Characterisation of fat and malignancy in transmissive ultrasound breast tomographs applying fuzzy logic

Early diagnosis of breast cancer has been often suffering from low accuracy of the images of different modalities using non-invasive detectors. Application of ultrasound as a safe imaging system has also failed due to the similar reflectivity indices of fat and malignant tissue. Here a new system called Intelligent Ultrasound Computed Tomography System (IUCTS) incorporating application of fuzzy decision making to a transmissive ultrasound tomography machine has been introduced. The diagnostic information in this mode stems from two major parameters; propagation time and attenuation of the wave inside different tissues. These values also changes with age of the patient. By using only one of these parameters, hardly the tissue can be characterised accurately. A rule-based fuzzy decision making algorithm nicely exploits all the variables in provision of an intelligent system. The Gaussian membership functions were estimated using images of a number of patient breasts. Remarkable diagnostic results with high accuracy were achieved by implementation of the system