计算机辅助鉴别乳腺良、恶性肿块的临床价值

目的 提出一种基于灰度级二维直方图的计算机辅助分割算法,对乳腺高频超声图像中的肿块进行自动识别和分割处理,旨在提高乳腺良、恶性肿块的检出准确率.方法 采集100个乳腺肿块二维超声图像共466张(原片),应用计算机软件对原片进行分割处理,得到分割后图片.超声医师采用双盲法分别根据原片和分割后图片中的超声征象进行良、恶性判断,运用受试者工作曲线(ROC曲线)计算曲线下面积(A),比较前后两次诊断结果 ,分析图片处理前后诊断结果 的差异性.结果 处理后的图片中乳腺肿块的边缘、钙化等信息明显突出.超声医师对良、恶性肿块的确诊率明显提高.当特异性为74.31%时,诊断敏感性由基于原片的70.32%提高到图片分割后的90.52%.ROC曲线下面积由分割前的80.8%上升到90.5%,差异有统计学意义(P<0.01).结论 此分割算法能明显优化乳腺肿块的边缘信息,较好地突显肿块内微钙化,在一定程度上降低漏诊率和误诊率,提高乳腺良恶性肿块的确诊率.     

Computer-aided diagnosis for the classification of breast masses in automated whole breast ultrasound images

New automated whole breast ultrasound (ABUS) machines have recently been developed and the ultrasound (US) volume dataset of the whole breast can be acquired in a standard manner. The purpose of this study was to develop a novel computer-aided diagnosis system for classification of breast masses in ABUS images. One hundred forty-seven cases (76 benign and 71 malignant breast masses) were obtained by a commercially available ABUS system. Because the distance of neighboring slices in ABUS images is fixed and small, these continuous slices were used for reconstruction as three-dimensional (3-D) US images. The 3-D tumor contour was segmented using the level-set segmentation method. Then, the 3-D features, including the texture, shape and ellipsoid fitting were extracted based on the segmented 3-D tumor contour to classify benign and malignant tumors based on the logistic regression model. The Student’s t test, Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. From the Az values of ROC curves, the shape features (0.9138) are better than the texture features (0.8603) and the ellipsoid fitting features (0.8496) for classification. The difference was significant between shape and ellipsoid fitting features (p = 0.0382). However, combination of ellipsoid fitting features and shape features can achieve a best performance with accuracy of 85.0% (125/147), sensitivity of 84.5% (60/71), specificity of 85.5% (65/76) and the area under the ROC curve Az of 0.9466. The results showed that ABUS images could be used for computer-aided feature extraction and classification of breast tumors. (E-mail: rfchang@csie.ntu.edu.tw)     

CAD Algorithms for Solid Breast Masses Discrimination: Evaluation of the Accuracy and Interobserver Variability

For a successful computer-aided diagnosis (CAD) approach, investigating the benefit of the output for radiologist diagnosis is as important as developing the computer algorithm itself. To evaluate the accuracy and the interobserver variability of two newly developed CAD algorithms for breast mass discrimination, eight radiologists with varied experience in breast ultrasonography (US) independently reviewed the lesions according to Breast Imaging Reporting and Data System (BI-RADS)-US. They interpreted the original ultrasound images, provided a final assessment category to indicate the probability of malignancy and then made a further diagnosis using the images processed by the proposed CAD algorithms. The receiver operating characteristic (ROC) curve and Cohen's κ statistics were employed to evaluate the effect of the CAD algorithms on radiologist diagnoses. By using the proposed CAD approach, the quality of the images was improved and more information was provided to the observers. With the processed images, the areas under the ROC (Az) of each reader (0.86∼0.89) were greater than those with the original ultrasound images (0.81∼0.86) and all the radiologists improved their performance significantly (p < 0.05) except two senior radiologists (p > 0.05). The Az values of the junior radiologists with CAD were comparable to those of the senior radiologists. Cohen's κ statistics showed that better interobserver agreement was obtained by using the processed images. We conclude that the proposed CAD method is more helpful for the junior radiologists than for the senior ones and it also showed the advantage of decreasing interobserver variability. (E-mail: jwtian2004@yahoo.com.cn)     

Analysis of Elastographic and B-mode Features at Sonoelastography for Breast Tumor Classification

