Computer-aided Diagnosis Based on Speckle Patterns for
Ultrasound Images
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Woo Kyung Moon1, Chung-Ming Lo2, Chiun-Sheng Huang3, Jeon-Hor Chen4,5,
and Ruey-Feng Chang2,6*
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Department of Radiology, Seoul National University Hospital, Seoul, Korea
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Department of Computer Science and Information Engineering
National Taiwan University, Taipei, Taiwan
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Department of Surgery, National Taiwan University Hospital and National Taiwan
University College of Medicine, Taipei, Taiwan
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Center for Functional Onco-Imaging and Department of Radiological Science
University of California Irvine, California, USA
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Department of Radiology, China Medical University Hospital, Taichung, Taiwan
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Graduate Institute of Biomedical Electronics and Bioinformatics
National Taiwan University, Taipei, Taiwan
* Corresponding Author:
Professor Ruey-Feng Chang
Department of Computer Science and Information Engineering
National Taiwan University
Taipei, Taiwan 10617, R.O.C.
Telephone: 886-2-33664888~331
Fax: 886-2-23628167
E-mail: rfchang@csie.ntu.edu.tw
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Abstract
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For breast ultrasound, the scatterer number density from backscattered echo was
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demonstrated in previous research to be a useful feature for tumor characterization. To
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take advantage of the scatterer number density in B-mode images, spatial compound
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imaging was obtained, and the statistical properties of speckle patterns were analyzed
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in this study for use in distinguishing between benign and malignant lesions. A total of
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137 breast masses (95 benign cases and 42 malignant cases) were used in the
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proposed computer-aided diagnosis (CAD) system. For each mass, the average
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number of speckle pixels in a region of interest (ROI) was calculated to utilize the
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concept of scatterer number density. In addition, the first-order and second-order
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statistics of the speckle pixels were quantified to obtain the distributions of the pixel
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values and the spatial relations among the pixels. The performance of the speckle
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features extracted from each ROI was compared with the performance of the
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segmentation features extracted from each segmented tumor. As a result, the proposed
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CAD system using the speckle features achieved an accuracy of 89.1% (122/137), a
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sensitivity of 81.0% (34/42), and a specificity of 92.6% (88/95). All of the differences
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between the speckle features and the segmentation features are not statistically
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significant (p>0.05). In a receiver operating characteristic (ROC) curve analysis, the
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Az value of the speckle features was significantly better than the Az value of the
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segmentation features (0.93 vs. 0.86, p=0.0359). The performance of this approach
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supports the notion that the speckle patterns induced by the scatterers in tissues can
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provide information for classifying tumors. The proposed speckle features, which
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were extracted readily from drawing an ROI without any preprocessing, also provide
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a more efficient classification approach than tumor segmentation.
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Keywords: Speckle, Breast cancer, Ultrasound, Spatial compound imaging,
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Computer-assisted diagnosis
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Introduction
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Breast ultrasound (US), including spatial compound imaging (SCI), is being
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explored for distinguishing between benign and malignant lesions (Stavros et al. 1995;
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Cha et al. 2005; Cha et al. 2007). The sonographic appearances of breast tumors are
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constructed by means of acoustic transmission and are interpreted by radiologists
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upon clinical examination for a diagnosis. To standardize the terminology used for
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describing tumors , the BI-RADS lexicon was proposed by the American College of
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Radiology (American College of Radiology 2003). According to the descriptors
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defined in BI-RADS descriptive categories, the dominant sonographic findings of a
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tumor are classified and analyzed by radiologists to evaluate the likelihood of
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malignancy. The descriptive categories include shape, orientation, margin, lesion
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boundary, echo pattern, and posterior acoustic features. With the quantification of the
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descriptors used clinically, various computer-aided diagnosis (CAD) systems
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(Rangayyan et al. 2000; Shen et al. 2007a; Shen et al. 2007b; Nie et al. 2008) were
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developed to provide an efficient procedure to diagnose breast tumors automatically.
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By tumor segmentation, the tumor characteristics were extracted and quantified to
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distinguish between benign and malignant lesions. The quantitative features utilized in
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these CAD systems can be classified as morphology or texture features. Morphology
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features are used to describe the tumors’ shape characteristics, and texture features are
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used to show the echogenicity properties through the correlations among pixels.
