06819211

IEEE TransacTIons on UlTrasonIcs, FErroElEcTrIcs, and FrEqUEncy conTrol, vol. 61, no. 6, JUnE 2014 955

08853010 2014 IEEE

Discrimination of Breast Microcalcifications Using a Strain-Compounding Technique with Ultrasound Speckle Factor Imaging

yin-yin liao, chia-Hui li, Po-Hsiang Tsui, chien-cheng chang, Wen-Hung Kuo, King-Jen chang, and chih-Kuang yeh, Senior Member, IEEE

AbstractThe usefulness of breast ultrasound could be ex-tended by improving the detection of microcalcifications by being able to detect and enhance microcalcifications while si-multaneously eliminating hyperechoic spots (e.g., speckle noise and fibrocystic changes) that can be mistaken for microcalcifi-cations (i.e., false microcalcifications). This study investigated the use of a strain-compounding technique with speckle factor (SF) imaging to analyze the degree of scatterer redistribu-tions in breast tissues under strain conditions for identifying microcalcifications and false microcalcifications. The efficacy of the proposed method was tested by collecting raw data of ultrasound backscattered signals from 26 lesions at BI-RADS category 4 or 5 with suspicious microcalcifications. The differ-ent strain conditions were created by applying manual com-pression to deform the breast lesion. For each region in which microcalcifications were suspected, estimates of the SNR of the strain-compounding B-scan images and estimates of the mean SF (SFavg) in the strain-compounding SF images were calculated. Compared with microcalcifications, the severity of speckle of the false microcalcifications would be easily de-graded under compressive strain conditions. The results dem-onstrated that the SNR estimates in the strain-compounding B-scan images for microcalcifications and false microcalcifica-tions were 5.22 1.04 (mean standard deviation) and 4.62 1.09, respectively; the corresponding SFavg estimates in the strain-compounding SF images were 0.47 0.10 and 0.22 0.10 (p < 0.01). The mean area under the receiver operating characteristic curve using the SNR estimate was 0.71, whereas that using the SFavg estimate was 0.94. These findings indicate that the strain-compounding SF imaging method is more ef-fective at discriminating between microcalcifications and false microcalcifications.

I. Introduction

Microcalcifications are tiny deposits of calcium oxalate or calcium hydroxyapatite within the breast

tissue that constitute the main diagnostic feature in early breast cancers [1]. Mammography has a high sensitivity but a low specificity for detecting breast microcalcifica-tions [2]. Mammographically detected clustered microcal-cifications have a size of about 100 m or greater [3]. The dense breast tissues, and especially in younger women, cause suspicious regions to be almost invisible, and yield a misdiagnosis rate for detection of breast cancers as high as 15% to 25% [4]. In addition, dense tissues can easily be misinterpreted as microcalcifications [5], [6]. although the scanning resolution of ultrasound imaging is lower than the that of mammography, the use of mammography is often complemented with ultrasound because ultrasound can penetrate dense tissues to detect microcalcifications, and thereby identify early breast cancers within dense breasts [7][9].

However, the rates of false-negative and false-positive results have been high when using ultrasound to detect microcalcifications [10]. It is known that the amplitude reflection coefficient of microcalcifications in tissue is close to 0.9, and they frequently manifest as a bright point re-flector in ultrasound B-scan images [10]. There is a lim-it to the physical size of microcalcifications detected by ultrasound which is dependent on the spatial resolution of ultrasound imaging. Microcalcification visualization is also influenced by many factors, including speckle patterns and display parameters [10][12]. Moreover, speckle noise or some types of anatomical structures such as coopers ligaments or fibrocystic changes could be mistaken for mi-crocalcifications, because they usually cause hyperechoic signals with shadowing effects [13][16]. several investi-gations have described these false-positive microcalcifica-tion signals as false microcalcifications. To eliminate the false detection of microcalcifications, physicians always alter the scan angle or compress the breast [17], [18]. If a hyperechoic signal with a shadowing effect disappears by transducer compression or angulation, it is probably caused by refractive shadowing related to a coopers liga-ment or speckle interference.

The described characteristics imply that combining in-formation from multiple images obtained under different states could be useful in discriminating between true and false microcalcifications. compounding refers to additive-ly combining multiple ultrasound images obtained from different apertures or frequency bands into a single image (e.g., spatial compounding or frequency compounding), which could suppress the artifacts such as speckle interfer-

Manuscript received december 25, 2013; accepted March 25, 2014. The authors acknowledge the national science council of Taiwan (grant 101-2221-E-007-035-My3) and the national Tsing Hua University (grants 102n2046E1 and 102n2745E1) for their support.

y.-y. liao, c.-H. li, and c.-K. yeh are with the department of Bio-medical Engineering and Environmental sciences, national Tsing Hua University, Hsinchu, Taiwan (e-mail: [email protected]).

