Detection Of Breast Masses Research Articles

피셔 분별 사전학습을 이용해 개선된 Sparse 표현 기반 악성 종괴 검출

X-ray를 이용한 여성의 유방암 검사인 유방조영술은 유방암의 초기 단계에서의 진단을 위한 효과적인 방법이다. 컴퓨터 지원 검출(CAD) 시스템은 유방조영술을 통한 진단 시 의사가 놓치기 쉬운 유방암의 징후인 종괴의 검출을 도와 유방암 진단율을 높이는 수단이다. 종괴는 다양한 모양을 지니며 경계가 뚜렷하지 않기 때문에 검출이 어렵고 결과적으로 비-종괴 영역을 포함한 많은 수의 종괴 후보영역이 CAD 시스템에서 검출된다. 따라서 CAD 시스템 설계 시 검출된 많은 수의 종괴 후보영역으로부터 실제 악성 종괴 영역을 분류할 수 있도록 우수한 성능의 분류기가 요구된다. 본 논문에서는 피셔 분별 사전학습을 통해 개선된 Sparse 표현(SR) 기반 분류방법을 제안한다. 개선된 SR 기반 분류기가 기존의 CAD 시스템에서 주로 사용되어온 Support Vector Machine (SVM) 분류기 보다 우수함을 비교실험을 통해 확인했다. Mammography, the process of using X-ray to examine the woman breast, is the one of the effective tools for detecting breast cancer at an early state. In screening mammogram, Computer-Aided Detection(CAD) system helps radiologist to diagnose cases by detecting malignant masses. A mass is an important lesion in the breast that can indicate a cancer. Due to various shapes and unclear boundaries of the masses, detecting breast masses is considered a challenging task. To this end, CAD system detects a lot of regions of interest including normal tissues. Thus it is important to develop the well-organized classifier. In this paper, we propose an enhanced sparse representation (SR) based classifier using Fisher discrimination dictionary learning. Experimental results show that the proposed method outperforms the existing support vector machine (SVM) classifier.

Mammographical mass detection and classification using Local Seed Region Growing–Spherical Wavelet Transform (LSRG–SWT) hybrid scheme

The purpose of this study is to implement accurate methods of detection and classification of benign and malignant breast masses in mammograms. Our new proposed method, which can be used as a diagnostic tool, is denoted Local Seed Region Growing–Spherical Wavelet Transform (LSRG–SWT), and consists of four steps. The first step is homomorphic filtering for enhancement, and the second is detection of the region of interests (ROIs) using a Local Seed Region Growing (LSRG) algorithm, which we developed. The third step incoporates Spherical Wavelet Transform (SWT) and feature extraction. Finally the fourth step is classification, which consists of two sequential components: the 1st classification distinguishes the ROIs as either mass or non-mass and the 2nd classification distinguishes the masses as either benign or malignant using a Support Vector Machine (SVM). The mammograms used in this study were acquired from the hospital of Istanbul University (I.U.) in Turkey and the Mammographic Image Analysis Society (MIAS). The results demonstrate that the proposed scheme LSRG–SWT achieves 96% and 93.59% accuracy in mass/non-mass classification (1st component) and benign/malignant classification (2nd component) respectively when using the I.U. database with k-fold cross validation. The system achieves 94% and 91.67% accuracy in mass/non-mass classification and benign/malignant classification respectively when using the I.U. database as a training set and the MIAS database as a test set with external validation.

Mass detection in reconstructed digital breast tomosynthesis volumes with a computer‐aided detection system trained on 2D mammograms

To develop a computer-aided detection (CAD) system for masses in digital breast tomosynthesis (DBT) which can make use of an existing CAD system for detection of breast masses in full-field digital mammography (FFDM). This approach has the advantage that large digital screening databases that are becoming available can be used for training. DBT is currently not used for screening which makes it hard to obtain sufficient data for training. The proposed CAD system is applied to reconstructed DBT volumes and consists of two stages. In the first stage, an existing 2D CAD system is applied to slabs composed of multiple DBT slices, after processing the slabs to a representation similar to that of the FFDM training data. In the second stage, the authors group detections obtained in the slabs that detect the same object and determine the 3D location of the grouped findings using one of three different approaches, including one that uses a set of features extracted from the DBT slabs. Experiments were conducted to determine performance of the CAD system, the optimal slab thickness for this approach and the best method to establish the 3D location. Experiments were performed using a database of 192 patients (752 DBT volumes). In 49 patients, one or more malignancies were present which were described as a mass, architectural distortion, or asymmetry. Free response receiver operating characteristic analysis and bootstrapping were used for statistical evaluation. Best performance was obtained when slab thickness was in the range of 1-2 cm. Using the feature based 3D localization procedure developed in the study, accurate 3D localization could be obtained in most cases. Case sensitivities of 80% and 90% were achieved at 0.35 and 0.99 false positives per volume, respectively. This study indicates that there may be a large benefit in using 2D mammograms for the development of CAD for DBT and that there is no need to exclusively limit development to DBT data.

