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Model based approach for Detection of Architectural Distortions and Spiculated Masses in Mammograms  [PDF]
Minavathi,Murali. S.,M. S. Dinesh
International Journal on Computer Science and Engineering , 2011,
Abstract: This paper investigates detection of Architectural Distortions (AD) and spiculated masses in mammograms based on their physical characteristics. We have followed a model based approach which separates the abnormal patterns of AD and spiculated masses from normal breast tissue. The model parameters are retrieved from Gabor filters which characterize the texture features and synthetic patternswere generated using pplanes to retrieve specific patterns of abnormalities in mammographic images. In addition, eight discriminative features are extracted from region of interest (ROI) which describes the patterns representing AD and spiculated masses. Support vector machine (SVM) and Multi-layer Perceptrons (MLP) classifiers are used to classify the discriminative features of AD and spiculated masses from normal breast tissue. This study concentrates on classifying AD and spiculated masses from the oneswhich actually are normal breast parenchyma. Our proposal is based on the texture pattern that represents salient features of AD and spiculated mass. Once the descriptive features are extracted SVM and MLP classifiers are used. We have used receiver operating characteristic curve (ROC) to evaluate the performance and we have compared our method with several other existing methods. Our methodoutperformed other existing methods by achieving 90% of sensitivity, 86% specificity in distinguishing AD from normal breast tissue and 93% sensitivity and 88% specificity in classifying spiculated mass from normal breast parenchyma. In first stage of this study we consider ROI’s that include AD, spiculated masses and normal breast tissue as input. Our method was tested on 190 ROI’s( 19 AD , 19 spiculated mass and 152 normal breast tissue) from Mini-MIAS database and 150 ROI’s( 23 AD , 30 spiculated mass and 97 normal breast tissue ) collected from DDSM database. In the second stage we have applied SVM classification model on whole images and the performance is analyzed by plotting Free Response Operating Characteristic (FROC) curves. SVM classifiers achieved 96% sensitivity with 9.6 false positives per image in detection of spiculated mass and 97% sensitivity with 6.6 false positives per image while detecting AD in digital mammograms.
Dual Modality: Mammogram and Ultrasound Feature Level Fusion for Characterization of Breast Mass  [PDF]
Minavathi,Dr. Murali. S.,,Dr. Dinesh M.S.
International Journal of Innovative Technology and Exploring Engineering , 2013,
Abstract: detection of abnormalities in breast is done in different phases using different modalities and different biomedical techniques. These techniques and modalities are able to furnish morphological, metabolic and functional information of breast. Integrating these information assists in clinical decision making. But it is difficult to retrieve all these information from single modality. Multimodal techniques supply complementary information for improved therapy planning. This work concentrates on early detection of breast cancer which characterises the breast mass as malignant or benign by investigating the features retrieved from dual modalities: mammograms and Ultrasound. Architectural distortion (AD) with Spiculated mass is an important finding for the early detection of breast cancer. Such distortions can be classified as spiculation, retraction, and distortion which can be detected in mammograms. Spiculated masses carry a much higher risk of malignancy than calcifications or other types of masses. The proposed approach is based on the fusion of two modalities at feature extraction level with Z-Score Normalization technique to improve the performance of dual modality. Gabor filters are used to retrieve texture features from region of interest (ROI) of mammograms. Shape and structural features are retrieved from ROI’s of Ultrasound. In addition to that some other discriminative features like denseness texture feature, standard deviation, entropy and homogeneity are also extracted from ROI’s of both modalities. Feature level fusion is then achieved by using a simple concatenation rule. Finally classification is done using Support vector machine (SVM) classifiers to classify breast mass as malignant or benign. Receiver operating characteristic curves (ROC) are used to evaluate the performance. SVM classifiers achieved 95.6% sensitivity in characterising the breast masses using the features retrieved from two modalities.
How accurate is ultrasound in evaluating palpable breast masses?