P.-H. Tsui is with the department of Medical Imaging and radio-logical sciences, college of Medicine, chang Gung University, Taoyuan, Taiwan, and the Institute for radiological research, chang Gung Uni-versity, Taoyuan, Taiwan.

c.-c. chang is with the Institute of applied Mechanics, national Tai-wan University, Taipei, Taiwan.

W.-H. Kuo and K.-J. chang are with the department of surgery, national Taiwan University Hospital, Taipei, Taiwan.

K.-J. chang is also with the department of surgery, cheng ching General Hospital, Taichung, Taiwan.

doI http://dx.doi.org/10.1109/TUFFc.2014.2991

IEEE TransacTIons on UlTrasonIcs, FErroElEcTrIcs, and FrEqUEncy conTrol, vol. 61, no. 6, JUnE 2014956

ence and acoustic shadowing [19], [20]. However, the spa-tial resolution is lower for such compounding techniques because the full aperture or full bandwidth is not used. a strain-compounding imaging technique was recently proposed for speckle noise reduction that degraded the spatial resolution less [21][23]. This technique is based on combining multiple images obtained under different strain conditions, where the different strain states can be created by acoustic-radiation-force excitation or applying manual external compression. The papers by anderson et al. [10], [24] discuss the detectability of microcalcifications in a speckle background and analyze the efficacy of spatial and frequency compounding in the detection of microcal-cifications. Those authors found that these compounding methods improve the visualization of microcalcifications by only a small degree because of the small effects that such methods have on the amplitudes of signals associ-ated with the microcalcifications. This suggests that the detectability of microcalcifications should be significantly advanced by a more comprehensive characterization of their acoustic properties in vivo.

recently, the nakagami distribution was considered as a general model to describe backscattered signals exhib-iting varying statistics, including those associated with pre-rayleigh, rayleigh, and post-rayleigh distributions, which are dependent on the density, arrangement, phase, and other properties of scatterers in tissues [25], [26]. our previous study showed that the existence of microcalcifi-cations resulted in one specific case of pre-rayleigh sta-tistics for the envelope, and the nakagami parameter (m) was used to detect and classify microcalcifications [27]. However, some research has reported that the presence of resolvable strong scatterers (i.e., the existence of micro-calcifications) led to different average powers from each cluster, constituting a scenario which was not part of the nakagami scattering regime [28], [29]. The speckle fac-tor (sF) given by shankar [29] was the inverse of the m in a peculiar case in which clusters had identical powers, but this peculiar case did not correspond to the existence of microcalcifications. nevertheless, shankar performed simulations showing that even when the nakagami distri-bution was not the right fit for the envelope signal from tissues containing microcalcifications, the sF still provid-ed useful information [29]. The presence of microcalcifi-cation-like regions in B-scan images was characterized by the existence of extremely bright spots and high variations in intensity, leading to high levels of speckle (i.e., high sF values). However, the study presented by shankar did not focus on the detection of actual microcalcifications within the breast lesions using ultrasound.

The objective of this study is to apply the strain-com-pounding method to sF imaging to discriminate between microcalcifications and false microcalcifications. The meth-od involves combining the compounding of multiple imag-es and sF imaging to provide information corresponding with the speckle characteristics of tissues. In this study, in vitro experiments were introduced to confirm the feasibil-ity of the strain-compounding sF image in identifying the

relative contribution of scattering from microcalcifications within the soft and hard phantoms after scatterers were redistributed as a result of mechanical deformations. In the ultrasound-based Breast Imaging reporting and data system (BI-rads) classification, lesions with suspicious microcalcifications are always classified as category 4 or 5. Therefore, clinical data were obtained from 26 breast lesions (20 and 6 at BI-rads categories 4 and 5, respec-tively) that had been subjected to histological work-up.

This paper is organized as follows. section II introduces the materials and methods for constructing the strain-compounding sF image, and describes how different strain conditions were created by manual compression. section III presents the results obtained in phantom experiments and clinical tests to reveal the capabilities of the strain-compounding B-scan image and the strain-compounding sF image; the performances of these two types of strain-compounding images in identifying microcalcifications and false microcalcifications are evaluated by receiver operat-ing characteristic (roc) curve. section V discusses the potential of the strain-compounding sF image in breast ultrasound.