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Furthermore, the speckle patterns from backscattered echo were analyzed to
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provide information for tissue characterization (Tuthill et al. 1988). Speckle is
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generated by the constructive and destructive interference of US waves backscattered
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from tissue scatterers, which are tissues with equal or smaller structures than the US
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wavelength. From observing the backscattered echoes, the effects of the scatterer
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number density can be shown by the statistical properties when the number per
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resolution cell is small (i.e., < 10). The amplitude histogram of the signal with a
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sparse scatterer number density has a pre-Rayleigh distribution. With the increasing
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number of scatterers, the speckle is fully developed, and the corresponding histogram
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approaches a Rayleigh distribution. Based on the analyzed results, the backscattered
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echo was further modeled by the Nakagami parameter to distinguish between benign
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and malignant lesions (Shankar et al. 2003; Chang et al. 2010). The Nakagami
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parameter effectively quantified the statistics of the backscattered echo from different
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scattering conditions, including pre-Rayleigh, Rayleigh and post-Rayleigh. In the
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previous studies, the speckle patterns from backscattered echo signal were
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demonstrated to be useful in classifying tumors. However, the signal data are not easy
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to acquire and are less familiar to radiologists who generally use B-mode images to
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evaluate tumor characteristics.
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In this study, we analyzed the speckle patterns in B-mode images obtained with
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SCI to develop more useful features for tumor classification. For speckle extraction, a
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tumor and its surrounding tissues were included in a region of interest (ROI) that was
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cropped from a B-mode image. Next, the statistical properties of the speckle pixels
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extracted from an ROI were quantified as features for distinguishing between benign
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and malignant lesions. Moreover, the diagnostic performance of the speckle features
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was compared with the performance of the morphology and texture features obtained
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by tumor segmentation.
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Materials and Methods
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US acquisition
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The breast US images collected in this study were acquired using an ATL HDI
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5000 scanner (Philips, Bothell, WA). The applied L12-5 probe was a 192-element
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linear array transducer with variable frequency ranging from 5 to 12 MHz and a scan
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width of 38 mm. We obtained image data sets with and without SCI (SonoCT™,
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Philips). The SCI method combined nine frames produced from different
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transmit angles to reduce speckle and shadowing. In our experiment, conventional
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US images were first obtained without SCI mode, and SCI images were subsequently
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obtained in an identical plane without changing depth, focus position, or gain settings.
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The gray-level value of the image pixels ranges from 0 to 255.
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A total of 137 masses were examined based on the B-mode images produced by
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the scanner from April 2002 to May 2003. Two breast radiologists with 5 and 15
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years of experience, respectively, classified the lesions into BI-RADS assessment
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categories according to the observation of the B-mode images before biopsy. There
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were 69 lesions (50%) in BI-RADS 3 (probably benign), 53 lesions (39%) in
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BI-RADS 4 (suspicious abnormality), and 15 lesions (11%) in BI-RADS 5 (highly
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suggestive of malignancy).
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The lesions of all patients that were pathologically proven by core needle biopsy
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or fine-needle aspiration cytology included 95 benign lesions (69%) and 42 malignant
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lesions (31%). The lesion sizes ranged from 0.4 to 3.0 cm (mean: 1.3 ± 0.6 cm). In the
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benign lesions, there were 74 cases of fibroadenoma and 21 cases of fibrocystic
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changes. The size was 0.4-2.5 cm (mean: 1.2 cm). In the malignant lesions, there
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were 40 cases of invasive ductal carcinoma, 1 case of invasive tubular carcinoma, and
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1 case of invasive papillary carcinoma. The size was 0.6-3.0 cm (mean: 1.6 cm). The
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patients with benign lesions had a mean age of 42 (range 22–64), and the patients with
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malignant lesions had a mean age of 49 (range 34–77). For this study, approval was
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obtained from our institution review board, and informed consent was waived.
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ROI
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A subimage of a B-mode image was cropped into an ROI. To specify a tumor
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region, a rectangular bounding box was used to enclose a tumor. In principle, there
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was a tumor in the center of the ROI, leaving a distance of 5–10 pixels between the
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tumor boundary and the bounding box. According to the tumor size, the smallest ROI
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was 55 × 42 pixels (0.57 cm × 0.44 cm), and the largest one was 290 × 160 pixels
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(3.02 cm × 1.67 cm). The illustration of ROI selection is shown in Fig. 1 (a).
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Speckle features
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A US B-mode image is composed of the backscattered echoes reflected from
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tissues. With the transmission of the US pulses, tissues generate various responses to
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the pulses, such as absorption, reflection, and scattering, according to their physical
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properties. The scatterers with microstructure contained in tissues, such as tissue
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parenchyma, scatter the US pulses and produce an interference pattern called speckle.
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In this study, the speckle pixels in B-mode images were extracted to generate useful
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features in tissue characterization.