MA Gonzaga
Pan African Medical Journal , 2010,
Abstract: Introduction: Breast masses have become common in women. Such masses pose a potential threat to women especially in the era of increased cases of breast cancer worldwide. Breast carcinoma ranks first among the malignant tumors affecting females in many parts of the world with the rate of breast cancer being 1 in 8 in USA. There are currently more than 600 000 cancer deaths annually in Africa. By 2020, 70% of the 15 million new annual cancer cases will be in developing countries. Ultrasound is a relatively inexpensive and readily accessible imaging modality that can be utilized in the evaluation of clinically palpable breast masses. The purpose of this study was to find out the accuracy of ultrasound in the diagnosis of palpable breast masses. Methods: Eighty palpable breast masses were evaluated at ultrasound and information about the characteristic features of the masses was recorded. An impression about the diagnosis was made and results were correlated with histology findings. Results: The overall sensitivity of ultrasound in detecting breast lumps was 92.5%. The sensitivity and specificity of ultrasound for detecting breast carcinoma was 57.1% and 62.8% respectively with a positive predictive value of 68.1%, a negative predictive value of 99.5%, a positive likelihood ratio of 39 and a negative likelihood ratio of 0.07. Ultrasound reliably differentiated cystic from solid breast masses. Conclusion: Ultrasound is significant in differentiating cystic from solid breast masses. Ultrasound is also important in detecting suspicious breast masses and should therefore be used in the evaluation of symptomatic breast masses.
How accurate is ultrasound in evaluating palpable breast masses?  [PDF]
Mubuuke Aloysius Gonzaga
Pan African Medical Journal , 2010,
Abstract: INTRODUCTION: Breast masses have become common in women. Such masses pose a potential threat to women especially in the era of increased cases of breast cancer worldwide. Breast carcinoma ranks first among the malignant tumors affecting females in many parts of the world with the rate of breast cancer being 1 in 8 in USA. There are currently more than 600 000 cancer deaths annually in Africa. By 2020, 70% of the 15 million new annual cancer cases will be in developing countries. Ultrasound is a relatively inexpensive and readily accessible imaging modality that can be utilized in the evaluation of clinically palpable breast masses. The purpose of this study was to find out the accuracy of ultrasound in the diagnosis of palpable breast masses. METHODS: Eighty palpable breast masses were evaluated at ultrasound and information about the characteristic features of the masses was recorded. An impression about the diagnosis was made and results were correlated with histology findings. RESULTS: The overall sensitivity of ultrasound in detecting breast lumps was 92.5%. The sensitivity and specificity of ultrasound for detecting breast carcinoma was 57.1% and 62.8% respectively with a positive predictive value of 68.1%, a negative predictive value of 99.5%, a positive likelihood ratio of 39 and a negative likelihood ratio of 0.07. Ultrasound reliably differentiated cystic from solid breast masses. CONCLUSION: Ultrasound is significant in differentiating cystic from solid breast masses. Ultrasound is also important in detecting suspicious breast masses and should therefore be used in the evaluation of symptomatic breast masses
Contrast enhanced ultrasound of renal masses  [cached]
Andre Ignee, Bernd Straub, Gudrun Schuessler, Christoph Frank Dietrich
World Journal of Radiology , 2010,
Abstract: Contrast enhanced ultrasound (CEUS) has gained clinical importance over the last years for the characterization of hepatic masses. Its role in extrahepatic indications has been investigated repeatedly but has been less comprehensively studied. Currently more than 50% of renal masses are incidentally diagnosed, mostly by B-mode ultrasound. The method of choice for characterization of renal lesions is contrast enhanced computed tomography (CECT). In the case of cystic lesions CECT refers to the Bosniak classification for cystic lesions to assess the risk of malignant behavior. The majority of masses are renal cell carcinoma, but the exact proportion is controversial. Disadvantages of CECT are a significant risk for patients with impaired renal function, allergic reactions and hyperthyroidism due to iodinated contrast agents. Several studies concerning CEUS for the characterization of both solid and cystic renal lesions have been published, but prospective multicenter studies are missing, the presented data being mainly descriptive. The aim of the this manuscript is to review the current literature for CEUS in renal masses, to summarize the available data and focus on possible concepts for studies in the future.