II. Materials and Methods

A. Phantom Experiments

Two types of 60 40 40 mm phantoms, each with a 1-mm-diameter cluster of calcium hydroxyapatite par-ticles embedded in the soft and hard inclusions, were produced to mimic clustered microcalcifications within the soft (e.g., benign tumor) and hard (e.g., malignancy) breast tumors, respectively. calcium hydroxyapatite has been considered to be the main chemical composition of the microcalcifications within the breast tumors [30]. Ta-ble I lists the compositions, scatterer density, and youngs modulus of the phantoms. The phantom was comprised of agarose (Ultrapure agarose, Invitrogen, carlsbad, ca), distilled deionized water, and 75-m glass beads (59200-U, supelco, Bellefonte, Pa). a homogeneous gel of agarose and distilled deionized water was produced by raising the temperature of the solution to 100c before adding glass beads. The mixture was then poured into the phantom container and rotated until it had cooled and congealed. The scatterer density (D) in the container, which is de-fined as the number of scatterers per cubic millimeter, can be estimated to be [31]

Dmr

= g

43

3pi , (1)

where mg, r, and correspond to the mass, radius, and density of glass beads, respectively, and denotes the total volume of the solution in the container. Then, the phan-toms with various scatterer densities were made by adding different weights of glass beads. according to the previous study [32], the scatterer density in the phantom of greater

liao et al.: discrimination of breast microcalcifications using a strain-compounding technique 957

or comparable to 32 scatterers/mm3 tended to be fully de-veloped in statistics, leading to the rayleigh distribution. In this study, each phantom background (32 scatterers/mm3) corresponded to global backscattered statistics of a rayleigh distribution, and the backscattered statistics of the inclusions (16 scatterers/mm3) were closer to pre-rayleigh distributions.

Ultrasound images of the phantom were acquired using a portable ultrasound scanner (model 3000, Terason, Bur-lington, Ma), with the wideband linear-array transducer operating at a central frequency of 7.5 MHz. Moreover, the pulse length of the incident wave was approximately 0.9 mm, the axial resolution was 0.4 mm, and the lateral resolution was 0.6 mm. Insana et al. [33] reported that stiff scatterers (e.g., glass beads) in the phantom redistrib-uted but did not individually deform during limited com-pression, and the number density of scatterers remained constant. They used an engineering strain = (L0 L)/L0 as that resulting from the compressive stress applied. L0 was the initial sample height along the compression axis and L was the instantaneous height. consequently, the compressive strain () should be smaller than 0.2 to ensure sample integrity for scattering measurements, and the number density of scatterers remained constant with the estimate because the phantom volume was conserved under deformation [33]. In this study, the linear trans-ducer was attached to a 3-axis motion stage to provide a uniform stress distribution, and it was pressed into the top surface a distance of 10% of the total height of the phantom (i.e., 4 mm) with the bottom surface held fixed, which corresponded to the applied value of 0.1. a mul-tiple compression strategy was employed in the phantom. approximately 75 sequential B-scan images were acquired in 3 s. Each scan line was demodulated using the Hilbert transform to obtain the envelope image, and the B-scan image was obtained based on the logarithm-compressed envelope image with a dynamic range of 40 dB. The pixel size of the B-scan image was 0.09 mm.

B. In Vivo Application: Clinical Data

To assess the clinical efficiency of the proposed method, we collected breast image data from the national Taiwan University Hospital, and obtained informed consent from the associated patients. subjects having breast masses with suspicious microcalcifications using breast ultrasound were included in the study. The 26 microcalcification-like regions associated with breast lesions in the B-scan im-

ages were identified by a physician with more than 20 years of clinical experience. These lesions were categorized based on the BI-rads as category 4a (suspicious ma-lignancy with low likelihood) in seven cases, category 4B (suspicious malignancy with intermediate likelihood) in five cases, category 4c (suspicious malignancy with mod-erate likelihood) in eight cases, and category 5 (highly suggestive of malignancy) in six cases. The final assess-ments were performed by mammography and ultrasound-guided core biopsy. note that the positions captured by ultrasound and punched by core biopsy were the same. The lesion sizes ranged from 1 to 3.5 cm. Finally, the da-tabase contained 14 microcalcifications within malignant tumors, 4 microcalcifications within benign tumors, and 8 benign lesions without microcalcifications. Thus, there were 8 cases of false-positive findings in detecting micro-calcifications using ultrasound, and they were denoted as false microcalcifications in this study.

several studies have stated that microcalcifications are mostly useful in detecting intraductal or small invasive cancers smaller than 5 mm [34]. Typical benign microcal-cifications may vary in size from 2 to 4 mm, and micro-calcifications suggestive of malignancy are usually smaller than 1 mm [35]. In addition, according to the previous studies [36], [37], microcalcifications ranging from 1 to 2 mm in diameter could be detected by 7.5 MHz ultra-sound. The diameter sizes of the clustered microcalcifica-tions included in this study ranged from 0.8 to 3 mm. The correlations between the associated ultrasound findings and the histological findings of all cases are summarized in Table II. The experimental procedure and image-acquisi-tion protocol were the same as those used in the phantom experiments. Manual compression by the experienced phy-sician was applied to the target lesion at a relatively con-stant velocity and the compression depth was 5 to 8 mm. a sequence of images was acquired within 3 s.

