Fuzzy discrimination of MCC and fat in breast transmissive ultrasound CT images

So far, hardly ever has breast cancer been diagnosed in its early stages 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 equipment 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 transmissive ultrasound tomography stems from two major parameters; propagation time and attenuation of the wave inside different tissues. These values are also proportional to the age of the patient. By using only one of these parameters, the tissue can hardly be characterised accurately. A rule-based fuzzy criterion 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