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The speckle patterns have granular appearances with small difference in gray
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level. For fully developed speckle, the number of scatterers is considerable. The
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intensity image should have an exponential distribution and a constant ratio of the
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mean to the standard deviation (SD) of 1.0 (Tuthill et al. 1998). According to this
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speckle property, the B-mode images were first log decompressed to obtain the raw
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intensity images (Smith and Fenster 2000). The decompression procedure was
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performed to extract the speckle pixels more precisely. The pixel value of the
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acquired image data was log compressed to a 0-255 log scale to reduce the dynamic
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range. To obtain the raw intensity value, the log decompression converted the 0-255
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log scale back to a linear scale. The gray value of the pixels was converted by the
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equation
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𝐼(𝑥, 𝑦) = 10𝐺(𝑥,𝑦)∕𝐺0
(1)
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where G(x,y) is the pixel value in B-mode images and G0, which is a linearization
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factor related to the frequency of the transducer (Smith and Fenster 2000), converts
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G(x,y) to a linear scale. The G0 value was used to determine the range of the intensity
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values. After the conversion, I(x,y) is obtained as the acoustic intensity. Next, a
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moving 5×5 window is used to find a region with a range of ratios of mean intensity
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to SD of 0.8–1.2 in the raw intensity image. If a region satisfies the condition, the
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center pixel of the region is defined as speckle.
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After the extraction of speckle pixels, the statistical properties of the speckle
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pixels were converted into parameters for tissue characterization. In a previous study
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(Tuthill et al. 1988), the scatterer number density from backscattered echo is useful
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for tumor characterization. For B-mode images, the average number of speckle pixels
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in an ROI is calculated as
𝑆_𝑎𝑣𝑔𝑛𝑢𝑚 = 𝑆_𝑛𝑢𝑚 ∕ 𝑅𝑂𝐼_𝑛𝑢𝑚
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(2)
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where S_num is the number of total speckle pixels in an ROI, and ROI_num means
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the number of all pixels in an ROI. An illustration is presented in Fig. 1. The ROIs of
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a benign tumor and a malignant tumor are in Fig. 1 (b) and (d), respectively. The
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speckle pixels extracted from the ROIs are shown in Fig. 1 (c) and (e) by a white
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appearance (with a pixel value of 255 on the 8 bit gray-level image) for visualization.
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Clearly, the density of speckle pixels in the ROI of the benign tumor (Fig. 1 (c)) is
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higher than that of the malignant tumor (Fig. 1 (e)).
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In addition to S_avgnum, the first-order statistics and second-order statistics of
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the extracted speckle pixels in the ROI are utilized. Among the first-order statistics,
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the mean and SD of the qualified mean/SD (0.8–1.2) in a moving window are defined
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as
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𝑆_𝑚𝑒𝑎𝑛 = (∑𝑃∈𝑆 𝑚𝑆𝐷(𝑃)) ∕ 𝑆_𝑛𝑢𝑚
(3)
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𝑆_𝑆𝐷 = √(∑𝑃∈𝑆(𝑚𝑆𝐷(𝑃) − 𝑆_𝑚𝑒𝑎𝑛)2 ) ∕ 𝑆_𝑛𝑢𝑚 ,
(4)
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where mSD(P) is the mean/SD value of a speckle pixel. Also, the mean and SD of the
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extracted speckle pixels in the 256 gray-level are defined as
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𝑆_𝑔𝑚𝑒𝑎𝑛 = (∑𝑃∈𝑆 𝐺(𝑃)) ∕ 𝑆_𝑛𝑢𝑚
(5)
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𝑆_𝑔𝑆𝐷 = √(∑𝑃∈𝑆(𝐺(𝑃) − 𝑆_𝑔𝑚𝑒𝑎𝑛)2 ) ∕ 𝑆_𝑛𝑢𝑚 ,
(6)
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where G(P) is the gray-level value of a speckle pixel.
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With regard to the spatial relations between speckle pixels, textures, as the
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second-order statistics, are assumed to describe the regional information. In this study,
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the gray-level co-occurrence matrices (GLCM) (Haralick et al. 1973) are employed to
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provide texture features. In this work, 21 texture features, which were implemented to
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predict the likelihood of malignancy of the tumors in a classifier, are listed in Table 1.
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Segmentation features
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In this study, the speckle features obtained from an ROI were compared with the
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features extracted from a segmented tumor. To segment a tumor, the level set method
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was implemented (Huang et al. 2011). The quantitative features obtained from
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segmentation can be classified into two categories: morphology features and texture
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features (Rangayyan et al. 2000; Shen et al. 2007a; Nie et al. 2008; Chang et al. 2011).
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Morphology features were proposed to describe the tumor shape, and texture features
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were proposed to represent the tumor echogenicity.
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Morphology features
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After segmentation, the tumor contour is delineated by the level set method.