C. Strain-Compounding B-Scan Imaging

Fig. 1 shows a schematic diagram of the four stages involved in the strain-compounding B-scan imaging meth-od: 1) compression using an external force; 2) in-plane motion estimation by speckle tracking; 3) correction for in-plane motion; and 4) incoherent image addition [22]. The manual compression used to induce different strain conditions deformed the object and caused the scatterers to move in three dimensions. accurate speckle tracking requires a strong correlation between two images, whereas

TaBlE I. compositions of the Three Types of Material Included in Phantoms designed to simulate the scatterer and Elasticity characteristics of Tissues.

Material

Ultrapure agarose

(g)

distilled deionized water

(ml)

Glass beads (g)

scatterer density

(scatterers/mm3)

calcium hydroxyapatite particles

youngs modulus

(mean std kPa)

Background 0.75 100 1.81 32 none 43.4 2.2soft inclusion 0.075 10 0.09 16 1-mm-diameter cluster 48.7 1.6Hard inclusion 0.15 10 0.09 16 1-mm-diameter cluster 135.2 6.3


a weak correlation is required for effective speckle reduc-tion. Therefore, the compression was performed in mul-tiple small increments. It was important to investigate the degree of speckle correlation under different strain condi-tions to develop an optimal strategy and to understand the performance of the strain-compounding B-scan imag-ing method [23].

The correlation coefficient between the reference B-scan image (uncompressed image) and the B-scan image obtained under a different strain condition (i.e., compari-son image) was calculated. In these images, we manually defined a rectangular roI that included the entire lesion and its margins, because the correlation coefficient de-pended on the size of the roI. Then, the correlation co-efficient as a function of the applied strain was used to determine the probabilistic speckle decorrelation, which was obtained when the correlation coefficients decreased monotonically to their minimum values. note that using

five compounded frames was sufficient to reduce the ap-pearance of speckle and to maximize the contrast resolu-tion in the compound B-scan imaging method [38], [39]. Therefore, five frames were compounded in the acquisition sequence that had produced probabilistic speckle decorre-lation. The block sum pyramid algorithm (BsP) and the multilevel block-matching algorithm were combined and referred to as the multilevel BsP algorithm, which had excellent computational performance for two-dimensional speckle tracking in B-scan images to obtain the in-plane displacement map (i.e., where the scatterers move to) [40]. The acquired displacement maps were then used to cor-rect the pixel coordinates of the five compounded frames. Finally, the strain-compounding B-scan image could be obtained by combining the five frames that had been spa-tially corrected.

The degree of speckle reduction for the B-scan images was quantified by calculating the speckle snr as

SNR targettarget

= , (2)

where and are the mean and standard deviations of the amplitude, respectively; these values were estimated in the target using the manually contoured regions.

D. Strain-Compounding Speckle Factor (SF) Imaging

The probability density function of the ultrasound backscattered-signal power Z under the nakagami statis-tical model is given by [26]

f zm zm

mz U z

m m

m( ) ( )exp ( ),= ( )1

(3)

where z corresponds to the possible values for random variable Z of the backscattered-signal powers, and () and U() are the gamma and unit step functions, respec-tively. The average power is given by . The nakagami parameter m associated with the nakagami distribution can be obtained from

mZ

=

2

2( ). (4)

TaBlE II. correlations Between Ultrasound Findings and Histological Findings of 26 lesions.

Ultrasound-based BI-rads category Microcalcifications

False microcalcifications Biopsy results

4a 1 6 Fibroadenoma (5)Fibroepithelial lesion (1)Fibrocystic disease (1)

4B 3 2 Fibroadenoma (5)4c 8 0 Invasive ductal carcinoma (6)

ductal carcinoma in situ (1)Intraductal papillary carcinoma (1)

5 6 0 Invasive ductal carcinoma (6)Total 18 8

Fig. 1. schematic diagram of the strain-compounding technique.