The purpose of this study was to evaluate the accuracy of neural network analysis of elastographic features at sonoelastography for the classification of biopsy-proved benign and malignant breast tumors. Sonoelastography of 181 solid breast masses (113 benign and 68 malignant tumors) was performed for 181 patients (mean age, 47 years; range, 24–75 years). After the manual segmentation of the tumors, five elastographic features (strain difference, strain ratio, mean, median and mode) and six B-mode features (orientation, undulation, angularity, average gradient, gradient variance and intensity variance) were computed. A neural network was used to classify tumors by the use of these features. The Student's t test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. Area under ROC curve (Az) values of the three elastographic features– mean (0.87), median (0.86) and mode (0.83)–were significantly higher than the Az values for the six B-mode features (0.54–0.69) (p < 0.01). Accuracy, sensitivity, specificity and Az of the neural network for the classification of solid breast tumors were 86.2% (156/181), 83.8% (57/68), 87.6% (99/113) and 0.84 for the elastographic features, respectively, and 82.3% (149/181), 70.6% (48/68), 89.4% (101/113) and 0.78 for the B-mode features, respectively, and 90.6% (164/181), 95.6% (65/68), 87.6% (99/113) and 0.92 for the combination of the elastographic and B-mode features, respectively. We conclude that sonoelastographic images and neural network analysis of features has the potential to increase the accuracy of the use of ultrasound for the classification of benign and malignant breast tumors. (E-mail: rfchang@csie.ntu.edu.tw)     

Automatic selection of representative slice from cine-loops of real-time sonoelastography for classifying solid breast masses

This study aimed to evaluate the performance of automatic selection of representative slice from cine-loops of real-time sonoelastography for classifying benign and malignant breast masses. This retrospective study included 141 ultrasound elastographic studies (93 benign and 48 malignant masses). A novel computer-assisted system was developed for the automatic segmentation of the targeted lesion from cine-loops of real-time sonoelastography. Its hard ratio, defined as the ratio of the number of hard pixels within the tumor divided by the total number of pixels of the whole tumor, was also calculated. The targeted mass was segmented by edge-detection and region growing methods, with combined motion registration after manually defining the original seed. Signal-to-noise ratio (SNR_e) and contrast-to-noise ratio (CNR_e) of ultrasound elastogram were computed to obtain an optimum slice for differentiating benign and malignant lesions. The diagnostic results of automatic slice selection using maximum strain, maximum SNR_e, maximum CNR_e, maximum compression and the slices selected by radiologists were compared. Mann-Whitney U test, performance indexes and receiver operating characteristic (ROC) curves were used for statistical analysis. Performance using the maximum SNR_e (accuracy 84.4%, sensitivity 83.3%, specificity 85.0% and A_z value 0.90) was the best as compared with those of maximum CNR_e (82.3%, 79.2%, 83.9% and 0.88, respectively), maximum compression (78.0%, 79.2%, 77.4% and 0.85, respectively), maximum strain (79.4%, 79.2%, 79.6% and 0.87, respectively) and radiologists’ selection (77.3%, 77.1%, 77.4% and 0.80, respectively). Automatic selection of representative slice from the cine-loops of real-time sonoelastography is a practical, objective and accurate approach for classifying solid breast masses.(E-mail: moonwk@radcom.snu.ac.kr and rfchang@csie.ntu.edu.tw)     

应用去噪后增强算法辅助乳腺肿块的超声诊断研究

目的探讨超声图像去噪后增强算法对乳腺肿块良恶性检测与分类的价值。方法选用211例603幅乳腺肿块超声图片(良性109例,恶性102例)进行去噪后增强处理,以手术病理结果作为金标准,对乳腺肿块原始图片和处理后图片进行分析,来区分乳腺肿块的良、恶性。利用ROC曲线下面积表现去噪后增强前后的诊断性能,计算超声诊断的准确率。结果通过去噪后增强算法处理后,使腺体和周围组织能分离,突出了腺体和病灶的部位,细节显示更加清晰,超声与病理诊断的各项指标符合率明显提高,准确率提高至92.73%,原片与处理后图片ROC曲线下面积二者之间差异有显著性统计学意义(P0.001)。结论新的超声图像去噪后增强算法可明显地改善了图像质量,提高了乳腺肿块的正确诊断率。     

1000-Case Reader Study of Radiologists' Performance in Interpretation of Automated Breast Volume Scanner Images with a Computer-Aided Detection System

