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7.5.3 Discussion About 3-D Versus 2-D Strain Methods
Two sets of protocols were used for our experiments with the datasets. One used the
3-D ultrasound acquisition method, and the other used the 2-D datasets acquired
using a conventional 2-D method. The 2-D strain imaging method means that only
one precompressed image slice and one postcompressed image slice were used for
feature analysis. These two slices were selected from the middle slice of the
precompressed and postcompressed images that contained tumor information.
Because the current system utilizes only one slice in each condition (precompressed
or postcompressed), it may lack statistical significance, thereby yielding errors.
Therefore, according to the ROC curves analysis in Figure 7.16, the A
z
values for all
proposed strain features for the 3-D strain method are superior to 2-D method. This
A
z
value also shows the robustness of the 3-D strain method.
Because the 2-D strain image method is low in spatial content due to only one
slice being used, it suffers from a lack of features. Therefore, all performance values
in Table 7.3 indicate that the 3-D strain image analysis is better than the 2-D
method. To test and verify the validity of this opinion, a statistical tool, the
Chi-square test, was used. Hence, the robustness of the 3-D strain image method can
be proven once more according to the lower p-values, especially in accuracy and
specificity.
7.6 Conclusion
In this chapter, the level set segmentation approach was adopted to extract contour
information about lesion. This information was then used for 3-D strain image anal-
yses. According to the experiment results, the 3-D strain image analysis method can
provide more robust and accurate results than the 2-D strain analysis method in
which only two images are used. Four strain features—volume difference, shape dif-
ference, contour difference and shift distance—were proposed to analyze the strain
characteristics of the lesions under the pressure of a probe. From the experimental
results, the combinationof thefour 3-Dstrain featureswas better than the one of the
2-D strain features.
The SVM experimental results show that the accuracy of our proposed system is
91%, and the sensitivity of our diagnosis system is 90%. That is, if a tumor is malig-
nant, the proposed CAD system can detect it with high probability. This CAD sys-
tem will offer an efficient and accurate diagnosis for physicians to help reduce
instance of misdiagnosis and the requirement for a biopsy. Hence, this system is
expected to be a useful CAD tool for physicians trying to discriminate between
benign and malignant lesions.
The main problem of dealing with 3-D strain images is how to register the
precompressed and postcompressed lesions efficiently and accurately. Hence,
other more advanced 3-D alignment methods are worth investigating and using in
this system to further improve its performance. Furthermore, reducing the
misdiagnosis rate and combining the system with other cancer diagnosis tools to
create a more robust CAD system are new challenges that should be addressed in
future work.
184 3-D Breast Ultrasound Strain Imaging
References
[1] American Cancer Society, Cancer Facts and Figures 2004, 2004.
[2] Kolb, T. M., J. Lichy, and J. H. Newhouse, “Comparison of the Performance of Screening
Mammography, Physical Examination, and Breast US and Evaluation of Factors That
Influence Them: An Analysis of 27,825 Patient Evaluations,” Radiology, Vol. 225, No. 1,
October 2002, pp. 165–175.
[3] Chang, R. F., et al., “Computer-Aided Diagnosis for 2-D/3-D Breast Ultrasound,” in
Recent Advances in Breast Imaging, Mammography, and Computer-Aided Diagnosis of
Breast Cancer, J. S. Suri and R. M. Rangayyan, (eds.), Bellingham, WA:SPIE Press, 2006.
[4] Chen, D. R., R. F. Chang, and Y. L. Huang, “Breast Cancer Diagnosis Using Self-Organiz-
ing Map for Sonography,” Ultrasound Med. Biol., Vol. 26, No. 3, March 2000,
pp. 405–411.
[5] Chen, D. R., et al., “Diagnosis of Breast Tumors with Sonographic Texture Analysis Using
Wavelet Transform and Neural Networks,” Ultrasound Med. Biol., Vol. 28, No. 10, Octo-
ber 2002, pp. 1301–1310.