Then, the sF is defined as [29]

SF = = ZZ

ZZ

2

2 21var( )

, (5)

where denotes the statistical average and var () is the variance. The m is bounded by the lower limit of 1/2. The sF becomes 1/m, and it was the quantitative measure of the severity of the speckle [29].

one previous study demonstrated that the optimal slid-ing window size for constructing the nakagami parameter image was a square with a side length equal to three times the pulse length of the incident ultrasound [32]. such a window size provided both stable estimations of the m and an acceptable imaging resolution. consequently, a sliding window size of 2.7 2.7 mm was applied in the follow-ing constructions of the sF image. The sF image was constructed from the sF map, which was obtained us-ing a sliding window within the envelope image to collect the local backscattered-signal powers for estimation of the sF based on the first and second moments of the power. Then, the sF estimate was assigned as the new pixel value located in the center of the window at each position in the image. The sF image was normalized, and a rainbow color scale was applied to emphasize the information in the image.

The process followed was similar to that used in the strain-compounding B-scan imaging method. To correct-ly compound five sF images obtained in the acquisition sequence, the in-plane displacement fields acquired from the five B-scan images were used to spatially correct the corresponding envelope images. The corrected envelope images were used to form the sF images, and these sF images were additively combined to create the strain-com-pounding sF image. The basic flowchart summarizing the procedure employed in this study is shown in Fig. 2. addi-tionally, we estimated the average of the sF (sFavg) values within the manually contoured areas in the sF images.

E. Statistical Assessment

The respective performances of using the strain-com-pounding B-scan and strain-compounding sF imaging methods to discriminate microcalcifications and false mi-

crocalcifications were evaluated using the roc curves and tests based on students t-test [41]. a difference was as-sumed to be statistically significant when its probability value was less than 0.05. The area under the roc curve (Az) was an effective way of comparing the diagnostic ef-ficiency, and it was independent of the decision cut-off point, eliminating the influence of the cut-off point on sensitivity and specificity values.

III. results

A. Phantom Experiments

Figs. 3(a) and 4(a) show the reference B-scan images of the phantoms consisting of hard and soft inclusions (delineated by white circles) associated with microcalci-fications (denoted by arrows), respectively. The two in-clusions corresponded to the hypoechoic signals, and the microcalcification regions showed hyperechoic signals with shadowing effects. The strain-compounding B-scan images of the phantoms are shown in Figs. 3(b) and 4(b). The measurements were performed on six separate phantoms (three with hard inclusions and three with soft inclusions). The snr estimates were 5.32 0.51 dB (mean stan-dard deviation) and 5.50 0.33 dB for microcalcifications within the hard (n = 3) and soft (n = 3) inclusions in the reference B-scan images, respectively, and the correspond-ing snr estimates for the strain-compounding B-scan im-ages were 5.40 0.45 dB and 6.91 0.73 dB.

The microcalcifications within the hard inclusion in the reference and strain-compounding sF images are shown in Figs. 3(c) and 3(d), respectively. Figs. 4(c) and 4(d) show the soft inclusion with microcalcifications in the ref-erence and strain-compounding sF images, respectively. To find the sF value distributions of microcalcifications embedded in the inclusions, regions that simultaneously included microcalcifications and the surrounding inclusion were selected to analyze the histograms of the sF esti-mates. Figs. 3(e) and 3(f) show the sF value histograms of microcalcifications within the hard inclusion in the ref-erence and strain-compounding sF images, respectively. The histograms for the reference and strain-compounding sF images contained two peaks; the one at 0.2 to 0.4 was

Fig. 2. Flowchart summarizing the strain-compounding sF imaging method used in this study. The steps involved in in-plane motion correction are indicated by solid arrows; dashed arrows indicate the steps involved in strain-compounding sF imaging.


caused by microcalcifications, as indicated by the dashed rectangles. This phenomenon was also observed in the soft inclusion with microcalcifications [see dashed rectangles in Figs. 4(e) and 4(f)]. We estimated the sFavg within the microcalcification areas in the sF images: this was 0.42 0.02 and 0.65 0.01 for the hard and soft inclusions in the reference sF images, and 0.43 0.02 and 0.66 0.01 in the strain-compounding sF images.

B. Clinical Applications

Figs. 5 and 6 represent images of an invasive ductal car-cinoma with microcalcifications and a fibroadenoma with false microcalcifications, respectively. Microcalcifications and false microcalcifications are indicated by arrows, and appear as hyperechoes in the reference B-scan images, as seen in Figs. 5(a) and 6(a). The corresponding strain-com-pounding B-scan images are respectively shown in Figs. 5(b) and 6(b). Figs. 5(c) and 5(d) respectively show mi-

crocalcifications in the reference and strain-compounding sF images. The tumor boundaries (delineated by white lines) were manually determined by the experienced phy-sician for analysis of the histograms. The histograms of microcalcifications were similar in the reference sF image and the strain-compounding sF image [see dashed rect-angles in Figs. 5(e) and 5(f)]. compared with the cases of microcalcifications, the histograms in the cases of false microcalcifications were changed after the compounding process, as shown in Figs. 6(c)6(f).