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Next, the geometric characteristics of the tumor contour can be described by
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quantitative morphology features. In past research (Rangayyan et al. 2000; Shen et al.
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2007a; Nie et al. 2008; Chang et al. 2011), a variety of morphology features have
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been suggested to estimate tumor shape, orientation, and margins. Instead of
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measuring tumors directly, the best-fit ellipse was utilized for approximating the size
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and position of a tumor (Shen et al. 2007b). By analyzing the properties of the best-fit
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ellipse, the close measurement of tumor characteristics was accomplished. For
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example, the angle of the major axis of the ellipse was used to calculate the tumor
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orientation. Comparing the ellipse perimeter and the tumor boundary helped to
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determine the smoothness of the tumor.
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Additionally, the inherent properties of tumors were utilized to develop other
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morphology features. Rangayyan et al. (2000) used the tumor perimeter and area to
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estimate the compactness of a tumor. Nie et al. (2008) described the roundness of a
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tumor by the normalized radial length (NRL), which was defined as the Euclidean
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distance between the tumor center and the pixels on the tumor boundary normalized
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by the maximum distance.
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Texture features
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Texture features were used to describe the characteristics of the specific pattern
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in a region. After segmentation, the tumor region is defined as the pixels surrounded
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by the tumor contour. Therefore, the analyses of the pixel values inside the tumor
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region were used as the texture features. In B-mode images, different tissues inside
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the tumor reflect different echogenicity patterns that result in various distributions of
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gray-level values. GLCM (Haralick et al. 1973), which calculates the spatial
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correlations among pixels, was used in this study to provide the texture information
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inside the tumors. Furthermore, the average intensity of the tumor was compared with
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that of surrounding tissues and posterior shadowing to describe the tumor
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characteristics (Shen et al. 2007b). In Table 2, a total of 38 quantitative features
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mentioned above were collected for predicting the likelihood of tumor malignancy in
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a classifier.
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Statistical analysis
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The quantitative features mentioned above were implemented in our experiment
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to distinguish between benign and malignant lesions. For this purpose, the speckle
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features were evaluated if they exhibited discriminability. According to the
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distribution of the feature values, different test methods were employed. At first, the
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distribution was determined by the Kolmogorov-Smirnov test (Field 2009). If the
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distribution of a feature was normal, then Student’s t-test (Field 2009) was applied.
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Nonnormal distributions were analyzed using the Mann-Whitney U test (Field 2009).
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The test result was quantified using p values. A p value of less than 0.05 was
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considered to be statistically significant. For classifying masses, only the significant
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features were used in the classifier.
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The classifier used in this study was the binary logistic regression model
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(Hosmer 2000). To classify the tumors as malignant or benign, the significant features
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were gathered in the classifier. Next, backward elimination was applied to select
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features from the significant features in the stepwise procedure. While the lowest error
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rate was achieved in the trained classifier, a subset of features was selected as the
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most relevant for classifying tumors. The performance of the classifier was examined
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by the leave-one-out cross-validation method (Alpaydin 2004). If there were K cases
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involved in the validation, the cases were trained K times. In every instance, one case
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was left out of the K cases and was used to test the result trained using the remaining
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K-1 cases.
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According to the biopsy-proven pathology, the classification result was
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evaluated based on five performance indices: accuracy, sensitivity, specificity, positive
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predictive value (PPV), and negative predictive value (NPV). Using the chi-square
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test, the performance indices of the speckle features, which were drawn out from
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ROIs, were compared with those of the segmentation features. Moreover, the
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trade-offs between sensitivity and specificity achieved by the two feature sets were
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compared using the receiver operating characteristic (ROC) curve. The Az value,
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which represents the normalized area under the curve, was measured for comparison
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via the z-test. ROCKIT software (C. Metz, University of Chicago, Chicago, IL, USA)
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was used to analyze the ROC curve, and all other test methods were performed with
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SPSS software (version 16 for Windows; SPSS, Chicago, IL, USA).
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Results
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For detecting speckle pixels, the performances of different window sizes
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were calculated and listed in Table 3. The detected speckle pixels were zero in
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most cases while using window sizes bigger than 9 × 9. Therefore, the
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performances of 3 × 3, 5 × 5, and 7 × 7 were compared. Although the differences
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were not significant, the result achieved by 5 × 5 was better than 3 × 3 and 7 × 7.
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We adopted 5 × 5 as the window size for the following calculations.
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To evaluate the effect of speckle reducing techniques, the speckle features
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extracted from conventional US and SCI were compared. Figure 2 (a) shows an ROI
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of a B-mode image generated by conventional US, and Fig. 2 (c) shows the identical
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case generated by SCI. The extracted speckle pixels of the ROIs are depicted with a
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white appearance in Fig. 2 (b) and (d). The illustration reveals that SCI reduced a little
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speckle in the B-mode images.