Quantitative Measures Confirm the Inverse Relationship between Lesion Spiculation and Detection of Breast Masses

To identify specific mammographic appearances that reduce the mammographic detection of breast cancer. This study received institutional board review approval and all readers gave informed consent. A set of 60 mammograms each consisting of craniocaudal and mediolateral oblique projections were presented to 129 mammogram Breastscreen readers. The images consisted of 20 positive cases with single and multicentric masses in 16 and 4 cases, respectively (resulting in a total of 24 cancers), and readers were asked to identify and locate the lesions. Each lesion was then ranked according to a detectability rating (ie, the number of observers who correctly located the lesion divided by the total number of observers), and this was correlated with breast density, lesion size, and various descriptors of lesion shape and texture. Negative and positive correlations between lesion detection and density (r = -0.64, P = .007) and size (r = 0.65, P = .005), respectively, were demonstrated. In terms of lesion size and shape, there were significant correlations between the probability of detection and area (r = 0.43, P = .04), perimeter (r = 0.66, P = .0004), lesion elongation (r = 0.49, P = .02), and lesion nonspiculation (r = 0.78, P < .0001). The results of this study have identified specific lesion characteristics associated with shape that may contribute to reduced cancer detection. Mammographic sensitivity may be adversely affected without appropriate attention to spiculation.

Computer Aided Detection of Breast Density and Mass, and Visualization of Other Breast Anatomical Regions on Mammograms Using Graph Cuts

Breast cancer mostly arises from the glandular (dense) region of the breast. Consequently, breast density has been found to be a strong indicator for breast cancer risk. Therefore, there is a need to develop a system which can segment or classify dense breast areas. In a dense breast, the sensitivity of mammography for the early detection of breast cancer is reduced. It is difficult to detect a mass in a breast that is dense. Therefore, a computerized method to separate the existence of a mass from the glandular tissues becomes an important task. Moreover, if the segmentation results provide more precise demarcation enabling the visualization of the breast anatomical regions, it could also assist in the detection of architectural distortion or asymmetry. This study attempts to segment the dense areas of the breast and the existence of a mass and to visualize other breast regions (skin-air interface, uncompressed fat, compressed fat, and glandular) in a system. The graph cuts (GC) segmentation technique is proposed. Multiselection of seed labels has been chosen to provide the hard constraint for segmentation of the different parts. The results are promising. A strong correlation (r = 0.93) was observed between the segmented dense breast areas detected and radiological ground truth.

Evaluation of Breast Masses Using Mammography and Sonography as First Line Investigations