To differentiate microcalcifications from false microcal-cifications, the snr and sFavg estimates were calculated by determining the regions containing suspicious micro-calcifications in the B-scan and sF images, respectively. Fig. 7 shows box plots for the distributions of snr and sFavg estimates for microcalcifications (n = 18) and false microcalcifications (n = 8). The snr estimates for mi-crocalcifications in the reference and strain-compounding

Fig. 3. different types of images of a phantom containing a hard in-clusion with clustered microcalcifications: (a) reference B-scan image, (b) strain-compounding B-scan image, (c) reference sF image, and (d) strain-compounding sF image. White circles delineate the hard inclu-sion, and the arrows indicate the microcalcifications. Histograms for the roI in (e) the reference sF image and (f) the strain-compounding sF image. The dashed rectangle encompasses most of the sF values corre-sponding to the microcalcifications.

Fig. 4. different types of images of a phantom containing a soft inclusion with clustered microcalcifications: (a) reference B-scan image, (b) strain-compounding B-scan image, (c), reference sF image, and (d) strain-compounding sF image. White circles delineate the hard inclusion, and the arrows indicate the microcalcifications. Histograms for the roI in (e) the reference sF image and (f) the strain-compounding sF image. The dashed rectangle encompasses most of the sF values corresponding to the microcalcifications.


B-scan images were 4.73 0.69 dB and 5.22 1.04 dB, respectively; the corresponding values for false microcal-cifications were 4.58 0.56 dB and 4.62 1.09 dB. The sFavg values for microcalcifications in the reference and strain-compounding sF images were 0.41 0.11 and 0.47 0.10, respectively; the corresponding estimates for false microcalcifications were 0.30 0.16 and 0.22 0.10. We found that the sFavg values differed significantly between the cases of microcalcifications and false microcalcifica-tions (p < 0.05 for reference sF image, p < 0.01 for strain-compounding sF image).

However, because the t-test did not take into account of the variation of samples, a comparison of the validity of the two estimates (i.e., snr and sFavg) in discriminat-ing between microcalcifications and false microcalcifica-tions, using roc analysis, is illustrated in Fig. 8. The diagnostic efficiency was best for the sFavg estimates in the strain-compounding sF images, with an accuracy of 88.5%, a sensitivity of 83.3%, and a specificity of 100.0%,

as indicated in Table III. The positive (PPV) and negative predictive values (nPV) were 72.7% and 100.0%, respec-tively. Moreover, the mean Az obtained by the snr esti-mate in the strain-compounding B-scan image was 0.71, with a 95% confidence interval of 0.47 to 0.94, and that obtained by the sFavg estimate in the strain-compounding sF image was 0.94, with a 95% confidence interval of 0.82 to 1.00. This difference was demonstrated by comparing the Az values. The ability in distinguishing microcalcifica-tions from false microcalcifications differed significantly between the snr and the sFavg estimates (p < 0.05); namely, the classification performance was significantly better for the strain-compounding sF imaging method than for the strain-compounding B-scan imaging method.

IV. discussion

Improvements in the ability to detect microcalcifica-tions would extend the usefulness of breast ultrasound.

Fig. 5. different types of images of an invasive ductal carcinoma as-sociated with microcalcifications (arrows): (a) reference B-scan image, (b) strain-compounding B-scan image, (c) reference sF image, and (d) strain-compounding sF image. The white line delineating the tumor contour was manually tracked by the physician. Histograms are shown for the tumor region in (e) the reference sF image and (f) the strain-compounding sF image. The dashed rectangle encompasses most of the sF values corresponding to the microcalcifications.

Fig. 6. different types of images of a fibroadenoma associated with false microcalcifications (arrows): (a) reference B-scan image, (b) strain-com-pounding B-scan image, (c) reference sF image, and (d) strain-com-pounding sF image. The white line delineating the tumor contour was manually tracked by the physician. Histograms are shown for the tumor region in (e) the reference sF image and (f) the strain-compounding sF image. The dashed rectangle encompasses most of the sF values corre-sponding to the false microcalcifications.