Sensitivity of Ultrasound-guided Vs. Free-hand Fine Needle Aspiration Biopsy in palpable Breast Masses
T. Beheshtian
Iranian Journal of Radiology , 2005,
Abstract: Introduction & Background: Fine Needle Aspiration and Biopsy (FNAB) has been widely used as a diag-nostic modality in breast masses; it is also considered as cost-benefit sensitive technique with no major complication. It is, however, not clear whether ultra-sound-guided FNAB is more sensitive in palpable breast masses. The objective of this study is to com-pare ultrasound-guided vs. Free-hand FNA in these patients. Patients & Methods: In this prospective study, pa-tients referred to Jahad Daneshgahi Clinic for Breast Diseases were first examined by a surgeon and then by a radiologist to select patients with a palpable and solid breast mass. Included patients (N=40) were then randomized as to undergo free-hand FNA by the sur-geon or ultrasound-guided FNA by the radiologist. Examining cytologist was blind to the method used. Sensitivity and insufficiency rate were calculated for both of the FNA methods in all patients. Patients with cytology score of 4 or 5 underwent excisional biopsy. Six-month follow-up was performed in pa-tients with scores of 1 to 3. Results: Ultrasound-guided FNA was associated with a 12% less insufficient aspirate than the free-hand method. When insufficient aspirates were considered as negative results, ultrasound-guided FNA was 14% more sensitive than the free-hand FNA. Excluding insufficient aspirates, we found ultrasound-guided FNAB to be only 1.5% more sensitive which was not statistically significant. Conclusion: Our results suggest that ultrasound-guided FNAB is a more sensitive technique than the free-hand maneuver in palpable breast masses mainly because of its significantly less insufficient aspirates.
The reliability and distinguishability of ultrasound diagnosis of ovarian masses  [cached]
Bagheban Alireza,Zayeri Farid,Anaraki Fatemeh,Elahipanah Zahra
Indian Journal of Medical Sciences , 2008,
Abstract: Background: For any radiologist, intra-observer agreement in observing and decision making in diagnosis of any disease is of great importance, and so is observing and reading ultrasound pictures of ovarian masses and distinguishing amongst their categories. Aims: In this study, the reliability and consistency of ultrasound diagnosis of ovarian tumors have been evaluated. Settings and Design: Two experienced and three less experienced radiologists assessed ultrasounds of 40 patients of Mirza Koochak Khan Hospital in Tehran, Iran, in 2005. Materials and Methods: In this prospective observational study, the ultrasounds were performed by an expert radiologist, with a single apparatus. These ultrasounds have been evaluated separately and independently in two periods (with a 1-week interval). Statistical Analysis Used: Weighted kappa was used to calculate intra-observer agreement (reliability), and two statistical models were applied to assess category distinguishability (consistency). SPSS version 10, SAS version 8, and EXCEL 2003 have been used to do an appropriate statistical analysis. Results: Mean of weighted kappa was 0.81, and mean of distinguishability was 0.995 for our experienced radiologists, due to their superior results. Because of weaker results obtained by the less experienced radiologists, mean of weighted kappa and mean of distinguishability were 0.65 and 0.967 respectively. Overall mean of distinguishability for benign and borderline categories was 0.969; and for malignant and borderline categories, it was 0.987. Conclusion: Although experienced radiologists functioned better than the less experienced radiologists, all of them showed appropriate distinguishability and intra-observer agreement in diagnosis and categorization of the ovarian masses. Distinguishing benign category from borderline was more difficult than distinguishing malignant category from borderline. In general, experienced radiologists showed better results compared to less experienced radiologists.