[6] Hamper, U. M., et al., “Three-Dimensional US: Preliminary Clinical Experience,” Radiol-
ogy, Vol. 191, No. 2, May 1994, pp. 397–401.
[7] Tong, S., et al., “Analysis of Linear, Area and Volume Distortion in 3-D Ultrasound Imag-
ing,” Ultrasound Med. Biol., Vol. 24, No. 3, March 1998, pp. 355–373.
[8] Fenster, A., and D. B. Downey, “3-D UltrasoundImaging: A Review,” IEEE Engineering in
Medicine and Biology Magazine, Vol. 15, No. 6, November 1996, pp. 41–51.
[9] Huang, S. F., et al., “Characterization of Spiculation on Ultrasound Lesions,” IEEE Trans.
on Med. Imaging, Vol. 23, No. 1, January 2004, pp. 111–121.
[10] Chen, D. R., et al., “Computer-Aided Diagnosis for 3-Dimensional Breast
Ultrasonography,” Arch. Surg., Vol. 138, No. 3, March 2003, pp. 296–302.
[11] Chen, W. M., et al., “Breast Cancer Diagnosis Using Three-Dimensional Ultrasound and
Pixel Relation Analysis,” Ultrasound Med. Biol., Vol. 29, No. 7, July 2003,
pp. 1027–1035.
[12] Suri, J. S., and R. M. Rangayyan, Recent Advances in Breast Imaging, Mammography and
Computer Aided Diagnosis of Breast Cancer, Bellingham, WA: SPIE Press, 2006.
[13] Chang, R. F., et al., “Improvement in Breast Tumor Discrimination by Support Vector
Machines and Speckle-Emphasis Texture Analysis,” Ultrasound Med. Biol., Vol. 29, No. 5,
May 2003, pp. 679–686.
[14] Kuo, W. J., et al., “Computer-Aided Diagnosis of Breast Tumors with Different US Sys-
tems,” Acad. Radiol., Vol. 9, No. 7, July 2002, pp. 793–799.
[15] Drukker, K., et al., “Computerized Detection and Classification of Cancer on Breast Ultra-
sound,” Acad. Radiol., Vol. 11, No. 5, May 2004, pp. 526–535.
[16] Horsch, K., et al., “Computerized Diagnosis of Breast Lesions on Ultrasound,” Med. Phys.,
Vol. 29, No. 2, February 2002, pp. 157–164.
[17] Drukker, K., et al., “Computerized Lesion Detection on Breast Ultrasound,” Med. Phys.,
Vol. 29, No. 7, July 2002, pp. 1438–1446.
[18] Horsch, K., et al., “Performance of Computer-Aided Diagnosis in the Interpretation of
Lesions on Breast Sonography,” Acad.Radiol., Vol. 11, No. 3,March 2004, pp. 272–280.
[19] Chen, D. R., et al., “Texture Analysis of Breast Tumors on Sonograms,” Semin. Ultrasound
CT MR, Vol. 21, No. 4, August 2000, pp. 308–316.
[20] Chen, D. R., R. F. Chang, and Y. L. Huang, “Computer-Aided Diagnosis Applied to US of
Solid Breast Nodules by Using Neural Networks,” Radiology, Vol. 213, No. 2, November
1999, pp. 407–412.
[21] Drukker, K., M. L. Giger, and E. B. Mendelson, “Computerized Analysis of Shadowing on
Breast Ultrasound for Improved Lesion Detection,” Med. Phys., Vol. 30, No. 7, July 2003,
pp. 1833–1842.
7.6 Conclusion 185
[22] Varghese, T., J. Ophir, and T. A. Krouskop, “Nonlinear Stress-Strain Relationships in Tis
-
sue and Their Effect on the Contrast-to-Noise Ratio in Elastograms,” Ultrasound Med.
Biol., Vol. 26, No. 5, June 2000, pp. 839–851.