Purpose: To study the specificity of mammography and ultrasonography separately and in combination for detection of breast masses (ultrasonography-mammography correlation); To study the investigations to evaluate various breast masses; To describe suitable indications, advantages and limitations of each technique compared with other available modalities; To study the mimics of breast masses; To have histopathology follow-up and retrospective evaluation with imaging findings to improve diagnostic skills in series of 166 patients complaining of breast mass. Material: The prospective clinical study was carried out in the department of Radiodiagnosis for a period of 2 year extending from December 2010 to December 2012 infemale patients complaining of breast mass. Well informed written consent was obtained from them. Histopathology follow up was obtained from either biopsy or post operative tissue. USG machine: Philips HD 11 XE USG of the breasts and axillary region done in supine position in presence of female attendant; Mammography machine: Allengers machine with Agfa special mammography cassettes. Cranio caudal and Medio-Lateral Oblique views are taken in the presence of female attendant. MRI: PHILIPS 1.5 T machine; CT: SIEMENS duel slice CT machine. Results: Ultrasonography and mammography was done in most of the cases were sufficient to diagnose the lesion in most of the cases especially in benign breast masses. MRI and CT scan was used in special cases to know the extent of the lesions, in mimics of breast masses, bony extensions, primary muscular and bony lesions. Total 166 patients complaining of breast mass in one or both breasts were examined and evaluated with USG and mammography. The lesions were confirmed on histopathology (FNAC/biopsy). Out of 30 diagnosed malignancies two lesions were missed on mammography and four lesions were missed on ultrasonography. One of them was missed on both. For malignancies specificity of mammography is 93.3% and that of ultrasonography is 86.67%. Combining both the modalities specificity is near 97%. Out of total 92 abnormal breasts 12 were missed on USG and 20 were missed on mammography. Combining both the modalities only 2 lesions were missed and were diagnosed on histopathology alone. Overall specificity for USG in breast masses is 86.9% and for mammography it is 78.6%. Combining both the modalities the specificity is 97.6%. The “p” value is obtained which is highly significant for combination of ultrasonography and mammography in comparison with any individual modality (p = 0.0059 & p = 0.0001 respectively). Conclusion: Our study confirms the higher combined sensitivity rate for ultrasonography and mammography for detection of breast masses including malignancies. USG is useful in cystic lesions, ectasias, infections, pregnancy-lactation, and dense breast evaluation and for image guidance, whereas mammography is useful in detecting microcalcifications, spiculated masses for early detection of malignancies and for stereotactic biopsies. To suggest single modality, ultrasonography is better in younger population and BIRAD 1, 2 & 3 lesions. Whereas, mammography is better in older population and BIRAD 4 & 5 lesions. However, sono-mammographic correlation is best in both.

Multiresolution local binary pattern texture analysis combined with variable selection for application to false-positive reduction in computer-aided detection of breast masses on mammograms

In this paper, a new and novel approach is designed for extracting local binary pattern (LBP) texture features from the computer-identified mass regions, aiming to reduce false-positive (FP) detection in a computerized mass detection framework. The proposed texture feature, the so-called multiresolution LBP feature, is well able to characterize the regional texture patterns of core and margin regions of a mass, as well as to preserve the spatial structure information of the mass. In addition, to maximize a complementary effect on improving classification accuracy, multiresolution texture analysis has been incorporated into the extraction of LBP features. Further, SVM-RFE-based variable selection strategy is applied for selecting an optimal subset of variables of multiresolution LBP texture features to maximize the separation between breast masses and normal tissues. Extensive and comparative experiments have been conducted to evaluate the proposed method on two public benchmark mammogram databases (DBs). Experimental results show that the proposed multiresolution LBP features (extracted from automatically segmented mass boundaries) outperform other state-of-the-art texture features developed for FP reduction. Our results also indicate that combining our multiresolution LBP features with variable selection strategy is an effective solution for reducing FP signals in computer-aided detection (CAD) of mammographic masses.

Comparative evaluation of support vector machine classification for computer aided detection of breast masses in mammography

False positive (FP) marks represent an obstacle for effective use of computer-aided detection (CADe) of breast masses in mammography. Typically, the problem can be approached either by developing more discriminative features or by employing different classifier designs. In this paper, the usage of support vector machine (SVM) classification for FP reduction in CADe is investigated, presenting a systematic quantitative evaluation against neural networks, k-nearest neighbor classification, linear discriminant analysis and random forests. A large database of 2516 film mammography examinations and 73 input features was used to train the classifiers and evaluate for their performance on correctly diagnosed exams as well as false negatives. Further, classifier robustness was investigated using varying training data and feature sets as input. The evaluation was based on the mean exam sensitivity in 0.05–1 FPs on normals on the free-response receiver operating characteristic curve (FROC), incorporated into a tenfold cross validation framework. It was found that SVM classification using a Gaussian kernel offered significantly increased detection performance (P = 0.0002) compared to the reference methods. Varying training data and input features, SVMs showed improved exploitation of large feature sets. It is concluded that with the SVM-based CADe a significant reduction of FPs is possible outperforming other state-of-the-art approaches for breast mass CADe.