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By Student’s t-test or the Mann-Whitney U-test, the speckle features and the
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segmentation features were evaluated to determine whether they were significant in
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distinguishing between benign and malignant lesions. The significant speckle features
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and the significant segmentation features are shown in Table 4 and Table 5,
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respectively. Next, the significant features were used in the classifier to predict
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whether the tumors are benign or malignant. In Table 6, the performances achieved by
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different feature sets are listed. First, the differences of all performance indices
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between the speckle features extracted from conventional US and SCI are not
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significant (p>0.05). Note that the significant speckle features listed in Table 4 were
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evaluated based on SCI. For conventional US, the number of significant speckle
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features is one less than SCI (Inertia ave). In further comparisons, the speckle features
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extracted from ROIs with SCI were used.
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Using the chi-square test, the differences in the five performance indices
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between the speckle features and the segmentation features are not significant
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(p>0.05). Significant differences were observed in the comparison of Az using the
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z-test (p=0.0359). The performance of the combined feature set including the speckle
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features and the segmentation features was also calculated. This comparison showed
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that the Az of the combined feature set was slightly better than the Az of the speckle
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features and was significantly better than the Az of the segmentation features
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(p=0.0219). To illustrate, the ROC curves of the speckle features extracted from
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conventional US and SCI are shown in Fig. 3 (a). In Fig. 3 (b), the ROC curves of the
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speckle features with SCI, the segmentation features, and the combined feature set are
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presented.
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Two cases are illustrated to show the performance of the speckle features. The
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malignant tumor in Fig. 4, which has an S_avgnum value of 0.13, was classified
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correctly by the speckle features and was misclassified by the segmentation features.
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Another example of a benign tumor is shown in Fig. 5. A tumor with an S_avgnum
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value of 0.17 was classified correctly by the speckle features and was misclassified by
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the segmentation features.
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In order to refine the features, the cases misdiagnosed by the speckle features
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were analyzed in terms of margin, echogenicity, calcification, and shadowing.
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Echogenicity was related to the misclassification. First, if the surrounding tissues of a
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tumor included in an ROI were hypoechogenic, the image composition may affect the
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classification result. Second, while a malignant tumor and the surrounding tissues had
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similar levels of isoechogenicity, the tumor was regarded as benign. Tumor margin,
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calcification, and shadowing did not affect the performance.
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Discussion
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Various US CAD systems were developed based on the features used by
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radiologists on clinical examination. The features can be observed by human vision
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and thus are used to describe the appearances of tumors, such as their shape,
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orientation, and margin. In these CAD systems, the feature extraction and
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quantification procedure, which affects the final performance of the classification, is
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highly dependent on the segmentation result. However, the computation required in a
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segmentation procedure is considerable, and it is inevitable that a well-segmented
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result will be necessary.
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With respect to the signal features suggested in other research studies, the
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analysis of the scatterer number density from the backscattered echo is also useful in
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classifying tumors (Shankar et al. 2003; Chang et al. 2010). Nevertheless, general US
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scanners are not designed to obtain the signal data conveniently. In this study, the
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speckle pixels in common B-mode images were extracted from ROIs to utilize the
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scatterer properties. The first-order and second-order statistics of the extracted speckle
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pixels were quantified into the features for tumor classification and were compared
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with the features from segmentation. To the best of our knowledge, this study
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represents the first attempt to use the speckle density and the first order statistics of
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speckle pixels in common B-mode images for breast tumor classification.
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Furthermore, GLCM texture features (Haralick et al. 1973) were utilized in this
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study. Conventionally, texture features were used to characterize the correlations
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among pixels inside the tumor. In our CAD system, only the speckle pixels
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detected in a ROI were considered. The diagnostic accuracy of proposed speckle
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features was better than the accuracy of the Nakagami parameter in the
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literature (89% vs. 82%)(Chang et al. 2010). Considering the trade-offs between
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sensitivity and specificity, the proposed speckle features was also better in the
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comparison of Az (0.93 vs. 0.81). The proposed speckle features were based on
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B-mode image which is the basic function of widely available US scanners. The
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convenience of the proposed CAD system can make it be widely used for every
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common US scanner. We obtained SCI and conventional images with and without
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the use of the scanner’s SonoCT feature.