The objective of our study was to assess, in a reader study, radiologists' performance in interpretation of automated breast volume scanner (ABVS) images with the aid of a computer-aided detection (CADe) system. Our study is a retrospective observer study with the purpose of investigating the effectiveness of using a CADe system as an aid for radiologists in interpretation of ABVS images. The multiple-reader, multiple-case study was designed to compare the diagnostic performance of radiologists with and without CADe. The study included 1000 cases selected from ABVS examinations in our institution in 2012. Among those cases were 206 malignant, 486 benign and 308 normal cases. The cancer cases were consecutive; the benign and normal cases were randomly selected. All malignant and benign cases were confirmed by biopsy or surgery, and normal cases were confirmed by 2-y follow-up. Reader performance was compared in terms of area under the receiver operating characteristic curve, sensitivity and specificity. Additionally, the reading time per case for each reader was recorded. Nine radiologists from our institution participated in the study. Three had more than 8 y of ultrasound experience and more than 4 y of ABVS experience (group A); 3 had more than 5 y of ultrasound experience (group B), and 3 had more than 1 y of ultrasound experience (group C). Both group B and group C had no ABVS experience. The CADe system used was the QVCAD System (QView Medical, Inc., Los Altos, CA, USA). It is designed to aid radiologists in searching for suspicious areas in ABVS images. CADe results are presented to the reader simultaneously with the ABVS images; that is, the radiologists read the ABVS images concurrently with the CADe results. The cases were randomly assigned for each reader into two equal-size groups, 1 and 2. Initially the readers read their group 1 cases with the aid of CADe and their group 2 cases without CADe. After a 1-mo washout period, they re-read their group 1 cases without CADe and their group 2 cases with CADe. The areas under the receiver operating characteristic curves of all readers were 0.784 for reading with CADe and 0.747 without CADe. Areas under the curves with and without CADe were 0.833 and 0.829 for group A, 0.757 and 0.696 for group B and 0.759 and 0.718 for group C. All differences in areas under the curve were statistically significant (p?<0.05), except that for group A. The average reading time was 9.3% (p?<?< 0.05) faster with CADe for all readers. In summary, CADe improves radiologist performance with respect to both accuracy and reading time for the detection of breast cancer using the ABVS, with the greater benefit for those inexperienced with ABVS.     

影像学诊断评价中的参数法ROC曲线分析

目的 探讨采用ROC曲线参数分析法对影像学分类诊断结果进行评价的价值,并介绍ROC曲线参数分析软件ROCKIT。 方法 2名医师分别对60幅肺部CT图像进行肺结节良恶性5级分类诊断。分别用ROCKIT软件和SPSS软件对他们的诊断结果进行参数法和非参数法ROC曲线分析。 结果 对2名医师的诊断结果利用ROCKIT进行参数法ROC分析时,ROC曲线下面积分别为0.940±0.039和0.785±0.075(Z=2.056, P=0.040),利用SPSS进行非参数估计时结果分别为0.913±0.042和0.771±0.075。通过ROCKIT软件可绘制光滑的拟合ROC曲线,SPSS软件可绘制不光滑的经验ROC曲线。 结论 当有序分类资料样本量适中时,参数估计一般均无偏倚,非参数估计的结果可能小于真实值;ROCKIT软件是双正态参数法ROC曲线分析的有力工具。     

Multiple Resolution Bayesian Segmentation of Ultrasound Images

We propose a novel method for obtaining the maximum a posteriori (MAP) probabilistic segmentation of speckle-laden ultrasound images. Our technique is multiple-resolution based, and relies on the conversion of speckle images with Rayleigh statistics to subsampled images with Gaussian statistics. This conversion reduces computation time, as well as allowing accurate parameter estimation for a probabilistic segmentation algorithm. Results appear to provide improvements over previous techniques in terms of low-contrast detail and accuracy.     

Principal component regression-based contrast-enhanced ultrasound evaluation system for the management of BI-RADS US 4A breast masses: objective assistance for radiologists