Automated detection of mass lesions in dedicated breast CT: A preliminary study

To develop an automated method to detect breast masses on dedicated breast CT (BCT) volumes and to conduct a preliminary evaluation of its performance. This method can be used in a computer-aided detection (CADe) system for noncontrast enhanced BCT. The database included patient images, which were acquired under an IRB-approved protocol. The database in this study consisted of 132 cases. 50 cases contained 58 malignant masses, and 23 cases contained 24 benign masses. 59 cases did not contain any biopsy-proven lesions. Each case consisted of an unenhanced CT volume of a single breast. First, each breast was segmented into adipose and glandular tissues using a fuzzy c-means clustering algorithm. The glandular breast regions were then sampled at a resolution of 2 mm. At each sampling step, a 3.5-cm(3) volume-of-interest was subjected to constrained region segmentation and 17 characteristic features were extracted, yielding 17 corresponding feature volumes. Four features were selected using step-wise feature selection and merged with linear discriminant analysis trained in the task of distinguishing between normal breast glandular regions and masses. Detection performance was measured using free-response receiver operating characteristic analysis (FROC) with leave-one-case-out evaluation. The feature selection stage selected features that characterized the shape and margin strength of the segmented region. CADe sensitivity per case was 84% (std = 4.2%) at 2.6 (std = 0.06) false positives per volume, or 6 × 10(-3) per slice (at an average of 424 slices per volume in this data set). This preliminary study demonstrates the feasibility of our approach for CADe for BCT.

Improving the performance of computer-aided detection of subtle breast masses using an adaptive cueing method

Current computer-aided detection (CAD) schemes for detecting mammographic masses have several limitations including high correlation with radiologists’ detection and cueing most subtle masses only on one view. To increase CAD sensitivity in cueing more subtle masses that are likely missed and/or overlooked by radiologists without increasing false-positive rates, we investigated a new case-dependent cueing method by combining the original CAD-generated detection scores with a computed bilateral mammographic density asymmetry index. Using the new method, we adaptively raise the CAD-generated scores of the regions detected on ‘high-risk’ cases to cue more subtle mass regions and reduce the CAD scores of the regions detected on ‘low-risk’ cases to discard more false-positive regions. A testing dataset involving 78 positive and 338 negative cases was used to test this adaptive cueing method. Each positive case involves two sequential examinations in which the mass was detected in ‘current’ examination and missed in ‘prior’ examination but detected in a retrospective review by radiologists. Applying to this dataset, a pre-optimized CAD scheme yielded 75% case-based and 55% region-based sensitivity on ‘current’ examinations at a false-positive rate of 0.25 per image. CAD sensitivity was reduced to 42% (case based) and 27% (region based) on ‘prior’ examinations. Using the new cueing method, case-based and region-based sensitivity could maximally increase 9% and 33% on the ‘prior’ examinations, respectively. The percentages of the masses cued on two views also increased from 27% to 65%. The study demonstrated that using this adaptive cueing method enabled us to help CAD cue more subtle cancers without increasing the false-positive cueing rate.

Breast masses detection using phase portrait analysis and fuzzy inference systems

Breast masses exhibit variability in margins, shapes, and dimensions, so their detection is a difficult task in mammographic computer-aided diagnosis. Mass detection is usually a two-step procedure: mass identification and false-positive reduction. A new method to automatically detect mass lesions in mammographic images with tuning according to the breast tissue density was developed and tested. A modified phase portrait analysis method was introduced, based on the eigenvalue condition number and an eigenvalue intensity map. The method uses an iterative and tissue density-adaptive segmentation procedure with extraction of geometric features. False-positive reduction is accomplished using a fuzzy inference-based classifier. A leave-one-image-out cross-validation procedure was implemented, and stepwise regression analysis was used to automatically extract an optimal set of features. Testing and validation were performed on two different data sets containing at least one malignant mass D1 (388 images) and D2 (674 images), and a third data set N1 (50 images) was used consisting of normal controls. These three data sets were taken from the Digital Database for Screening Mammography. For sensitivities of 0.9, 0.85, 0.80, and 0.75, the best results on cancer images exhibit an False-Positive per Image (FPpI) equal to 0.6, 0.45, 0.35, and 0.3, respectively, using a Bayes Linear Discriminant Analysis (LDA) classifier and an FPpI of 0.85, 0.7, 0.55, and 0.45 using a fuzzy inference system (FIS) for false-positive reduction. When the algorithm is tested on normal images, an FPpI equal to 0.4, 0.3, 0.25, and 0.2 was observed using LDA and 0.3, 0.25, 0.2, and 0.15 using the FIS. A preclinical study of an automatic breast mass detection algorithm provided promising results in terms of sensitivity and low false-positive rate. Further development and clinical testing are justified based on the results.