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In our study, the speckle features extracted from B-mode images obtained by
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using both conventional US and SCI achieved good performance (Az=0.89, Az=0.93),
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thereby suggesting that the speckle features can be used in these two settings of the
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US scanner. For comparison with the segmentation features, the speckle features
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obtained with SCI were used. The reason for this choice is that a relatively large
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amount of noise appeared in the conventional US and caused the failure of tumor
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segmentation. In comparison, the performance of five indices achieved by the speckle
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features obtained with SCI is close to the performance achieved by the segmentation
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features. Considering the trade-offs between sensitivity and specificity, the Az of the
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speckle features is significantly better than the Az of the segmentation features (0.93
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vs. 0.86, p=0.0359). By combining the speckle features and the segmentation features,
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the combined feature set also performed significantly better than the segmentation
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features (p=0.0219). The result indicates that the proposed speckle features provide
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equal or better diagnostic information than the conventional segmentation features.
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For the likelihood of malignancy, the combined feature set can determine the
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classification result with greater accuracy. That is, incorporating the morphological
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properties of a tumor with the underlying scatterer characteristics improves the
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reliability of the CAD system.
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For clinical use, the proposed CAD system, which employs the speckle features
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to classify tumors, is expected to be a more efficient procedure than tumor
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segmentation. In our experiment, calculating the speckle features of a total of 137
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cases took 121 seconds using a computer with an Intel® Core™2 Quad CPU Q9400
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at 2.66 GHz 2.67 GHz and 3.25 GB memory (Intel, Santa Clara, CA, USA). After
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acquiring the ROIs, the average processing time for the proposed CAD system to
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classify each case was 0.88 seconds. In clinical examination, the speckle density can
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immediately be displayed visually by a white appearance on the screen. The suggested
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classification result can be shown almost in real-time while the radiologists specify
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the tumor area. Generally, the routine procedure for radiologists to mark the tumor
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size on a B-mode image is sufficient to specify the tumor area. The extra time for the
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processing of the underlying CAD system would not be noticeable.
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The limitation of this study is that the B-mode images were acquired from one
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US scanner (ATL HDI 5000). In our experiment, we have shown the difference
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between conventional US and SCI for the proposed speckle features. The result
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demonstrated that the performances achieved by these two techniques were very
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close. The B-mode images used in the experiment were acquired from the
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manipulation of the experienced radiologist (15 years). For each patient, the
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adaptive configuration of the US scanner was customized for generating each US
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image. Similarly, the proposed CAD system can be adjusted for individual US
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systems. By training the up-to-date data, the classifier generates the customized
393
parameters for optimizing performance. So far, the available image data
394
collected from the clinical examinations was obtained from the US scanner.
395
Applying the proposed CAD system to different US platforms will be
396
investigated in the future. Another method for showing the advantage of the
397
proposed CAD system would be to collect various nonmass types of lesions as the
18
398
specimens for diagnosis. Nonmass lesions are abnormal tissues without distinct
399
boundaries. Because of the indistinct boundary between the lesion and its background
400
tissues, it is not practical to utilize segmentation for extracting features. Lesions that
401
are close to a nipple or that have posterior shadowing are the specimens that we will
402
use to evaluate the proposed CAD system.
403
404
Acknowledgments
405
The authors would like to thank the National Science Council of the Republic of
406
China and National Taiwan University for financially supporting this research under
407
Contract No. NSC 98-2221-E-002 -172 -MY3 and 10R80919-6. This study was also
408
supported by a grant from the Innovative Research Institute for Cell Therapy,
409
Republic of Korea (A062260).
410
411
19
412
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413
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Shen WC, Chang RF, Moon WK, Chou YH, Huang CS. Breast ultrasound
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462
463
Figure Captions
464
465
Fig. 1 Illustration of the ROI selection and speckle extraction. (a) A tumor is selected
466
by drawing an ROI from the B-mode image. (b) The ROI of a benign tumor. (c)
467
The extracted speckle pixels inside the ROI of (b) are shown by a white
468
appearance. (d) The ROI of a malignant tumor. (e) The extracted speckle
469
pixels inside the ROI of (d) are indicated by a white appearance.
470
Fig. 2 The comparison between conventional US and SCI for speckle pixels. (a) The
471
ROI of a B-mode image with conventional US. (b) The extracted speckle
472
pixels inside the ROI of (a) are shown by a white appearance. (c) The ROI of a
473
B-mode image with SCI. (d) The extracted speckle pixels inside the ROI of (c)
474
are indicated by a white appearance.
475
Fig. 3 The ROC curves of feature sets. (a) The ROC curves of the speckle features
476
with conventional US and with SCI. (b) The ROC curves of the speckle
477
features with SCI, segmentation features, and the combined feature set. Note
478
that the combined feature set includes the speckle features with SCI and the
479
segmentation features.