A portion of detected breast masses might be overrated by using the Breast Imaging-Reporting and Data System ultrasonography (BI-RADS US) lexicon. A principal component regression-based contrast-enhanced ultrasound (PCR-CEUS) evaluation system was built to quantitatively illustrate whether CEUS could help radiologists to differentiate 4A masses. The PCR-CEUS evaluation system, based on principal component analysis (PCA) and logistic regression, was verified by random assignment into training and test sets and shown to reduce the data dimension and avoid collinearity in CEUS variables. This prospective study consecutively collected 238 patients with 238 4A masses confirmed pathologically. All enrolled patients accepted CEUS examination. The diagnostic performance of senior and junior radiologists, PCR-CEUS and combined methods was compared. The PCR-CEUS system had consistent diagnostic performance in both the training and test sets, with an area under the curve (AUC) of 0.831 (0.765-0.897), 0.798 (0.7034-0.892) and 0.854 (0.765-0.943) (all P > 0.05). The AUC of the combined diagnostic model (PCR-CEUS + Senior radiologists) was higher than that of senior radiologists, and the combined model had higher sensitivity (0.875 (0.781-0.969) vs. 0.729 (0.603-0.855)) without compromising specificity. Furthermore, the AUC and specificity of the combined model (PCR-CEUS + Junior radiologists) (0.852 (0.787-0.916)) was higher than that of junior radiologists (0.665 (0.592-0.737) (P < 0.00001)). PCR-CEUS demonstrated good ability in differentiating malignant BI-RADS-US 4A masses and was helpful for both senior and junior radiologists.     

Automating Spine Curvature Measurement in Volumetric Ultrasound via Adaptive Phase Features

Ultrasound volume projection imaging (VPI) has been recently suggested. This novel imaging method allows a non-radiation assessment of spine deformity with free-hand 3-D ultrasound imaging. This paper presents a fully automatic method to evaluate the spine curve in VPI images corresponding to different projection depth of the volumetric ultrasound, thus making it possible to analyze 3-D spine deformity. The new automatic method is based on prior knowledge about the geometric arrangement of the spinous processes. The frequency bandwidth of log-Gabor filters is adaptively adjusted to calculate the oriented phase congruency, facilitating the segmentation of the spinous column profile. And the spine curvature angle is finally calculated according to the inflection points of the curve over the segmented spinous column profile. The performance of the automatic method is evaluated on spine VPI images among patients with different scoliotic angles. The curvature angles obtained using the proposed method have a high linear correlation with those by the manual method (r = 0.90, p < 0.001) and X-ray Cobb's method (r = 0.87, p < 0.001). The feasibility of 3-D spine deformity assessment is also demonstrated using VPI images corresponding to various projection depth. The results suggest that this method can substantially improve the recognition of the spinous column profile, especially facilitating the applications of 3-D spine deformity assessment.     

数字胸片多频域后处理对计算机辅助检测系统肺结节检测的影响

目的评价数字胸片多频域后处理对计算机辅助检测(CAD)系统肺结节检出的影响。方法对经CT证实的54例肺结节病例及54例正常者的数字胸片选择三种不同的structure preference参数进行多频域后处理,在1次曝光的条件下各得到标准图像、高通过图像及低通过图像三组图像,然后,采用计算机辅助检测系统(IQQATM-Chest V1.2)进行阅片,首先由2名观察者共同根据CT结果客观分析并记录CAD检出的结节数及假阳性数。其次,由另外4名观察者(高年资及低年资放射医师各2名)应用CAD系统所提供的肺结节的智能质量和数量分析功能,对CAD输出图像独立进行分析,并记录检出结节数及假阳性数,采用受试者操作特征(ROC)曲线分析观察结果。结果三组图像中,低通过组图像的平均ROC曲线下面积最大,人-机交互前、高年资及低年资人-机交互后的平均ROC曲线下面积分别为0.77、0.81、0.80。高通过组图像的平均ROC曲线下面积为最小,人-机交互前、高年资及低年资人-机交互后的平均ROC曲线下面积分别为0.57、0.67、0.71。三组图像的平均ROC曲线下面积存在差异(P<0.01)。结论数字胸片多频域后处理影响计算机辅助检测系统肺结节的检出。     

Characterization of Benign and Malignant Solid Breast Masses: Harmonic Versus Nonharmonic 3D Power Doppler Imaging

