**5.1 Fracture detection**

**Figure 5** shows an example of how to identify an artificial defect on a human femur. The defect is through an incision at the end of the sample whose width is reduced in depth. Three heights are analyzed: one (H1) in the very fine portion of the crack, a second (H2) in the intermediate zone, and the third (H3) in the very open

*Automatic Noise Reduction in Ultrasonic Computed Tomography Image for Adult Bone Fracture… DOI: http://dx.doi.org/10.5772/intechopen.101714*

**Figure 5.** *Adult bone defect.*

upper zone. We can see in **Figures 6** and **7** that the implemented Haar wavelet detects the external edge. Hence, it is very clear, but the fracture is not detected and the problem still exists. It is because of the noise, the inhomogeneity of pixel intensities and the difficulty of separating anatomic regions. We do not have any idea about the intensity of the three regions. The problem is how to detect the fracture and how to obtain the different regions of interest. For this fact, a hybrid algorithm is proposed in the second step.

An example of the application of the k-means algorithm in the identification of a bone defect is discussed as an interesting breakthrough work of USCT bone imaging. Despite the ability of automatic fracture detection with k-means, as shown in **Figure 8** (a), (b) and (c), the results are not sufficient because we need to have an excellent quality of images and to extract the background that noises the ultrasonic image. With k-means, we detect regions and fractures. We need to extract the background and detect the edges. For this fact, we apply the morphologic algorithm. As shown in **Figure 9** (d), (e) and (f), with the morphologic algorithm, edges are well detected and we can calculate the distances with more precision. We can clearly distinguish the different zones, especially in the H1 part where the rack is not very open. The PSNRs

### *Biosignal Processing*

#### **Figure 7.**

*External edge detection by wavelet transformation: (a) LL: PSNR = 16.18, (b) LH: PSNR = 24.65, (c) HL: PSNR = 25.15, (d) HH: PSNR = 22.98.*

#### **Figure 8.**

*Denoised USCT image with k-means algorithm: (a) adult bone USCT image, (b) segmented USCT adult bone image with the k-means algorithm, k = 3, (c) segmented USCT adult bone image with the k-means algorithm, k = 2.*

are (H1) 13.08, (H2) 13.11 and (H3) 13.14, respectively. As seen in **Figure 10 (g)**, **(h)** and **(i)**, due to the application of the Otsu method, we obtain the image histogram value. After that, we extract the background. Finally, we obtain every region

*Automatic Noise Reduction in Ultrasonic Computed Tomography Image for Adult Bone Fracture… DOI: http://dx.doi.org/10.5772/intechopen.101714*

#### **Figure 9.**

*Denoised USCT with k-means algorithm combined with the morphologic algorithm: (d) H1 fracture defect detection in USCT image, (e) H2 fracture defect detection in USCT image, (f) H3 fracture defect detection in USCT image.*

#### **Figure 10.**

*Denoised USCT with k-means algorithm combined with morphologic algorithm and Otsu algorithms: (g) cortical bone detection, (h) cortical bone detection with enhanced PSNR, (i) medullary cavity detection.*

independent of the other. We are interested in the internal region which is the medullary cavity with a diameter of 0.8 cm. These results are similar to the reality measures where the cortical bone width D2 is about 3 mm, as given in **Figure 8 (b)** and the cancellous bone represents a D3 width of 1 mm as depicted in **Figure 9(d)**.

#### **5.2 PSNR results**

The PSNR results provided in **Table 1** demonstrate the best result with a value of 25.15 with an implemented Haar wavelet. However, a value of 13.08 is recorded with k-means combined with the morphologic algorithms and Otsu method. In general, image areas with higher PSNR and CNR estimates indicate having better contrast resolution [23]. The PSNR is important as it is a good measure of image quality. In detecting lesions in the body, however, a high PSNR alone will not guarantee that sufficient contrast exists to make the lesion detectable. The CNR between the lesion and background is important as it serves as a quantitative metric for low-contrast lesion detection: The higher the CNR between the lesion and the background, the more likely the lesion detection [24, 25].

#### **5.3 Time process**

As shown in **Figure 11**, the time execution process is about 33 s while time execution with k-means combined with the Otsu and morphologic algorithms is about 1.8 s.


**Table 1.** *PSNR results.*

**Figure 11.** *Time process.*