480
Fig. 4 A malignant tumor that was classified correctly by the speckle features but was
23
481
misclassified by the segmentation features. (a) The ROI of the tumor. (b) The
482
S_avgnum of the tumor is relatively small (0.13). (c) The segmentation result
483
of the tumor.
484
Fig. 5 A benign tumor that was classified correctly by the speckle features but was
485
misclassified by the segmentation features. (a) The ROI of the tumor. (b) The
486
S_avgnum of the tumor is relatively large (0.17). (c) The segmentation result
487
of the tumor.
488
24
489
490
Table 1 21 speckle features.
Category
Feature
Description
First order statistics
S_avgnum
The average number
speckle pixels in a ROI
S_mean, S_SD
The mean and SD of the
qualified mean/SD in a
moving window
S_gmean, S_gSD
The mean and SD of speckle
of
pixels in 256 gray-level
Second order statistics
Energy ave., Energy std.,
16 GLCM texture features
Entropy ave., Entropy std.,
Correlation ave., Correlation std.,
Inverse Difference Moment ave.,
Inverse Difference Moment std.,
Inertia ave., Inertia std.,
Cluster Shade ave.
Cluster Shade std.,
(Haralick et al. 1973)
Cluster Prominence ave.,
Cluster Prominence std.,
Haralick Correlation ave.,
Haralick Correlation std.
491
492
Table 2 38 segmentation features.
Category
Feature
Description
Morphology
Tumor_a, Tumor_p
Tumor area and perimeter
Ellipse_a,
The length of the major axis of the best-fit
ellipse
Ellipse_b,
The length of the minor axis
Ellipse_a/b,
Ellipse_a / Ellipse_b
Ep/Tp,
The ratio of the ellipse perimeter and the tumor
perimeter
25
Texture
Ellipse_compactness,
The overlap between the ellipse and the tumor
Ellipse_theta
The angle of the major axis of the ellipse (Shen
et al. 2007a)
NRL entropy, NRL variance
NRL features(Nie et al. 2008)
Compactness
Tumor roundness(Rangayyan et al. 2000)
Undulation, Sharp, MU
Features about undulations on the tumor
boundary(Shen et al. 2007b)
NS
The number of spicules on the tumor boundary
MNS
NS×Compactness
MaxSpicule
The length of the longest spicule of NS
LB
The average intensity difference between the
inner and outer bands around the tumor
boundary (Shen et al. 2007b)
PS
The average intensity difference between the
tumor and the region under the tumor (Shen et
al. 2007b)
PS_diff
The average intensity difference between the
surrounding tissues and the region under the
tumor
EPc
The average intensity difference between the
25% brighter pixels and whole tumor pixels
(Shen et al. 2007b)
EP_diff
The average intensity difference between the
tumor and the surrounding tissues
16 GLCM texture features (Haralick et al.
1973)
Energy ave., Energy std.,
Entropy ave., Entropy std.,
Correlation ave., Correlation std.,
Inverse Difference Moment ave.,
Inverse Difference Moment std.,
Inertia ave., Inertia std.,
Cluster Shade ave.
Cluster Shade std.,
Cluster Prominence ave.,
Cluster Prominence std.,
Haralick Correlation ave.,
26
Haralick Correlation std.
493
494
Table 3 The comparison of different window sizes for detecting speckle pixels.
Accuracy
Sensitivity
Specificity
PPV
NPV
Az
495
3×3
5×5
7×7
≥9×9
83.9%
(115/137)
73.8%
(31/42)
88.4%
(84/95)
73.8%
(31/42)
88.4%
(84/95)
0.89
89.1%
(122/137)
81.0%
(34/42)
92.6%
(88/95)
82.9%
(34/41)
91.7%
(88/96)
0.93
84.7%
(116/137)
76.2%
(32/42)
88.4%.
(84/95)
74.4%
(32/43)
89.4%
(84/94)
0.89
N/A
5×5
VS.
3×3
(p-value)
0.2160
5×5
VS.
7×7
(p-value)
0.2833
N/A
0.4340
0.5949
N/A
0.3217
0.3217
N/A
0.3136
0.3421
N/A
0.4537
0.5875
N/A
0.0657
0.0762
N/A: not available.
496
497
Table 4 The mean, standard deviation (SD), median, and p-value (Student’s t-test or
498
Features
S_avgnum
S_mean
S_SD
S_gmean
S_gSD
Mann-Whitney U-test) of significant speckle features with SCI.
Benign
Mean±SD
0.17±0.05
1.01±0.01
Median
Malignant
Mean±SD
0.10±0.05
1.03±0.01
0.114
64.07±17.82
25.52±5.57
Median
0.110
45.83±18.48
17.66±3.94
27
p-value
<0.001*
<0.001*
<0.001*
<0.001*
<0.001*
Energy ave.