Intraoperative Ultrasound Assistance for Excision of Impalpable Musculoskeletal Soft Tissue Tumors

Intraoperative ultrasonography is a useful tool for the detection and extirpation of liver metastases, breast masses, and melanoma. However, the efficacy of this technology in intraoperative localization and resection of small soft tissue tumors has not been addressed. The purpose of this study is to report on the efficacy of intraoperative ultrasound assistance in excising impalpable musculoskeletal soft tissue tumors. Twenty-two soft tissue tumors <3 cm (range, 0.7-3 cm) were resected with intraoperative ultrasound assistance. All tumors were localized in the deep panniculus, fascia, or muscle. Surgical time and length of incisions was recorded in all the cases. Intra- and postoperative reregistration was made to confirm the tumor resection. Ultrasound assistance was successful in obtaining an accurate localization in all treated cases. Mean surgical time was 30 minutes (range, 13-87 minutes). Average incision length was 5.7 cm (range, 2.5-10.6 cm). Reregistration allowed intraoperative confirmation of the adequacy of the excision. The procedure allowed recognized and excised additional nodules not previously diagnosed in 3 cases. Postoperative echography done in all patients confirmed complete extirpation of the tumors, and histopathology confirmed adequate margins obtained. Intraoperative ultrasound can be used as an efficient tool to localize and treat impalpable small soft tissue tumors.

Intramuscular Lipoma Arising Within the Pectoralis Major Muscle Presenting as a Radiographically Detected Breast Mass

Lipomas are commonly encountered benign mesenchymal neoplasms composed of adipocytes that arise in the superficial subcutaneous tissues of the extremities and trunk. Occasionally, these tumors can originate from either within muscle (intramuscular) or between muscles (intermuscular). The former typically occurs within skeletal muscle of large muscles of an extremity, such as the thigh and shoulder. Intramuscular lipomas arising specifically from the pectoralis muscle are exceedingly rare. In this report, to our knowledge we describe the second case of an intramuscular lipoma arising within the pectoralis muscle. Similar to the first published report, our patient's tumor was clinically thought to be a breast mass. However, this example is unique in that it is the first of its kind to be found incidentally during screening ultrasound. Intramuscular lipomas are not routinely included in the radiologic differential diagnosis of a breast mass. However, when the tumor is posteriorly located and appears to involve the pectoralis muscle, the diagnostic possibility of intramuscular lipoma should be entertained.

Detection of masses in mammogram images using CNN, geostatistic functions and SVM

Breast cancer occurs with high frequency among the world's population and its effects impact the patients' perception of their own sexuality and their very personal image. This work presents a computational methodology that helps specialists detect breast masses in mammogram images. The first stage of the methodology aims to improve the mammogram image. This stage consists in removing objects outside the breast, reducing noise and highlighting the internal structures of the breast. Next, cellular neural networks are used to segment the regions that might contain masses. These regions have their shapes analyzed through shape descriptors (eccentricity, circularity, density, circular disproportion and circular density) and their textures analyzed through geostatistic functions (Ripley's K function and Moran's and Geary's indexes). Support vector machines are used to classify the candidate regions as masses or non-masses, with sensitivity of 80%, rates of 0.84 false positives per image and 0.2 false negatives per image, and an area under the ROC curve of 0.87.

TU‐E‐220‐05: Characterizing Breast Lesions and Their Environments with Ultrasound Tomography

Purpose: The purpose of this presentation is to report on a clinical study investigating the use of ultrasound tomography for characterization of breast lesions and their environments.Methods: A series of clinical studies were carried out using a recently upgraded clinical prototype based on the principles of ultrasound tomography. The in‐vivo performance of the prototype was assessed by imaging patients at the Karmanos Cancer Instituteˈs Alexander J. Walt Comprehensive Breast Center. Patients were recruited on the basis of having suspicious masses on mammography and were subsequently imaged with the prototype. Reflection images were constructed using sum and delay type beamforming in post‐processing while sound speed and attenuation images were reconstructed using bent‐ray tomographic techniques. Masses were identified by biopsy and their locations inferred from conventional mammography and ultrasound exams to provide the ground truth against which the performance of the prototype was measured. Results: Our techniques successfully demonstrated tomographic imaging of breast architecture as well as masses as small as 4 mm in size. Detected masses included cancers, cysts, fibroadenomas and hamartomas while anatomical structures corresponding to fat, parenchyma and fibrous stroma were also revealed. Masses were characterized using a combination of qualitative reflection parameters and the quantitative acoustic parameters of sound speed and attenuation. Furthermore, visualization and characterization of the tumor environment was achieved by combining the three imaging parameters, leading to clear evidence of the interaction of cancer with its surroundings. Conclusions: These results indicate that operator‐independent whole‐breast imaging and the detection and characterization of cancerous breast masses and their environment are feasible using ultrasound tomography techniques. This approach has the potential to provide a low cost, non‐invasive, and non‐ionizing means of evaluating breast masses, leading to a better understanding of tumor development and possible improvements in lesion detection and identification compared to current breast imaging techniques.