[23] Doyley, M. M., et al., “A Freehand Elastographic Imaging Approach for Clinical Breast
Imaging: System Development and Performance Evaluation,” Ultrasound Med. Biol.,
Vol. 27, No. 10, October 2001, pp. 1347–1357.
[24] Garra, B. S., et al., “Elastography of Breast Lesions: Initial Clinical Results,” Radiology,
Vol. 202, No. 1, January 1997, pp. 79–86.
[25] Ophir, J., et al., “Elastography: A Quantitative Method for Imaging the Elasticity of Biolog-
ical Tissues,” Ultrason. Imaging, Vol. 13, No. 2, April 1991, pp. 111–134.
[26] Cespedes, I., et al., “Elastography: Elasticity Imaging Using Ultrasound with Application to
Muscle and Breast In Vivo,” Ultrason. Imaging, Vol. 15, No. 2, April 1993, pp. 73–88.
[27] Ophir, J., et al., “Elastography: Ultrasonic Estimation and Imaging of the Elastic Properties
of Tissues,” Proc. Inst. Mech. Eng. [H] J. Eng. Med., Vol. 213,No. 3, 1999, pp. 203–233.
[28] Moon, W. K., et al., “Solid Breast Masses: Classification with Computer-Aided Analysis of
Continuous US Images Obtained with Probe Compression,” Radiology, Vol. 236, No. 2,
August 2005, pp. 458–464.
[29] Suri, J. S., D. L. Wilson, and S. N. Laxminarayan, Handbook of Medical Image Analysis:
Segmentation Models, Vols. I, II, and III, Berlin: Springer, 2005.
[30] Rakotomamonjy, A., P. Deforge, and P. Marche, “Wavelet-Based Speckle Noise Reduction
in Ultrasound B-Scan Images,” Ultrason. Imaging, Vol. 22, No. 2, April 2000, pp. 73–94.
[31] Marom, E., S. Kresic-Juric, and L. Bergstein, “Speckle Noise in Bar-Code Scanning Sys-
tems—Power Spectral Density and SNR,” Appl. Opt., Vol. 42, No. 2, January 2003,
pp. 161–174.
[32] Tang, H., T. Zhuang, and E. X. Wu, “Realizations of Fast 2-D/3-D Image Filtering and
Enhancement,” IEEE Trans. on Med. Imaging, Vol. 20, No. 2, February 2001,
pp. 132–140.
[33] Rapp, C. L., “Sonography of the Breast,” Proceedings of Society of Diagnostic Medical
Sonography’s 17th Annual Conference, Dallas, TX, 2003, pp. 57–67.
[34] Osher, S., and J. Sethian, “Fronts Propagating with Curvature-Dependent Speed: Algo-
rithms Based on Hamilton-Jacobi Equations,” J. Comput. Phys., Vol. 79, No. 1, 1988,
pp. 12–49.
[35] Sethian, J. A., The Level Set Methods and Fast Marching Methods: Evolving Interfaces in
Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, 2nd
ed., Cambridge, U.K.: Cambridge University Press, 1999.
[36] Suri, J. S., and S. N. Laxminarayan, PDE and Level Sets: Algorithmic Approaches to Static
and Motion Imagery, New York: Kluwer Academic/Plenum Publishers, 2002.
[37] Suri, J. S., et al., “Modeling Segmentation viaGeometric DeformableRegularizers, PDE and
Level Sets in Still/Motion Imagery: A Revisit,” Int. J. Image and Graphics, Vol. 1, No. 4,
December 2001, pp. 681–734.
[38] Suri, J. S., et al., “Shape Recovery Algorithms Using Level Sets in 2-D/3-D Medical Imagery:
A State-of-the-Art Review,” IEEE Trans. on Inf. Technol. Biomed., Vol. 6, No. 1, March
2002, pp. 8–28.
[39] Malladi, R., J. A. Sethian, and B. C. Vemuri, “Shape Modeling with Front Propagation: A
Level Set Approach,” IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 17,
No. 2, February 1995, pp. 158–175.