Energy std.
Entropy ave.
Correlation ave.
Correlation std.
Inverse Difference Moment ave.
Inertia ave.
Inertia std.
Cluster Shade ave.
Cluster Shade std.
Cluster Prominence ave.
Cluster Prominence std.
Haralick Correlation ave.
Haralick Correlation std.
499
0.02
0.006
5.76±0.38
0.04
0.009
5.16±0.44
0.08
0.003
0.55±0.01
1.82±0.19
0.69±0.1
0.17
0.013
0.58±0.01
1.57±0.17
0.57±0.1
121.95
13.14
4399.12
333.57
33598.55
1164.68
40.23
4.02
909.71
79.64
9968.95
418.58
<0.001*
0.04*
<0.001*
<0.001*
<0.001*
<0.001*
<0.001*
<0.001*
0.002*
<0.001*
<0.001*
<0.001*
<0.001*
<0.001*
* p-value<0.05 indicates a statistically significant difference.
500
501
Table 5 The mean, standard deviation (SD), median, and p-value (Student’s t-test or
502
Mann-Whitney U-test) of significant segmentation features.
Features
Tumor_a
Tumor_p
Ellipse_a
Ellipse_b
Ellipse_a/b
Ep/Tp
Ellipse_theta
NRL entropy
NRL variance
Undulation
Sharp
MU
MNS
LB
EPc
EP_diff
Energy ave.
Entropy std.
Correlation std.
Inverse Difference Moment ave.
Inverse Difference Moment std.
Inertia ave.
Benign
Mean±SD
Median
4676
344
57.05±22.22
Malignant
Mean±SD
p-value
Median
12839
616
70.92±23.73
30.92
1.63
0.79±0.09
54.77
1.34
0.67±0.10
0.08
2.56±0.37
0.14±0.03
0.20
2.31±0.42
0.11±0.03
2
2
5
1.69±0.73
34.91±8.95
4
3.5
7.5
2.15±1.06
26.15±8.97
0.41
36.05±9.27
0.64
28.88±9.09
0.08
0.20±0.03
0.09
0.18±0.03
0.02
0.72±0.04
0.05±0.01
0.80±0.24
0.01
0.75±0.05
0.04±0.01
0.67±0.23
28
<0.001*
<0.001*
<0.001*
<0.001*
<0.001*
<0.001*
0.002*
<0.001*
<0.001*
<0.001*
<0.001*
<0.001*
0.01*
<0.001*
0.004*
<0.001*
0.02*
<0.001*
0.04*
<0.001*
<0.001*
0.003*
Inertia std.
Cluster Shade ave.
Haralick Correlation ave.
Haralick Correlation std.
503
0.23±0.08
0.18±0.08
9.60
3529.22
54.12
17.18
2166.20
27.39
<0.001*
0.005*
0.01*
<0.001*
* p-value<0.05 indicates a statistically significant difference.
504
505
Table 6 The comparison of five performance indices and p-values (chi-square test)
506
Accuracy
Sensitivity
Specificity
PPV
NPV
Az
between the speckle features and the segmentation features.
Speckle
(with SCI)
Speckle
(with
convention
al US)
Segmentat
ion
Combined
89.1%
(122/137)
81.0%
(34/42)
92.6%
(88/95)
82.9%
(34/41)
91.7%
(88/96)
0.93
88.3%
(121/137)
78.6%
(33/42)
92.6%
(88/95)
82.5%
(33/40)
90.7%
(88/97)
0.89
84.7%
(116/137)
73.8%
(31/42)
89.5%.
(85/95)
75.6%
(31/41)
88.5%
(85/96)
0.86
89.8%
(123/137)
81.0%
(34/42)
93.7%
(89/95)
85.0%
(34/40)
91.8%
(89/97)
0.94
Speckle
(with SCI)
VS.
Speckle
(with
convention
al US)
(p-value)
0.8487
Speckle
(with SCI)
VS.
Segmentat
ion
(p-value)
Combined
VS.
Speckle
(with SCI)
(p-value)
Combined
VS.
Segmentat
ion
(p-value)
0.2833
0.8443
0.2052
0.7860
0.4340
1.0000
0.4340
1.0000
0.4458
0.7738
0.2960
0.9595
0.4138
0.7994
0.2886
0.8168
0.4684
0.9827
0.4541
0.2513
0.0359*
0.9573
0.0219*
507
* p-value<0.05 indicates a statistically significant difference.
508
Combined feature set includes the speckle features with SCI and the segmentation
509
features.
29