For this it is necessary to detect and enhance microcal-cifications while simultaneously eliminating other hyper-echoic spots that can result in false microcalcifications. The strain-compounding technique was used in this study to improve the discrimination of microcalcifications and false microcalcifications because the uncertainty associ-ated with working on a single image can be reduced by combining multiple images obtained under different strain conditions. applying the strain-compounding technique to the B-scan and sF images can have different physi-cal meanings. The strain-compounding B-scan image de-

scribes the similarity and correlation of the pixels among multiple compounded B-scan images, reflecting the varia-tion in the structural echogenicity of tissues under differ-ent strain conditions. The effect on speckle reduction for the strain-compounding B-scan image could be minimal because of the effect such a method had on the amplitudes of signals associated with the microcalcifications, result-ing in a small degree of the improvement for identifying microcalcifications (i.e., the snr estimates for microcalci-fications in the reference and strain-compounding B-scan images were 4.73 0.69 dB and 5.22 1.04 dB, respec-tively). on the other hand, the strain-compounding sF image displays the statistical distribution of the backscat-tered signals in tissues, which is determined by the corre-lation and similarity of the statistical moments of powers within a scattering medium among multiple compounded sF images. The severity of speckle of the false microcal-cifications would be easily degraded under compressive strain conditions compared with that of microcalcifica-tions. That might be one of the reasons why compounding reduced the sFavg values of the false microcalcifications while the actual microcalcifications withstood the effects of compounding and stayed where they were, improving their visibility in the strain-compounding sF images.

Fig. 7. Box plots showing the distributions (a) of the snr estimates for the B-scan images and (b) of the sFavg estimates for the sF images for microcalcifications and false microcalcifications. Box plots indicate the median value (bold line), 25th to 75th percentiles (box), and the data range (whiskers). an asterisk indicates p < 0.05; double asterisks indicate p < 0.01.

Fig. 8. roc curves for when using the snr estimates and the sFavg estimates to classify microcalcifications and false microcalcifications.

TaBlE III. Performances of the snr and the sFavg Estimates assessed by Their accuracy, specificity, sensitivity, Positive Predictive Value (PPV), negative Predictive Value (nPV), and Az Value in classifying

Microcalcifications and False Microcalcifications.

classifier performance parameter

snr estimates in reference

B-scan images

snr estimates in strain-compounding

B-scan images

sFavg estimates in reference sF images

sFavg estimates in strain-compounding

sF images

accuracy (%) 57.7 65.4 76.9 88.5sensitivity (%) 44.4 87.5 83.3 83.3specificity (%) 87.5 55.6 62.5 100.0PPV (%) 41.2 90.9 62.5 72.7nPV (%) 88.9 46.7 83.3 100.0Az (mean standard error) 0.60 0.12 0.71 0.12 0.72 0.12 0.94 0.06Az (95% confidence interval) 0.340.85 0.470.94 0.490.95 0.821.00


In the present study, we only considered cases with clearly developed tumor masses. In other words, the strain compounding technique with sF imaging was applied to breast B-scan images with clear tumor boundaries. The sFavg estimate for strain-compounding sF imaging distin-guished all false microcalcifications among eight lesions at BI-rads category 4a or 4B on conventional ultrasound (see Table II), and thus can give physicians the opportu-nity to downgrade from BI-rads category 4 to category 3 in these cases. These effects could be more important in such cases than the cases of suspicious microcalcifications within highly malignant masses on ultrasound.

several imaging techniques have been proposed for as-sisting the detection of microcalcifications in ultrasound imaging. For instance, the elastography image, which re-flects the tissue stiffness, has been used to classify benign and malignant lesions associated with microcalcifications [42], and vibro-acoustography has been applied in tissue elasticity imaging to detect breast microcalcifications [43], [44]. However, the extent of microcalcifications within the lesion cannot be identified and evaluated by elastography. Moreover, implementing vibro-acoustography for clinical applications requires a hydrophone to receive ultrasound backscattered signals during scanning. The strain-com-pounding sF imaging method proposed herein can visu-alize microcalcifications using a commercial ultrasound system, but its ability to detect microcalcifications still depends on their size, distribution, form, and density.

V. conclusions

In conclusion, the measurements of strain-compounding sF images in breast tissues can be used to distinguish the relative contribution of scattering from microcalcifications versus false microcalcifications after scatterers were redis-tributed as a result of mechanical deformations. However, the manual roI should be close to the size of microcalci-fications and larger than the sF image resolution. There will be variation from person to person and that is the reason for difficult putting a specific value of sFavg in the strain-compounding sF image for classification. Besides, this method would be limited by adopting the unfitting statistical model (i.e., nakagami model), estimating er-ror in the moment-based method, and the manual com-pression conditions. recently, the McKay density model presented by shankar [29] was utilized to retain all the scattering conditions contained in the nakagami model and to include the specific condition caused by the exis-tence of microcalcifications. It is certain that validating the McKay model in clinical tissue characterization will become an important issue in future work. In addition, the ultrasound transducer can be mounted on a positioner controlled by a three-dimensional stepping motor to re-duce operator dependence. Further research is being done to advance this imaging technique with the McKay den-sity for fitting physical descriptions and clinical applica-tions simultaneously.

