American Cancer Society estimates that in 2021 nearly 300,000 women in the United States will be diagnosed with invasive breast cancer, and about 43,600 women will die from breast cancer. While many have access to health care and cancer screening, women from rural or underdeveloped communities often have limited access. Therefore, there is a need for an inexpensive and easy-to-use breast cancer identification device, which can be employed in small clinics to provide support to primary care physicians. This work aims to develop a method to characterize breast tumors and tissue using non-invasive imaging modalities. The proposed bimodal imaging system has tactile and multispectral imaging capabilities. Tactile imaging modality characterizes tumors by esti-mating their depth, size, and stiffness, along with the Tactile Index. Multispectral imaging modality identifies breast asymmetry, texture, and inflammation changes, together with the Spectral Index. These indices are combined with the BCRAT Index, the risk score devel¬oped by the National Institute of Health, to form the Multimodal Index for personalized breast cancer risk assessment. In this study, we will describe the development of the bimodal imaging system. We will present the algorithms for tactile and multispectral modalities. Tactile and Multispec¬tral Profile Diagrams are developed to capture broad imaging signals in a compact and application-specific way. A Tactile Profile Diagram is a pictorial representation of the rel¬ative depth, size, and stiffness of the imaged tumor. A Multispectral Profile Diagram is a representative pattern image for breast tissue superficial optical properties. To classify the profile diagrams, we employ the Convolutional Neural Network deep learning method. We will describe the results of the experiments conducted using tissue-mimicking phan¬toms and human in-vivo experiments. The results demonstrate the ability of the method to classify and quantify tumor and tissue characteristics. Finally, we describe the method to calculate Multimodal Index for the malignancy risk assessment via tactile and multispectral imaging modalities and the risk probability based on the health records. / Electrical and Computer Engineering

The senseFly eBeePlus fixed-wing drone is a market available UAV compatible with a range of sensors that includes the Parrot Sequoia+ multispectral camera. Commercial applications of the drone predominantly focus on agriculture, environmental management, and engineering applications. The Sequoia 4-band multispectral sensor with bands optimised for plant health analysis, has a spectral range that coincides with the absorption features of iron. Previous studies with the use of hyperspectral sensors on multicopter UAVs have proven successful in the detection and delineation of hydroxides and sulphates associated with weathering of sulphides at the surface. This study aims to evaluate the ability of the eBeePlus drone equipped with a Parrot Sequoia+ sensor to effectively detect and delineate surficial sulphide mineral expressions by testing its capability on a known nickel-copper mineralisation occurrence at Illukunnguaq, on the north-western coast of Disko Island, West Greenland. Formally hosting a 28-tonne nickeliferous pyrrhotite massive sulphide boulder, many companies have sought this region for a possible extension of the mineralisation or another local mineral occurrence. Iron-feature band ratios and Spectral Angle Mapping (SAM) are two methods tested to first characterise the known occurrence, then search the wider region for other features with a similar signature as the Illukunnguaq dyke. To assist the evaluation and fine tune the Sequoia sensor, it will be compared against the trialled and trusted Rikola hyperspectral sensor, proven to map iron features. In addition, eigen maxima as one of many geomorphological indices that utilise the co-product Digital Surface Model (DSM) of the spectral survey, is employed to assess whether the Illukunnguaq dyke and other features are structurally mappable.  Results show that the Sequoia multispectral sensor, albeit less spectrally resolved than the Rikola hyperspectral sensor was able to detect surficial sulphide mineral expressions both by applying iron-feature band ratios and SAM. The latter was performed using laboratory measured and open-access library spectra. To fine-tune the tools compatible with the Sequoia sensor, in-depth investigations into iron-feature band ratio index values and best-fit library spectra for SAM was conducted. Confidence was increased by the blind detection of another known exposure and permitted a regional search to find additional features with spectral similarities to the Illukunnguaq dyke for future ground truthing. This study demonstrates that the eBeePlus drone can be used for mineral exploration when iron-sulphides are a part of the mineral system and outcropping at the surface. Leading field programs with detailed multispectral mapping can improve the efficiency of geologists by generating or verifying targets prior to ‘boots-on-the-ground’ geological sampling or mapping.