The aim of this study was to compare the diagnostic performance of nonharmonic ultrasound (US) and tissue harmonic imaging (THI) using three-dimensional (3D) power Doppler sonographic technique to classify benign and malignant breast tumors by vascularization. From January 2003 to February 2004, we evaluated 200 patients and one of lobular carcinoma in situ was excluded from the malignant category. One hundred and ninety-nine subjects were enrolled. All subjects with one or more breast masses were studied with 3D power Doppler US nonharmonic and harmonic technologies. Sixteen of 199 subjects were excluded because masses exceeded 3 cm limit of our US probe's footprint (n = 5) or no harmonic Doppler information (n = 11). A total of 97 benign and 86 pathologically proven malignant breast tumor images were analyzed. 3D power Doppler US imaging was performed using a Voluson730 US system. Three histogram indices, the vascularization index (VI), flow index (FI) and vascularization-flow index (VFI), on both nonharmonic and harmonic images were calculated for the intratumor and for shells with an outside thickness of 3 mm surrounding the breast lesion. A multilayer perception (MLP) neural network classifier used the vascularity indices to determine whether the breast tumors are benign or malignant. The receiver operating characteristic (ROC) curves are performed to estimate the diagnostic performances for nonharmonic and harmonic methods. ROC curve analysis used overall age, volume, VI, FI and VFI for both intratumor and shells with an outside thickness of 3 mm surrounding the breast lesions in nonharmonic US and THI. The area under the ROC curve (A_Z) was 0.9086 and 0.9009 (p = 0.3770). The sensitivity was 90.7% and 83.7% (p = 0.72), respectively, and the specificity was 92.8% and 92.8% (p = 1.00), respectively. In conclusion, the performance of 3D power Doppler US with respect to the characterization of solid breast masses as benign or malignant was not significantly improved with tissue harmonic imaging. (E-mail: darren_chen@cch.org.tw)     

Multiresolution texture based adaptive clustering algorithm for breast lesion segmentation

Objective: A specific algorithm is presented for the automatic extraction of breast tumors in ultrasonic imaging. Method: The algorithm involves two-dimensional adaptive K-means clustering of the gray scale and textural feature images. The segmentation problem is formulated as a maximum a posteriori (MAP) estimation problem. The MAP estimation is achieved using Besag's iterated conditional modes algorithm for the minimization of an energy function. This function has three components: the first constrains the region to be close to the data; the second imposes spatial continuity; and the third takes into consideration the texture of the various regions. A multiresolution implementation of the algorithm is performed using a wavelets basis. Results: Experiments were carried out on synthetic images and on in vivo breast ultrasound images. Various parameters involved in the algorithm are discussed to evaluate the robustness and accuracy of the segmentation method. Conclusion: Including textural features in the segmentation of ultrasonic data improves the robustness of the algorithm and makes the segmentation result less parameter dependent.     

Prostate boundary detection and volume estimation using TRUS images for brachytherapy applications

Objective: Authors propose a semi-automatic segmentation algorithm for three-dimensional prostate boundary detection from trans-rectal ultrasound images. As a part of brachytherapy treatment with seeds for early stage prostate cancer, a patient’s prostate is scanned using a trans-rectal ultrasound probe, its boundary is manually outlined, and its volume is estimated for dosimetry purposes. Proposed algorithm requires a reduced amount of radiologist’s input, and thus speeds up the surgical procedure. Methods: The proposed segmentation algorithm utilizes texture differences between ultrasound images of the prostate and the surrounding tissues. It is carried out in the polar coordinate system and uses three-dimensional data correlation to improve the smoothness and reliability of the segmentation. The algorithm is applied to axial trans-rectal ultrasound images and the results are compared to the “ground truth” set by manual prostate boundary outlining (by experienced radiologist). Method is validated on six patients. Results: In our tests, the proposed algorithm estimated prostate volume within 95% of the original radiologist’s estimate. Conclusions: The boundary segmentation obtained from the algorithm can reduce manual input by a factor of 3, without significantly affecting the accuracy of the segmentation. The reduction in the manual input reduces the overall brachytherapy procedure time.     

Coherence-Based Beamforming Increases the Diagnostic Certainty of Distinguishing Fluid from Solid Masses in Breast Ultrasound Exams

Ultrasound is often used as a supplement for mammography to detect breast cancer. However, one known limitation is the high false-positive rates associated with breast ultrasound. We investigated the use of coherence-based beamforming (which directly displays spatial coherence) as a supplement to standard ultrasound B-mode images in 25 patients recommended for biopsy (26 masses in total), with the eventual goal of decreasing false-positive rates. Because of the coherent signal present within solid masses, coherence-based beamforming methods allow solid and fluid-filled masses to appear significantly different (p < 0.001). When presented to five board-certified radiologists, the inclusion of robust short-lag spatial coherence (R-SLSC) images in the diagnostic pipeline reduced the uncertainty of fluid-filled mass contents from 47.5% to 15.8% and reduced the percentage of fluid-filled masses unnecessarily recommended for biopsy from 43.3% to 13.3%. These results are promising for the potential introduction of R-SLSC (and related coherence-based beamforming methods) into the breast clinic to improve diagnostic certainty and reduce the number of unnecessary biopsies.     