[40] Suri, J. S., “Leaking Prevention in Fast Level Sets Using Fuzzy Models: An Application in
MR Brain,” Proc. IEEE–EMBS Int. Conf. Information Technology Applications in
Biomedicine, 2000, pp. 220–225.
[41] Richardson, I. E. G., H. 264 and MPEG-4 Video Compression: Video Coding for Next-
Generation Multimedia, New York: John Wiley & Sons, 2003.
186 3-D Breast Ultrasound Strain Imaging
[42] Pontil, M., and A. Verri, “Support Vector Machines for 3-D Object Recognition,” IEEE
Trans. on Patt. Anal. Mach. Intell., Vol. 20, No. 6, June 1998, pp. 637–646.
[43] Chapelle, O., P. Haffner, and V. N. Vapnik, “Support Vector Machines for Histo
-
gram-Based Image Classification,” IEEE Trans. on Neur. Networks, Vol. 10, No. 5, Sep
-
tember 1999, pp. 1055–1064.
[44] Vapnik, V. N., The Nature of Statistical Learning Theory, 2nd ed., New York:
Springer-Verlag, 1999.
[45] Chang, R. F., et al., “Support Vector Machines for Diagnosis of Breast Tumors on US
Images,” Acad. Radiol., Vol. 10, No. 2, February 2003, pp. 189–197.
[46] Besthorn, C., et al., “Discrimination of Alzheimer’s Disease and Normal Aging by EEG
Data,” Electroencephalogr. Clin. Neurophysiol., Vol. 103, No. 2, August 1997,
pp. 241–248.
[47] Cichocki, A., et al., “EEG Filtering Based on Blind Source Separation (BSS) for Early Detec-
tion of Alzheimer’s Disease,” Clin. Neurophysiol., Vol. 116, No. 3, March 2005,
pp. 729–737.
7.6 Conclusion 187
CHAPTER 8
Automatic Classification of Breast Lesions
in 3-D Ultrasound Images
Paulo S. Rodrigues, Gilson A. Giraldi, Marcia Provenzano, Marcelo D. Faria,
Ruey-Feng Chang, and Jasjit S. Suri
8.1 Introduction
Per the American Cancer Society [1], breast cancer ranks second in the list of
women’s cancers. Even though the rate of breast cancer has risen since 1980, the
mortality rates have declined since 1990. The reduction in mortality rate is due to
early detection and improvements in treatment.
With the advances in ultrasound acquisition techniques [2] and the digital
nature of 3-D volume acquisition, computer-aided diagnosis (CAD) has become
even more powerful, thereby improving the workflow of breast imaging and reduc-
ing the physician’s workload (see [3]). The process of breast lesion classification
using efficient CAD-based methods with ultrasound is generally categorized into
several techniques: Bootstrap [4], SVM [5], and neural networks [6, 7]. Along these
lines, Sawaki et al. [8] proposed a CAD system that uses fuzzy inference for breast
sonography, and they adopted six different criteria to classify lesions: lesion shape,
border, edge shadows, internal echoes, posterior echoes, and halo. However, their
system accuracy, sensitivity, and specificity were only 60.3%, 82.1%, and 42.9%,
respectively.
However, the texture framework in ultrasound image processing is also an
important tool for extracting interesting lesion features. Approaches underlining
texture go back more than two decades. In this respect, Garra et al. [9] analyzed
breast lesions in ultrasound images using a co-occurrence matrix to represent their
textures. Chen et al. [10] proposed an autocorrelation coefficient to analyze the tex-
ture information. Because their implementations were presented on a small set of
data, there is a fundamental weakness with texture-based strategies. The settings for
ultrasound machine parameters have to be fixed when acquiring ultrasound images.
If the ultrasound parameter settings changed, the CAD performance became very
unstable [11]. Moreover, a CAD system trained by images from one ultrasound
machine needs to be trained again for a different ultrasound machine due to differ-
ent image resolution and image quality. Hence, Chen et al. [12] proposed nearly
setting-independent features based on shape information. Their system was very
189
robust and powerful because the statistical data using ROC curves were all greater
than 0.95.
