Rapid assessment of tissue biopsies is a critical issue in modern histopathology. For breast cancer diagnosis, the shape of the nuclei and the architectural pattern of the tissue are evaluated under high and low magnifications, respectively. In this study, we focus on the development of a pattern classification system for the assessment of breast cancer images captured under low magnification (×10). Sixty-five regions of interest were selected from 60 images of breast cancer tissue sections. Texture analysis provided 30 textural features per image. Three different pattern recognition algorithms were employed (kNN, SVM, and PNN) for classifying the images into three malignancy grades: I–III. The classifiers were validated with leave-one-out (training) and cross-validation (testing) modes. The average discrimination efficiency of the kNN, SVM, and PNN classifiers in the training mode was close to 97%, 95%, and 97%, respectively, whereas in the test mode, the average classification accuracy achieved was 86%, 85%, and 90%, respectively. Assessment of breast cancer tissue sections could be applied in complex large-scale images using textural features and pattern classifiers. The proposed technique provides several benefits, such as speed of analysis and automation, and could potentially replace the laborious task of visual examination.

The development of contemporary complex technological systems prerequisites interdisciplinary design and simulation methodologies. Such an approach is demonstrated in the present work. Toward manufacturing a hand-held device aiming at internal body temperature measurements using passive microwave radiometer technology, five design and simulation perspectives are elaborated. The proposed system consists of an ultra-wide-band microwave compact antenna, a multi-frequency microwave radiometer, and a digital processing unit, all enclosed in a portable arrangement. A modeling and visualization software, processes acquired measurements according to a predefined model of human breast. The system's concept of operation is based on the fact that a malignant tumor turns out to local temperature increase inside the tissue. By measuring this temperature in successive depths, using different frequency bands in the region of 1-4 GHz, as well as in nearby spatially arranged spots on the human tissue surface, 2-D and 3-D imaging of the temperature distribution are realized. This paper focuses on design and simulation approaches of all system's aspects.