Superficial detection of breast mass by fourier transform infrared spectroscopy: caner versus the benign

Objective To explore the feasibility of superficially differentiating breast cancer from the benign fourier transform infrared ( FTIR) spectroscopy. Methods Twenty-six cases of breast cancer and 34 cases of the benign were examined superficially by FTIR spectroscopy. For each FTIR spectrum,the peak positions (P), relative intensity (I) and full width at half maximum ( F) of the band were recorded and the relative intensity ratios were calculated. Results In the group of breast cancer, both the peaks of 1546 cm-1 and 1303 cm-1 shifted toward lower wave number (P ＜0. 05), as compared to those of the benign group. The relative intensity ratio of I1256/I1460) was increased significantly (P ＜ 0. 05). Conclusion There existed many significant differences in FTIR spectra between malignant and benign breast masses. Key words: Breast carcinoma; Spectroscopy

Mutual information-based template matching scheme for detection of breast masses: From mammography to digital breast tomosynthesis

Development of a computational decision aid for a new medical imaging modality typically is a long and complicated process. It consists of collecting data in the form of images and annotations, development of image processing and pattern recognition algorithms for analysis of the new images and finally testing of the resulting system. Since new imaging modalities are developed more rapidly than ever before, any effort for decreasing the time and cost of this development process could result in maximizing the benefit of the new imaging modality to patients by making the computer aids quickly available to radiologists that interpret the images. In this paper, we make a step in this direction and investigate the possibility of translating the knowledge about the detection problem from one imaging modality to another. Specifically, we present a computer-aided detection (CAD) system for mammographic masses that uses a mutual information-based template matching scheme with intelligently selected templates. We presented principles of template matching with mutual information for mammography before. In this paper, we present an implementation of those principles in a complete computer-aided detection system. The proposed system, through an automatic optimization process, chooses the most useful templates (mammographic regions of interest) using a large database of previously collected and annotated mammograms. Through this process, the knowledge about the task of detecting masses in mammograms is incorporated in the system. Then, we evaluate whether our system developed for screen-film mammograms can be successfully applied not only to other mammograms but also to digital breast tomosynthesis (DBT) reconstructed slices without adding any DBT cases for training. Our rationale is that since mutual information is known to be a robust inter-modality image similarity measure, it has high potential of transferring knowledge between modalities in the context of the mass detection task. Experimental evaluation of the system on mammograms showed competitive performance compared to other mammography CAD systems recently published in the literature. When the system was applied “as-is” to DBT, its performance was notably worse than that for mammograms. However, with a simple additional preprocessing step, the performance of the system reached levels similar to that obtained for mammograms. In conclusion, the presented CAD system not only performed competitively on screen-film mammograms but it also performed robustly on DBT showing that direct transfer of knowledge across breast imaging modalities for mass detection is in fact possible.

Comparative performance of multiview stereoscopic and mammographic display modalities for breast lesion detection