Note that all of these strategies were used on 2-D ultrasound images, which has
limitations. For example, it did not allow 3-D spatial processing during the ultra-
sound data acquisition process. As a result, the shape and structure information of
the breast lesions could not be reconstructed, and consequently it may be not possi-
ble to determine the growth of the cancer and its spatial relationships. Recently, 3-D
ultrasound [13] has shown promising signs that overcome the limitations of tradi-
tional 2-D ultrasound, allowing the anatomy to be viewed interactively in three
dimensions. Chen et al. [14] proposed a 3-D ultrasound texture features
autocorrelation matrix and run difference matrix. However, as pointed out earlier,
texture-based CAD is highly machine dependent and utilizes these settings all of the
time.
The main reasons for investigating automatic analysis are as follows. The auto-
matic diagnosis can assist new physicians in lesion investigation, and it can be used
for training studentsin thearea of medicine education because the results can be eas-
ily compared (manual with automatic ones). In addition, diagnostics can be carried
out in a relatively short time and with better precision. Finally, an automatic analy-
sis at a local computer or workstation generally is the first step before the develop-
ment of parallel or grid computing systems so that, in the future, it will seriously
diminish the computational overhead, allowing for more reliable and faster
analyses.
A totally automatic process, however, is still not a reality; so much work
remains, mainly in the early steps, which may involve segmentation, recognition,
and extraction of lesions from the background. Specifically speaking, in the case of
ultrasound images, the initial segmentation is a complex process because the images
have a low SNR, low resolution, low contrast, and several small and spurious
regions. Several other challenges in all steps remain. In particular, the initial segmen-
tation is a fundamental task because the successof the remaining steps dependson it.
Despite the number and size of the regions inside the ultrasound images, in most
cases the unique requirement is to extract the region of interest from its background.
Then, algorithms to binarize images may be sufficient. However, the lesion’s region
is difficult to define in terms of its boundary location and texture pattern. Generally,
the boundary represents a third (narrow) region that separates the inside from the
outside of the lesion’s region. This narrow boundary is generally composed of dead
cells that later move to the inside of the lesion, where, due to pressure instability,
they may be transformed to liquid material. These watery environments, more
related to benign than malignant lesions, tend to generate anechoic regions due to
liquid concentration. The contrary occurs with malignant regions. Then, the
anechoicity is one of the main factors that influences the lesion analysis. Other
important features are the presence of protuberances, circularity, homogeneity, and
lesion area.
Algorithms for image segmentation based only on binarization are not com-
pletely sensitive to a lesion’s pattern behavior and frequently fail to trace adequately
the lesion boundary. On the other hand, traditional algorithms such as fuzzy
c-means (FCM), k-means (KM), self-organized maps (SOMs), and Bootstrap can
also be used and generally give good results because they can achieve several clusters
190 Automatic Classification of Breast Lesions in 3-D Ultrasound Images
adequately. Nonetheless, they have a high computational time because they use
implementations with some kind of energy minimization, usually in an iterative
loop. Other algorithms, such as those based on the watershed approach, may also
generate several regions, but their disadvantage is the oversegmentation. Because
algorithms based on thresholding do not have such problems, they may be a good
alternative. However, the choice of the threshold is not always an easy task, because
thresholds are generally based on some kind of contrast enhancement between the
background and the foreground. In the narrow boundary around the lesion, the
threshold is not easy to compute, so these algorithms usually do not provide a good
boundary location.
Regarding the fact that patterns of ultrasound images are probability distribu-
tions of gray scales, the entropy associated with these distributions may be mea-
sured in order to maximize the differences in interclusters composed by the
foreground and background pixels. In this respect, the algorithms of Pun [15],
Kapur et al. [16], Abutaleb et al. [17], Li and Lee [18], Pal [19],and Sahoo et al. [20]
can be used to achieve the desired threshold. The disadvantage of these methods, as
mentioned, is the image binarization, which may be insufficient for US breast
images in termsof expected segmentation, when a narrow boundaryis important.