The Role of Ultrasound Guided FNAB (Fine Needle Aspiration Biopsy) of Nonpalpable Breast Masses
M. Haghighi
Iranian Journal of Radiology , 2007,
Abstract: Background and Objective: Because of the high inci-dence and mortality rate of breast cancers, and high survival rate of patients after detecting masses smaller than 1cm, it is important to do tissue sampling with imaging guidance. Our goal was to determine the role of sonoguided FNAB as a first step to avoid the more invasive and expensive unnecessary core or excisional biopsies. Materials and Methods: This was an observational study. Our cases included the patients referred to our clinic from 1998-2004 to get FNAB for their nonpal-pable breast masses that had been found in imaging and were visible with Ultrasound and classified in cat 3 or 4 BIRads. The number of cases was 500. The in-strument used was ESaote EU4 with 10 MHZ probe. The results of cytological tests were collected and classified into three groups known as: 1-Benign (negative) 2-Malignant (positive) 3-Indeterminate Our gold standard was excisional biopsy with three years follow up. Results: Sono and mammo guided FNA, core biopsy and needle localization biopsy are three ways to reach nonpalpable breast lesions. According to other studies, the overall accuracy for imaging guided core biopsy is %97 without FP and for FNA is %77 with %5 FP. An inadequate amount of sample is reported in 32% of sonoguided FNAs but in our study, it was not significant. We had normal breast tissue in %2 (n=10) of our cases and they did not develop malig-nancy in three years follow up. The cost of FNA is very low compared with the other two procedures. Also our NPV was %100 and three fourth of our cases had benign pathology (negative for malignancy). Conclusion: According to our results doing FNA as the first step for cat 3 and 4 masses and R/O of malig-nancy in most patients can save money and time. Core biopsy could be reserved only for the other one fourth of patients.
Analysis and assessment of real-time contrast-enhanced ultrasonography in the diagnosis of breast masses
实时超声造影在乳腺良恶性肿块鉴别诊断中的应用

AN Shu,LIU Jian,GU Peng,ZHAO Xing-you,YUAN Shun-xian,ZHAO Xiao-bo,
安姝
,刘健,顾鹏,赵兴友,袁顺娴,赵小波

中华医学超声杂志(电子版) , 2010,
Abstract: Objective To investigate the perfusion characteristics of intraductal breast lesion by real-time gray-scale contrast ultrasound and to determine the value of real contrast ultrasound in the diagnosis of breast masses. Methods A total of 30 breast lumps by ultrasound contrast enhancement were observed from the enhanced level. An enhanced mode and enhanced border were observed when the lesion was clear. The perfusion characteristics were compared between the benign and malignant lesions. Results Thirty breast...
Observer Variability in BI-RADS Ultrasound Features and Its Influence on Computer-Aided Diagnosis of Breast Masses  [PDF]
Laith R. Sultan, Ghizlane Bouzghar, Benjamin J. Levenback, Nauroze A. Faizi, Santosh S. Venkatesh, Emily F. Conant, Chandra M. Sehgal
Advances in Breast Cancer Research (ABCR) , 2015, DOI: 10.4236/abcr.2015.41001
Abstract: Objective: Computer classification of sonographic BI-RADS features can aid differentiation of the malignant and benign masses. However, the variability in the diagnosis due to the differences in the observed features between the observations is not known. The goal of this study is to measure the variation in sonographic features between multiple observations and determine the effect of features variation on computer-aided diagnosis of the breast masses. Materials and Methods: Ultrasound images of biopsy proven solid breast masses were analyzed in three independent observations for BI-RADS sonographic features. The BI-RADS features from each observation were used with Bayes classifier to determine probability of malignancy. The observer agreement in the sonographic features was measured by kappa coefficient and the difference in the diagnostic performances between observations was determined by the area under the ROC curve, Az, and interclass correlation coefficient. Results: While some features were repeatedly observed, κ = 0.95, other showed a significant variation, κ = 0.16. For all features, combined intra-observer agreement was substantial, κ = 0.77. The agreement, however, decreased steadily to 0.66 and 0.56 as time between the observations increased from 1 to 2 and 3 months, respectively. Despite the variation in features between observations the probabilities of malignancy estimates from Bayes classifier were robust and consistently yielded same level of diagnostic performance, Az was 0.772-0.817 for sonographic features alone and 0.828-0.849 for sonographic features and age combined. The difference in the performance, ΔAz, between the observations for the two groups was small (0.003-0.044) and was not statistically significant (p < 0.05). Interclass correlation coefficient for the observations was 0.822 (CI: 0.787-0.853) for BI-RADS sonographic features alone and for those combined with age was 0.833 (CI: 0.800-0.862). Conclusion: Despite the differences in the BI-RADS sonographic features between different observations, the diagnostic performance of computer-aided analysis for differentiating breast masses did not change. Through continual retraining, the computer-aided analysis provides consistent diagnostic performance independent of the variations in the observed sonographic features.
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