Mammography is known to be one of the most difficult radiographic exams to interpret. Mammography has important limitations, including the superposition of normal tissue that can obscure a mass, chance alignment of normal tissue to mimic a true lesion and the inability to derive volumetric information. It has been shown that stereomammography can overcome these deficiencies by showing that layers of normal tissue lay at different depths. If standard stereomammography (i.e., a single stereoscopic pair consisting of two projection images) can significantly improve lesion detection, how will multiview stereoscopy (MVS), where many projection images are used, compare to mammography? The aim of this study was to assess the relative performance of MVS compared to mammography for breast mass detection. The MVS image sets consisted of the 25 raw projection images acquired over an arc of approximately 45 degrees using a Siemens prototype breast tomosynthesis system. The mammograms were acquired using a commercial Siemens FFDM system. The raw data were taken from both of these systems for 27 cases and realistic simulated mass lesions were added to duplicates of the 27 images at the same local contrast. The images with lesions (27 mammography and 27 MVS) and the images without lesions (27 mammography and 27 MVS) were then postprocessed to provide comparable and representative image appearance across the two modalities. All 108 image sets were shown to five full-time breast imaging radiologists in random order on a state-of-the-art stereoscopic display. The observers were asked to give a confidence rating for each image (0 for lesion definitely not present, 100 for lesion definitely present). The ratings were then compiled and processed using ROC and variance analysis. The mean AUC for the five observers was 0.614 +/- 0.055 for mammography and 0.778 +/- 0.052 for multiview stereoscopy. The difference of 0.164 +/- 0.065 was statistically significant with a p-value of 0.0148. The differences in the AUCs and the p-value suggest that multiview stereoscopy has a statistically significant advantage over mammography in the detection of simulated breast masses. This highlights the dominance of anatomical noise compared to quantum noise for breast mass detection. It also shows that significant lesion detection can be achieved with MVS without any of the artifacts associated with tomosynthesis.

Computer‐aided detection of breast masses: Four‐view strategy for screening mammography

To improve the performance of a computer-aided detection (CAD) system for mass detection by using four-view information in screening mammography. The authors developed a four-view CAD system that emulates radiologists' reading by using the craniocaudal and mediolateral oblique views of the ipsilateral breast to reduce false positives (FPs) and the corresponding views of the contralateral breast to detect asymmetry. The CAD system consists of four major components: (1) Initial detection of breast masses on individual views, (2) information fusion of the ipsilateral views of the breast (referred to as two-view analysis), (3) information fusion of the corresponding views of the contralateral breast (referred to as bilateral analysis), and (4) fusion of the four-view information with a decision tree. The authors collected two data sets for training and testing of the CAD system: A mass set containing 389 patients with 389 biopsy-proven masses and a normal set containing 200 normal subjects. All cases had four-view mammograms. The true locations of the masses on the mammograms were identified by an experienced MQSA radiologist. The authors randomly divided the mass set into two independent sets for cross validation training and testing. The overall test performance was assessed by averaging the free response receiver operating characteristic (FROC) curves of the two test subsets. The FP rates during the FROC analysis were estimated by using the normal set only. The jackknife free-response ROC (JAFROC) method was used to estimate the statistical significance of the difference between the test FROC curves obtained with the single-view and the four-view CAD systems. Using the single-view CAD system, the breast-based test sensitivities were 58% and 77% at the FP rates of 0.5 and 1.0 per image, respectively. With the four-view CAD system, the breast-based test sensitivities were improved to 76% and 87% at the corresponding FP rates, respectively. The improvement was found to be statistically significant (p < 0.0001) by JAFROC analysis. The four-view information fusion approach that emulates radiologists' reading strategy significantly improves the performance of breast mass detection of the CAD system in comparison with the single-view approach.

Computer-aided detection of breast masses depicted on full-field digital mammograms: a performance assessment

To investigate the feasibility of converting a computer-aided detection (CAD) scheme for digitised screen-film mammograms to full-field digital mammograms (FFDMs) and assessing CAD performance on a large database. The database included 6478 FFDM images acquired on 1120 females, with 525 cancer cases and 595 negative cases. The database was divided into five case groups: (1) cancer detected during screening, (2) interval cancers, (3) "high-risk" recommended for surgical excision, (4) recalled but negative and (5) negative (not recalled). A previously developed CAD scheme for masses depicted on digitised images was converted and re-optimised for FFDM images while keeping the same image-processing structure. CAD performance was analysed on the entire database. The case-based sensitivity was 75.6% (397/525) for the current mammograms and 40.8% (42/103) for the prior mammograms deemed negative during clinical interpretation but "visible" during retrospective review. The region-based sensitivity was 58.1% (618/1064) for the current mammograms and 28.4% (57/201) for the prior mammograms. The CAD scheme marked 55.7% (221/397) and 35.7% (15/42) of the masses on both views of the current and the prior examinations, respectively. The overall CAD-cued false-positive rate was 0.32 per image, ranging from 0.29 to 0.51 for the five case groups. This study indicated that (1) digitised image-based CAD can be converted for FFDMs while performing at a comparable, or better, level; (2) CAD detects a substantial fraction of cancers depicted on prior examinations, albeit most having been marked only on one view; and (3) CAD tends to mark more false-positive results on "difficult" negative cases that are more visually difficult for radiologists to interpret.