A d = 5cm, Y = 25deg Ad = 10cm, Y = 25deg - - A d = 5cm, Y = 50deg - ■ A d = 10cm, Y = 50deg 5 4 3 2 1 0 0 200 400 600 800 1000 1200 d (cm]
Figure 5.1: Radius of a sphere which results in the same volume as the spherical cell for typical EchoScope parameters
segments where the sphere is centred on the optical centre of the camera) of a size dependent on the range to the visited point in contrast to the filter with fixed filter size which searches for neighbouring points in a sphere centred on the voxel itself with fixed radius. Figure 5.1 shows the radius of a sphere which would result in the same volume as a spherical cell as a function of the range distance to the point for some typical EchoScope parameters.
5.1.3
P re-p rocessin g on neighbouring points
The pre-processing applied to the neighbouring points is the same for the 3D filter with fixed and range dependent filter size. In both cases, if less than a specified minimum number of neighbours are identified the intensity value of the output data point is set to zero and hence removes spurious noise, otherwise the intensity of the associated output point at the position of the visited point I (Pi) is determined by the average intensity value of the intensity values of the visited point and its
neighbours,
= {
1/nT.IiPn.p,), n >" z •
(5.3)
The output is a pre-processed intensity image. It should be noted th at the associ ated range values are not altered, but spurious points are removed by setting the associated intensity values to zero.
5.1.4
C onclusions
The described pre-processing method not only removes outlier points which are at a far distance from the expected structure by using the minimum number of neighbours threshold, but at the same time increases the intensity values of points which are likely to belong to the expected structure and which originally had low intensity values, for example due to speckle noise or other defects, by averaging the intensity values of the point and its neighbours. Simple averaging has been chosen in favour of other methods, such as median filtering, due to the speed of computation.
The approach described differs from conventional 3D convolution filters in that it does not require the 3D data to be given as a “packed” voxel space (volumetric images described by 3D arrays), which is the case for many medical image processing applications (e.g. data from MRI, CT). The described 3D filter can cope with 3D data of different nature including “packed” voxel space data, ordered and unordered 2D arrays of 3D data points. The method described is therefore more fiexible and can be more efficient for incomplete 3D data as produced by the 3D acoustic camera. Section 8.1 describes pre-processing tests th at have been performed on synthetic
_______________________CHAPTER 5. 3D ACOUSTIC IMAGE PROCESSING
data. These tests show the influence of the parameters on the pre-processing results and it is shown th a t the choice of the parameters requires a trade-off between minimising the number of false negatives and positives. In order to achieve a small number of false negatives a small number of minimum number of points threshold is required and in order to achieve a small number of false positives the opposite is true. The choice of the filter cell size is more critical with respect to the number of false negatives, where a large value is advisable, whereas the number of false positives is not aflfected very much as spurious points are likely to be removed already due to their low intensity values in the original acoustic data.
5.2
Image segm entation
The segmentation of an image is an essential step prior to any model-based ob ject recognition process [PG98]. The main aim of the segmentation step in this thesis is to partition the image into regions representing objects and background. Often a simple thresholding segmentation of the acoustic intensity image is suffi cient, but other more sophisticated segmentation methods could be employed at added computational cost. Segmentation methods which have been considered and tested include segmentation methods based on thresholding, fuzzy k-means cluster ing, connected components and Markov random fields. These methods are briefly described here.
5.2.1
T h resh olding segm en tation
Simple thresholding segmentation transforms an intensity image I into a binary segmented image S by labelling every point which has an acoustic intensity above a certain threshold intthresh as an object point (1) and those below the threshold as a background point (0),
»'•••>-{?: î f c . l s i t r ■
M
Figure 5.2 shows a two-dimensional slice of the 3D acoustic intensity image for a sin gle point source at 5m distance using the standard settings for a 300kHz and 25deg viewing angle operation of the EchoScope acoustic camera (figure 4.15), which has been calculated using the processing steps for the 3D acoustic imaging simulation described in chapter 4.3.2. This figure represents the two-dimensional directional sensitivity of an acoustic imaging device. It can be seen th at the sensitivity is largest in the beam direction (main lobe), and several smaller peaks (side lobes) can be seen off the beam direction. An indication for a minimum threshold level is given by the sidelobe level which is generally known for a particular imaging system. Also the threshold selection can be guided by inspecting the intensity level histogram. Section 8.2.1 describes synthetic data tests for the thresholding seg mentation and from the quantitative and qualitative results it can be seen th at for increasing threshold values the number of object points decreases, resulting in an increase of false negatives and a decrease of false positives. Segmentation tests on real data are demonstrated in section 9.
The simple thresholding segmentation which has only one control parameter,
CHAPTER 5. 3D ACOUSTIC IMAGE PROCESSING