**6.1 Comparative study with related work**

A comparative study with the state of the art is depicted in **Table 2**. Our implemented Haar wavelet shows a PSNR enhancement of 7.27 dB compared to the original image. Compared to that found by [2, 5], we have gained an enhancement with a result of 43%, outperforming the related work. In the discrete wavelet transform, one approximation can be further split, as it will remove the noise from images, but for higher noise, it will lose the details and create irregularities in the edges [26, 27]. In this context, we have used the 2D Haar wavelet decomposition to detect osteopathologies, we have to detect edges for distance measurements between bones. As a result, we have improved the PSNR and we have detected external edges, but the *Automatic Noise Reduction in Ultrasonic Computed Tomography Image for Adult Bone Fracture… DOI: http://dx.doi.org/10.5772/intechopen.101714*


#### **Table 2.**

*Comparative study with related works.*

fracture has not been detected. With our hybrid proposed algorithms, we have detected it from the bone image. Consequently, we have outperformed [2, 5] detecting pathologies from USCT images. In 2019 [3], the authors achieved a USCT PSNR value of 21.17 dB. We outperform them with a PSNR enhancement value of 4 dB. In addition, our denoised images represent a high USCT image quality and we have produced a free USCT database, given the difficulty to have a big data of USCT images.

#### *6.1.1 Edge detection*

If we assimilate the method of the Haar wavelet transform with visual C++ with the sliding window algorithm using MATLB [11, 12], we can say that we have made a net automatic edge detection and we have improved the resolution. The increase in the USCT imaging process utilizing only signal processing cannot be isolated. The way to enhance the resolution of the image [5], we can detect some bone abnormalities. First, the associated signals are treated before processing the image reconstruction. Afterward, the automatic image processing tool and precision are used. This will be the right way for the method perspective.

#### *6.1.2 Time execution with visual C++ and python*

We can say that we have reduced the time of execution of the image to 1.8 seconds. The implemented Haar wavelet algorithm is faster than the one proposed by Lasaygues in 2006. Comparing our time execution with the run time using Matlab. We have saved and gained with about 5.45 times. However, with k-means, Python runtime is about 1.8 seconds. Thus, our proposed k-means algorithm is the best method to save time compared to that found with the algorithm of the Haar wavelet. On the other hand, we must think of a deep learning algorithm that combines the neural network and the genetic algorithm to help accelerate the time execution by its implementation on a Graphic Processor Unit (GPU) system.

#### *6.1.3 Region detection*

Comparing our work done with the suggested algorithm and the method of classification with the Laboratory of Mechanic and Acousttics (LMA) work [5, 12] done with the sliding window, the present error to determine the bone cortical area is roughly between 4% and 20%. It is very high. However, our proposed algorithm aims to detect different regions of interest, as depicted in **Figures 9** and **10**.

## *6.1.4 Results validations*

A comparative study with ground truth: To validate our results, a comparative study has been done with an echographic image of human bones. As illustrated in **Figure 12**, multiple structures of an ultrasonic bone have not been detected because of the issue of echographic frequencies to perforate bones.

In the ground truth, the used bone to be imaged is a real adult Padilla bone with a fracture of three levels (H1, H2 and H3) as depicted in **Figure 5**. The lengths of these fracture diameters are approximately similar to those lengths found with our proposed algorithm. Moreover, the width of the cancellous and cortical bone as well as the diameter of the medullary cavity is similar to the ground truth width with an error of + 1 mm. Indeed, this similarity of diameter measurements indicates the performance of our used method.

Our implemented Haar wavelet using the FMC interface and Visual C++ can be applied to various tomographic images, such as cerebral computed tomography (CT) and retinal vessel computed tomography for edge and pathology detection. **Figure 13** shows the performance of our implemented Haar wavelet algorithm.

Our k-means combined with the morphologic and the Otsu algorithms have resolved the problem of adult bone region detection and outperformed the wavelet

**Figure 13.** *Vessels retinal detection in CT image.*

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

signal processing method [2, 5]. Furthermore, it has detected osteopathologies such as fractures with its highest ability to remove noise and save time.

## **7. Conclusion**

In this chapter, an improved hardware/software design has been used. In the beginning, we have presented the USCT prototype to avoid any x-ray exposure to human beings. Thus, we have had USCT noisy images. As a solution, we have implemented the Haar wavelet and improved k-means combined with the morphologic and Otsu algorithms. It is a very crucial method that has solved the problem of noise in USCT images. Therefore, with this approach, the resolution has been improved, the time execution has been reduced, the noise has been removed and we have gained automatic diagnosis detection. In fact, we have to say that compared to what has been already published, the method presents more resolved results. With unsupervised developed k-means, the USCT image is segmented into three anatomic regions of interest, namely, the cortical and cancellous bone and the medullary cavity. These regions will be the labeled features of bone tomographic images. The next step will be devoted to the combination of unsupervised deep learning and supervised deep learning to automatically classify our image data into two classes (pathologic bone images and healthy bone images) and for the extraction of the region of interest from big data of bone tomographic images. Besides, interest will be given to the reconstruction of 3D images.

### **Funding**

This study was funded by the Ministry of High Education and Scientific Research in Tunisia and the Laboratory of Mechanics and Acoustics in Marseille.

### **Conflict of interest**

Authors declare that they have no conflict of interest.

### **Ethical Approval**

This chapter does not contain any studies with human participants performed by any of the authors.

### **Informed consent**

This chapter does not contain patient data.
