## 3 Experiment
### Experimental Results  
We used phantom (I) for imaging toward verifying that the images obtained using the MICF-DMAS algorithm on a homogeneous phantom had less clutter. Phantom (I) was a homogeneous phantom consisting of a single medium, and the tumor, measuring 10 mm in diameter, was situated at coordinates (17 mm, \(-\)23 mm). Figure 11 shows the imaging results obtained for phantom (I) using the four algorithms DMAS, CF-DAS, CF-DMAS, and MICF-DMAS. All the images were normalized based on the maximum intensity value in the reconstruction plane, with yellow representing the highest value and blue representing the lowest. The images obtained using the DMAS and CF-DAS algorithms contain more low-intensity clutter. There is reduced clutter in the image obtained by the CF-DMAS algorithm, but some clutter is still present. There is almost no clutter in the image obtained using the MICF-DMAS algorithm. Table 3 presents the SCR, CR, \(X\), and \(D\) values. SCR and CR were used to assess the level of clutter in the image and the contrast between the tumor and normal tissue. A higher SCR value indicates reduced clutter and improved imaging clarity, while a higher CR value suggests an increased image contrast ratio. Compared with the other three algorithms, the MICF-DMAS algorithm improved the SCR by at least 4 dB and the CR at least four-fold. Similar coordinates \(X\) for the point of maximum intensity in the images were obtained using the four algorithms. \(D\) represented the diameter of the tumor. The DMAS, CF-DAS, CF-DMAS, and MICF-DMAS algorithms yielded images with \(D\) of 11.3 mm, 10.7 mm, 9.6 mm, and 8.1 mm. The diameter of the tumor obtained by the MICF-DMAS algorithm was smaller compared to the true value (10 mm). The reason is that the MICF-DMAS algorithm suppressed the intensity of the focus near the tumor. Figure 10: (**a**) Dielectric constants and (**b**) conductivity of tumor and normal tissue in phantom (I). (**c**) Dielectric constants and (**d**) conductivity of tumor, fat, and skin in phantoms (II) and (III). We used phantom (II) for imaging toward verifying that the MICF-DMAS algorithm minimized clutter when imaging individual tumors in a heterogeneous model that more closely resembled a real breast. Phantom (II) was a non-homogeneous model consisting of three layers of skin, fat, and tumor, and the tumor, measuring 10 mm in diameter, was situated at coordinates (17 mm, \(-\)23 mm) with skin thickness of 9-12 mm. Figure 12 shows the imaging results obtained for phantom (II) using the four algorithms. Due to the inhomogeneous media and skin thickness, the images of Figure 12 show more clutter compared to those of Figure 11 in addition to a larger tumor localization error of about 6 mm. The results obtained using the DMAS and CF-DAS algorithms are depicted in Figure 12a,b, showing that the images contain a large amount of clutter. Notably, there is high-intensity clutter at coordinates (\(-\)23 mm, \(-\)20 mm) that could be mistaken for a tumor. As can be seen in Figure 12c, there is less clutter in the image obtained by the CF-DMAS algorithm, but the high-intensity clutter at (\(-\)23 mm, \(-\)20 mm) is still present. Figure 12d shows the image obtained using the MICF-DMAS algorithm, clearly showing a tumor, and the intensity and instances of clutter are reduced. The high-intensity area around the tumor is minimal. The high-intensity clutter located at (\(-\)23 mm, \(-\)20 mm) was also suppressed to a low intensity. Table 4 presents the SCR and CR of the images acquired  
Figure 11: Imaging results of the four algorithms for the homogeneous single-tumor phantom (I) (Z = 45 mm). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & \(D\) **(mm)** & \(X\) **(mm)** & **SCR (dB)** & **CR** \\ \hline MICF-DMAS & 8.1 & (18,\(-\)25) & 14.72 & 23.14 \\ CF-DAS & 10.7 & (18,\(-\)25) & 5.52 & 11.46 \\ DMAS & 11.3 & (19,\(-\)24) & 6.09 & 11.92 \\ CF-DMAS & 9.6 & (19,\(-\)25) & 10.61 & 17.68 \\ \hline \hline \end{tabular}
\end{table}