To overcome this problem, Albuquerque et al. [21] presented an algorithm for
ultrasound image segmentation based on a threshold computed using entropy infor-
mation. Their proposal considers the interpixel relationships as having log-time and
log-space interactions. These interpixel features yield a new kind of entropy, called
nonextensive entropy because it is a generalization of the traditional Boltzman-
Gibbs (BG) entropy for extensive systems. Their work suggests good results for US
breast images: It achieved better thresholds than the traditional BG entropy. How-
ever, the image binarization also is a limitation due to the issues mentioned earlier.
Our idea, as explained in this chapter, is as follows. (1) We propose a natural
extension to the algorithm proposed in [21] which recursively achieves more than
two regions in ultrasound images and also has a low computational time. (2) The
proposed algorithm, called the Nonextensive Segmentation Recursive Algorithm
(NESRA), is the first step of a five-step methodology for a CAD system for breast
lesion classification. (3) This methodology uses an SVM framework to classify the
breast regions as malignant or benign. This classification is carried out by taking
five lesion features into account: lesion area, circularity, protuberance, heterogene-
ity, and anechoicity. These features are combined to achieve the best performance in
terms of the system’s accuracy, sensitivity, specificity, PPV, NPV, and ROC curves.
The results shows better performance than the current literature documents.
In the next section we treat related works. Section 8.3 points out some theoreti-
cal background for further discussion. Section 8.4 presents our original approach,
which is discussed in Section 8.5.
8.2 Related Works
Several US techniques are used to classify breast lesions, including Bootstrap [4],
SVM [5], and neural networks [6, 7, 10]. These classifiers, however, are dependent
on the features that are defined by radiologists. Such characteristics are those
8.2 Related Works 191
dependent on the tumor profile, say, the geometric features or texture and color pat-
terns, as well as the features surrounding the lesion. These features, together with
the classifiers, determine the efficiency of the CAD software.
Sawaki et al. [8] proposed a CAD system that uses fuzzy inference for breast
sonography and adopted six different criteria to classify lesions: lesion shape, bor-
der, edge shadows, internal echoes, posterior echoes, and halo. However, their sys-
tem accuracy, sensitivity, and specificity were only 60.3%, 82.1%, and 42.9%,
respectively.
The use of a texture framework in US image processing goes back more than
two decades. Garra et al. [9] analyzed the breast lesions in ultrasound images using a
co-occurrence matrix of ultrasound images to represent their texture information.
Chen et al. [10] proposed an autocorrelation coefficient to analyze the texture infor-
mation. Because their implementations were presented on a smaller set of data, there
is a fundamental weakness with texture-based strategies: The settings for the US
machine parameters have to be fixed to acquire ultrasound images. If the ultrasound
parameter settings were changed, CAD performance became very unstable [6].
Moreover, a CAD system trained with images from one ultrasound machine needs
to be trained again for a different ultrasound machine because each machine has dif-
ferent image resolutions and image quality. Hence, Chen et al. [6] proposed nearly
setting-independent features based on shape information. Their system was very
robust and powerful because the statistical data using ROC curves were all greater
than 0.95. Chang et al. [22] also used six shape features in their analysis: factor,
roundness, aspect ratio, convexity, solidity, and extent. Note that all of the preced-
ing strategies were used on 2-D US images. As a result, the shape and structure infor-
mation of the breast lesions could not be reconstructed; consequently, it may not be
possible to determine the growth of the cancer and its spatial relationships.
Recently, 3-D US [13] has shown promising signs of overcoming the limitations of
traditional 2-D US to allow physicians to view the anatomy in three dimensions
interactively, instead of assembling the sectional images in their minds.
In the following, we discuss research that uses an autocorrelation matrix to
extract texture feature and neural networks as classifiers.
