## 3 Results  
### Semi-Automated Breast Lesion Detection Based on Persistence  
The Wavelia MBI algorithm for morphological breast lesion detection based on persistence has been specified in the previous section. The lesion persistence is assessed over a set of MBI images that were generated under varying assumptions on the dielectric properties of the healthy tissue of the breast (varying pc_fib parameter search ranges). Lesions that are morphologically detected in at least 3 out of the 5 pc_fib search ranges under evaluation are considered persistent and validated. The principle of the breast lesion detection method is illustrated in Figures 5-8 on two indicative patient test cases.  
* Patient 032: Group-1: 54-years old patient with an Invasive Lobular Carcinoma (ILC) of size 30 mm (MRI data) at the 12 o'clock position of the Right Breast. Breast density: BIRADS Category c, Volumetric Breast Density (VBD) = 13.3%.
* Patient 031: Group 3: 38-years old patient with a Fibroadenoma of size 19 mm (Ultra-sound) in the lateral Left Breast. Breast density: BIRADS Category c, VBD = 10.8%.  
The achieved persistence level of each breast lesion on MBI may vary depending on the histological type of the lesion and the density of the breast. It is interesting to note that the 30 mm ILC of Patient 032, which was not clearly visible on both mammogram and ultrasound, was persistent at 60% (i.e., 3 out of the 5 raw MBI images formed with the employment of distinct pc_fib search ranges) with MBI. More than a single dominant ROIs were visible on the raw MBI images. In Figure 6, the ROI which was extracted and  
Figure 4: Average confusion matrices for the 2 trained classifiers, estimated with 10-fold cross-validation, (**a**) NB classifier, (**b**) QDA classifier, (**c**) the training dataset and the decision hypersurfaces of the 2 classifiers, (**d**) Partitioning of the 3-d feature space illustrated with 3 cuts for the QDA classifier.  
validated in terms of morphological properties, sufficient intensity contrast against the other competing ROIs in each image and persistence over varying pc_fib search ranges is presented encircled in the 3 out of the 5 raw MBI images in which it was detectable. As illustrated in Figures 7 and 8, the fibroadenoma of Patient 031 was persistent at 100% (i.e., 5 out of the 5 raw MBI images formed with the employment of distinct pc_fib search ranges) and was predominantly visible with MBI. Both patients had comparably dense breasts (P032: VBD = 13.3%, P031, VBD = 10.8%); however, the difference in terms of consistency of the two lesions may have been the principal reason for the distinct level of persistence of the response of the two lesions to MBI. The MBI scan datasets for the two patients have been processed using the same configuration of the Wavelia QIF.  
In future upgraded implementations of the Wavelia QIF, both the persistence level over the varying assumption of the dielectric properties of the healthy tissue in the breast (varying pc_fib search ranges), but also the presence of a single dominant ROI or various competing ROIs in the image, may serve to define a confidence level for each MBI lesion detection, to better support the diagnosis.  
Figure 5: Patient 032, ILC in the Right Breast: (**a**) Bilateral mammogram Craino-Caudal (CC) view, (**b**) Bilateral mammogram Medio-Lateral Oblique (MLO) view, (**c**) Ultrasound scan, Right Breast, (**d**) MRI scan, bilateral axial image and sagittal image of the Right breast (**e**) MBI test results, Right Breast.  
Figure 6: Patient 032, Right breast: ILC morphological detection based on persistence, with Wavelia #1 MBI.  
Figure 7: Patient 031, Fib broadenoma in the Left Breast: (**a**) Bilateral mammogram Cranio-Caudal (CC) view, (**b**) Bilateral mammogram Medio-Lateral Oblique (MLO) view, (**c**) Ultrasound scan, Left Breast, (**d**) MBI test results, Left Breast.