Computer-aided image analysis of focal hepatic lesions in ultrasonography: preliminary results

Purpose  To develop a computer-aided image analysis (CAIA) algorithm for analyzing US features of focal hepatic lesions and to correlate the feature values of CAIA with radiologists’ grading. Materials and methods  Two abdominal radiologists, blinded to the final diagnosis, independently evaluated sonographic images of 51 focal hepatic lesions in 47 patients: hemangiomas (n = 19), hepatic simple cysts or cystic lesions (n = 14), hepatocellular carcinoma (n = 11), metastases (n = 6), and focal fat deposition (n = 1). All images were graded using a 3- to 5-point scale, in terms of border (roundness, sharpness, and the presence of peripheral rim), texture (echogenicity, homogeneity, and internal artifact), posterior enhancement, and lesion conspicuity. Using a CAIA, texture and morphological parameters representing radiologists’ subjective evaluations were extracted. Correlations between the radiologists and the CAIA for assessing parameters in corresponding categories were computed by means of weighted κ statistics and Spearman correlation test. Results  A good agreement was achieved between CAIA and radiologists for grading echogenicity (weighted κ = 0.675) and the presence of hyper- or hypoechoic rim (weighted κ = 0.743). Several CAIA-derived features representing homogeneity of the lesions showed good correlations (correlation coefficient (γ) = 0.603∼0.641) with radiologists’ grading (P < 0.05). For internal artifact (γ = 0.469–0.490) and posterior enhancement (γ = −0.516) of the cyst and lesion conspicuity (γ = 0.410), a fair correlation between CAIA and radiologists was obtained (P < 0.05). However, parameters representing lesions’ border such as sharpness (γ = 0.252–0.299) and roundness (γ = −0.134–0.163) showed no significant correlation (P > 0.05). Conclusion  As a preliminary step in US computer-aided diagnosis for focal hepatic lesions, a CAIA algorithm was constructed with a good agreement and correlation with human observers in most US features. In addition, these features should be weighted highly when a computer-aided diagnosis for characterizing focal liver lesions on US is designed and developed.     

Automated Skin Segmentation in Ultrasonic Evaluation of Skin Toxicity in Breast Cancer Radiotherapy

Skin toxicity is the most common side effect of breast cancer radiotherapy and impairs the quality of life of many breast cancer survivors. We, along with other researchers, have recently found quantitative ultrasound to be effective as a skin toxicity assessment tool. Although more reliable than standard clinical evaluations (visual observation and palpation), the current procedure for ultrasound-based skin toxicity measurements requires manual delineation of the skin layers (i.e., epidermis-dermis and dermis-hypodermis interfaces) on each ultrasound B-mode image. Manual skin segmentation is time consuming and subjective. Moreover, radiation-induced skin injury may decrease image contrast between the dermis and hypodermis, which increases the difficulty of delineation. Therefore, we have developed an automatic skin segmentation tool (ASST) based on the active contour model with two significant modifications: (i) The proposed algorithm introduces a novel dual-curve scheme for the double skin layer extraction, as opposed to the original single active contour method. (ii) The proposed algorithm is based on a geometric contour framework as opposed to the previous parametric algorithm. This ASST algorithm was tested on a breast cancer image database of 730 ultrasound breast images (73 ultrasound studies of 23 patients). We compared skin segmentation results obtained with the ASST with manual contours performed by two physicians. The average percentage differences in skin thickness between the ASST measurement and that of each physician were less than 5% (4.8 ± 17.8% and −3.8 ± 21.1%, respectively). In summary, we have developed an automatic skin segmentation method that ensures objective assessment of radiation-induced changes in skin thickness. Our ultrasound technology offers a unique opportunity to quantify tissue injury in a more meaningful and reproducible manner than the subjective assessments currently employed in the clinic.     

An approach to the detection of lesions in mammograms using fuzzy image processing

An algorithm was developed in this study, using rule-based fuzzy logic, to enable masses that are hard to recognize or detect in mammograms to become more readily perceptible. Small lesions, such as microcalcifications and other masses that are hard to recognize, especially on film scan mammograms, were processed through segmentation. A total of 40 mammograms were used and they were classified by radiologists into three groups: those with microcalcifications (n=15), those with tumours (n=15), and those with no lesions (n=10). Five mammograms were taken as training data sets from each of the groups with microcalcifications and tumours. The algorithm was then applied to data not taken for training. The algorithm achieved a mean accuracy of 99% compared with the findings of the radiologists.