imaging [13]. While higher-order statistics such as variance, skewness, kurtosis, and joint entropy are effective measures of asymmetry [13], there are other technical methodologies to develop an automatic asymmetry analysis system, such as independent component analysis [12], Hough transform-aided image segmentation and pattern classification [16], and fuzzy logic [17].…”

A Novel Model for Smart Breast Cancer Detection in Thermogram Images

“…Breast cancer, among women, is the second-most common cancer and the leading cause of cancer death. It has become a major health issue in the world over the past decades and its incidence has increased in recent years mostly due to increased awareness of the importance of screening and population ageing (Boquete et al, 2012). Currently, mammography is the dominant method for detection of breast cancer.…”

Scoliosis Follow-Up Using Noninvasive Trunk Surface Acquisition

“…Recently, ICA has been used for image segmentation [30] and medical image analysis [8,19,41]. Boquete et al [8] have proposed a thermographic image analysis based on ICA for automated detection of high tumor risk areas.…”

“…Boquete et al [8] have proposed a thermographic image analysis based on ICA for automated detection of high tumor risk areas. Hassen et al [19] used ICA to built a cardiovascular disease diagnosis system based on magnetic resonance imaging.…”