8.2.1 Breast Cancer Diagnosis with Autocorrelation Matrix
Because the surface features and internal structure of a tumor are not easily demon-
strated simultaneously using traditional 2-D US, Chen et al. [12, 14, 23] have inves-
tigated the use of texture features with 3-D US devices. They proposed use of an
autocorrelation matrix that is running difference matrixes. They have studied pixel
relation analysis techniques for use with 3-D US breast images and compares its per-
formance to 2-D versions of images. The rectangular subimages of the volume of
interest (VOI) were manually selected, and the selected VOIs were outlined to
include the entire extent of the tumor margin. They found that using features from
just a few slices were enough to provide good diagnostic results if the adopted fea-
tures were modified from the 2-D features.
Because the speckle noise artifacts and low-intensity regions, generally from
blood vessels, are present, it is not an easy task to extract the features from the US
images by means of a common image processing technique [24].One general feature
192 Automatic Classification of Breast Lesions in 3-D Ultrasound Images
of breast tumors is that the pixels in the tumor have a lower level intensity than the
surrounding normal tissue [25]. Chen et al. [10] presented work that suggest thats
the 2-D normalized autocorrelation coefficients are suitable for reflecting the
interpixel relationships within an image and make it possible to differentiate
between benign and malignant breast masses. Different tissues have significantly
varying pixel relations in US images.
Classically, benign tumors are described as regular masses with homogeneous
internal echoes, but carcinomas are described as having fuzzy borders with hetero-
geneous internal echoes. The correlation between neighboring pixels within the 2-D
images is a patent feature of the tumor. This study is based on pixel relation analysis
techniques developed by Chen et al. [10, 26, 27]. They have used normalized
autocorrelation coefficients to reflect the interpixel correlation within a region. To
generate similar autocorrelation characteristics for tumor regions under different
brightnesses but with a similar pixel relation, the autocorrelation coefficients are
further modified into mean-removed autocovariance coefficients. In their work,
these autocovariance coefficients for each breast tumor image are found and taken
as interpixel relation features in order to distinguish the differences between benign
and malignant tumors.
Generally, the pixel per centimeter rates of each 3-D US image are not all the
same and aredecided bythe magnifier control. Inpractice, therange ofpixel rates in
their database was between 20.35 and 110.56 pixels/cm. For an US image device,
the autocorrelation computation will give a 5 × 5 autocorrelation matrix. Because
the large variations in the resolution will affect and reduce the diagnostic result, the
distances in the autocorrelation matrix should not be fixed for all cases and should
be changed according to their pixel rates.
With 3-D US, the scanned slices are reconstructed into a single 3-D volume
dataset. The original 2-D autocorrelation coefficients do not accord with the 3-D
volume dataset. To overcome this problem, Chen et al. [14] proposed three new fea-
ture extraction methods to reduce the dimension of 3-D volume data from three to
two to fit the 2-D extraction function and to attempt to compare the diagnostic
results of these methods.
The first method is called the accumulative autocorrelation scheme (ACC-US).
In this scheme, each 2-D image is first extracted from a 3-D volume dataset, and
then the pixel relation coefficients are computed for each 2-D image. The
autocorrelation matrix for the 3-D dataset will be accumulated into a new
autocorrelation matrix. The accumulative matrixes for all of the 3-D training
datasets are then fed into a neural network to compete the training process; then,
the trained neural network could be used for breast tumor diagnosis.
There are two main stages. In the first segmentation stage, the extraction of the
VOI is carried out and the autocorrelation matrix is computed. The procedure of
VOI extraction is carried out in two steps. First, the physician must choose three key
planes in the 3-D volume dataset, called the first, middle, and last planes. The first
and last planes are the first and last slices that contain the tumor. The middle plane
is the slice where the area of the tumor is the biggest. Second, the physician defines
three ROIs in these three planes. This system then uses a linear interpolation
method to select the ROIs of the other planes in the 3-D volume by relying on the
three key ROIs and, then, all of the ROIs are combined to form a VOI. After the
8.2 Related Works 193