**3.1 Data acquisition**

The data can be obtained from any ultrasound scanning systems that are presented in Section 2, such as sensorless system, electromagnetic tracking system, and optical tracking system. The obtained data are the 2D ultrasound frames and the orientation and position of the tracking sensor when a particular frame was taken. The B-scan image and its relative orientation and position must be synchronized [11]. As for the real-time system, there is a need to synchronize the image captured, position and orientation, and the time [17]. This synchronization process is also known as the temporal calibration [18].

Next, ultrasound probe calibration or spatial calibration is used to get the homogeneous transformation to convert each 2D coordinate pixel in 2D ultrasound frames into 3D coordinates voxel of ultrasound probe frame [12, 19, 20]. This method is used mostly in the real-time ultrasound 3D reconstruction system [19, 21].

**79**

**Figure 5.**

*A Survey on 3D Ultrasound Reconstruction Techniques DOI: http://dx.doi.org/10.5772/intechopen.81628*

**3.2 Data preprocessing**

Scan conversion is also important for the reconstruction and visualization processes later, because of the possibility of different coordinate systems used by the scanning devices, such as in the work of [22], where the polar coordinate system recorded by the tracking system is converted into Cartesian coordinate system for 3D reconstruction. Besides that, reference [11] provides a method for the conversion of quaternion-based coordinate system into Cartesian coordinate system.

After the acquisition of the data, the data such as the 2D ultrasound frames are sent to the workstation for further processing. Most of the image processing techniques are used during this step, in order to enhance the 2D frames quality, remove noise, and preserve the edge boundary. This is because the 2D frames have various types of noise and artifacts, such as speckle noise, refraction, shadowing, reverberation, etc., and the spatial resolution within a 2D ultrasound frame is not uniform due to the transducer and signal characteristics varies with the penetration depth [7]. The example of image enhancement techniques included noise removing

technique, histogram equalization, 2D Gaussian filter, median filtering, etc.

because of the high contrast between these two [6].

**3.3 Volume reconstruction**

**Figure 5** shows the noise and artifacts found in the 2D ultrasound frame. Besides that, segmentation process is also important to distinguish between the scanned objects in a region of interest (ROI), such as the skin, bone structure, etc., before the volume can be calculated. There are three types of segmentation process, which are automatic segmentation algorithms, semiautomatic segmentation algorithm, and manual segmentation. Automatic segmentation proves to be effective in obstetrics as the boundary of fetus and surrounding amniotic fluid is easy to be detected

The volume reconstruction methods are the most important part in the 3D ultrasound reconstruction process, which involved the implementation of interpolation and approximation algorithm to get the 3D volume data and put them in a 3D volume grid based on the spatial information acquired from the tracking system. The volume reconstruction also aims to reduce computational requirements without damaging or losing the underlying shape of the data [18]. Before the volume reconstruction methods start, the coordinate system and volume grid of reconstructed volume need to be established, such as volume size, axes of volume, origin of axes, and the size of voxel [23]. The volume coordinate configuration uses principal component analysis (PCA), which is a statistical tool that estimates the largest difference of collected data

*Noise and artifacts [7]: (a) speckle noise; (b) transducer malfunction, and (c) elevational focal zone.*

**Figure 4.** *The 3D ultrasound reconstruction process.*

Scan conversion is also important for the reconstruction and visualization processes later, because of the possibility of different coordinate systems used by the scanning devices, such as in the work of [22], where the polar coordinate system recorded by the tracking system is converted into Cartesian coordinate system for 3D reconstruction. Besides that, reference [11] provides a method for the conversion of quaternion-based coordinate system into Cartesian coordinate system.

## **3.2 Data preprocessing**

*Artificial Intelligence - Applications in Medicine and Biology*

**3. 3D ultrasound reconstruction process**

known as the temporal calibration [18].

**3.1 Data acquisition**

freehand data without any limitation on the trajectory [15]. In recent study, the image-based algorithm makes use of the adaptive speckle decorrelation to learn relative position and orientation between the acquired 2D ultrasound image pairs [16]. Since the sensorless freehand ultrasound does not need any position tracking sensor, it is considered the most portable 3D freehand ultrasound system [16]. However, the inconsistency scan rate and angle can cause the reconstruction result to be not smooth and also results in less quality 3D image during 3D visualization step [14].

This section explained the 3D ultrasound reconstruction process in detail. This process is achieved from the use of 2D ultrasound probe with linear array. Based on [6], the standard workflow of the 3D ultrasound reconstruction is data acquisition stage, data preprocessing stage, volume reconstruction method stage, and 3D visualization stage. **Figure 4** shows the overall process of 3D ultrasound reconstruction.

The data can be obtained from any ultrasound scanning systems that are presented in Section 2, such as sensorless system, electromagnetic tracking system, and optical tracking system. The obtained data are the 2D ultrasound frames and the orientation and position of the tracking sensor when a particular frame was taken. The B-scan image and its relative orientation and position must be synchronized [11]. As for the real-time system, there is a need to synchronize the image captured, position and orientation, and the time [17]. This synchronization process is also

Next, ultrasound probe calibration or spatial calibration is used to get the homogeneous transformation to convert each 2D coordinate pixel in 2D ultrasound frames into 3D coordinates voxel of ultrasound probe frame [12, 19, 20]. This method is used mostly in the real-time ultrasound 3D reconstruction system [19, 21].

**78**

**Figure 4.**

*The 3D ultrasound reconstruction process.*

After the acquisition of the data, the data such as the 2D ultrasound frames are sent to the workstation for further processing. Most of the image processing techniques are used during this step, in order to enhance the 2D frames quality, remove noise, and preserve the edge boundary. This is because the 2D frames have various types of noise and artifacts, such as speckle noise, refraction, shadowing, reverberation, etc., and the spatial resolution within a 2D ultrasound frame is not uniform due to the transducer and signal characteristics varies with the penetration depth [7]. The example of image enhancement techniques included noise removing technique, histogram equalization, 2D Gaussian filter, median filtering, etc.

**Figure 5** shows the noise and artifacts found in the 2D ultrasound frame. Besides that, segmentation process is also important to distinguish between the scanned objects in a region of interest (ROI), such as the skin, bone structure, etc., before the volume can be calculated. There are three types of segmentation process, which are automatic segmentation algorithms, semiautomatic segmentation algorithm, and manual segmentation. Automatic segmentation proves to be effective in obstetrics as the boundary of fetus and surrounding amniotic fluid is easy to be detected because of the high contrast between these two [6].