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Yin-Yin Liao was born in 1985 in Taichung, Tai-wan. she received her M.s. and Ph.d. degrees in the department of Biomedical Engineering and Environmental sciences from the national Tsing Hua University, Hsinchu, Taiwan, in 2009 and 2013, respectively. she worked as a postdoctoral researcher with the department of Biomedical En-gineering and Environmental sciences at the na-tional Tsing Hua University in 2013. Her research interests include computer-aided diagnosis of breast cancer, ultrasound tissue characterization, and tissue elasticity imaging.

Chia-Hui Li was born in 1986 in Taipei, Taiwan. she earned her B.E. degree in the department of Medical Imaging and radiological sciences from chang Gung University, Taoyuan, Taiwan, in 2009. she received her M.s. degree in the department of Biomedical Engineering and En-vironmental sciences from the national Tsing Hua University, Hsinchu, Taiwan, in 2011. she is currently a radiological technologist in the de-partment of obstetrics and Gynecology at the national Taiwan Uni-versity Hospital, Taipei, Taiwan. Her research interests include breast ultrasound and ultrasound parametric imaging.

Po-Hsiang Tsui (M09) was born in Taiwan. He received the B.E., M.s., and Ph.d. degrees in bio-medical engineering from chung yuan christian University, chung li, Taiwan, in 2000, 2001, and 2005, respectively. In 2006, he was with the re-search center for applied sciences, academia si-nica, Taipei, Taiwan, for postdoctoral research. In 2010, he joined the department of Medical Imag-ing and radiological sciences, college of Medi-cine, chang Gung University, Taoyuan, Taiwan. He is currently an associate Professor and the

director for the Medical Imaging research center, Institute for radio-logical research, chang Gung University and Hospital. His research in-terests focus on ultrasound scattering, parametric imaging, and tissue characterization.

Chien-Cheng Chang received a bachelors de-gree in chemical engineering from the national Taiwan University in 1980. In 1982, he was award-ed a University Fellowship to do an advanced graduate study at the University of california, Berkeley, and he received the Ph.d. degree in ap-plied mathematics in 1985. after that, dr. chang worked at the lawrence Berkeley national labo-ratory (lBnl) as a research associate. In 1986, he went to Minneapolis, holding a Postdoctoral Fellowship at the Institute for Mathematics and

its applications. since 1987, he has been a faculty member at the Insti-tute of applied Mechanics, national Taiwan University, and currently he is a distinguished Professor. From July 2005 to June 2009, Prof. chang held a joint appointment in academia sinica to establish the division of Mechanics and served as its Head in the research center for applied sciences. From august 2009 to July 2012, he served as the director of the Institute of applied Mechanics, national Taiwan University. His fields of research interest include fluid mechanics, multi-scale mechanics, and biomedical mechanics and engineering.

Wen-Hung Kuo is currently the attending sur-geon in the department of surgery, national Tai-wan University Hospital, Taipei, Taiwan. His re-search interests focus on clinical applications of breast ultrasound.


King-Jen Chang obtained his Ph.d. degree from the Institute of clinical Medicine, national Taiwan University, Taipei, Taiwan in 1982. He is currently the superintendent of cheng ching Hos-pital (chung-Kang Branch), Taichung, Taiwan. His research interests focus on clinical applica-tions of functional ultrasound imaging on breast diagnosis.

Chih-Kuang Yeh was born in 1973 in Taiwan. He received his B.s. and M.s. degrees in biomedi-cal engineering from chung-yuan christian Uni-versity, chung-li, Taiwan, in 1995, and the na-tional cheng-Kung University, Tainan, Taiwan, in 1997, and his Ph.d. degree in electrical engineer-ing from the national Taiwan University, Taipei, Taiwan, in 2004, respectively. He joined Professor Katherine Ferraras research group at the Univer-sity of california, davis as a visiting researcher from 2003 to 2004. In 2005, he joined the depart-

ment of Biomedical Engineering and Environmental sciences, national Tsing Hua University, Hsinchu, Taiwan. He now is a professor, and his current research interests include ultrasound contrast agents and ultra-sound theranostics.

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