Malignant lesions on mammography: accuracy of two different computer-aided detection systems

We retrospectively compared the accuracy of two computer-aided detection (CAD) systems for the detection of malignant breast lesions on full-field digital mammograms. Mammograms of 326 patients were analyzed (117 patients with breast cancer, 209 negative cases), and each set of cases was read by two CAD systems (Second Look versus AccuDetect Galileo). True-positive fractions per image and case for soft densities, microcalcifications, and total cancers were assessed. Study results showed better overall performance of AccuDetect Galileo (when compared to Second Look) in detecting masses, microcalcifications, and all cancer types, especially in extremely dense breast parenchyma.     

数字乳腺摄影计算机辅助诊断对乳腺癌的检出价值

目的：探讨全数字化乳腺摄影（DM）计算机辅助诊断（CAD）在不同乳腺结构内检出乳腺癌的价值。方法：185例经病理证实的单乳单灶性乳腺癌及179例正常乳腺均行DM检查,根据BI-RADS将所有乳腺分为非致密组和致密组。将所有乳腺摄影图像采用CAD法进行诊断,计算CAD的诊断敏感性,记录CAD在病例组及正常组的平均每例假阳性标记数,并进行不同结构乳腺组间及病例组与正常组的比较。结果：CAD检出乳腺癌的敏感度为88．6％;在非致密组与致密组中检出乳腺癌的敏感度分别为97．4％和82．4％,两组比较,差异具有统计学意义（P〈0．05）。在病例组和正常组中cAD的假阳性标记数的中位数（最小值,最大值）分别为1（0,12）个和2（0,8）个,两组间的差异有统计学意义（P〈0．05）。结论：cAD检出乳腺癌的敏感性较高,乳腺密度可能影响DM对单纯肿块型乳腺癌的检出。     

Screening mammography-detected cancers: sensitivity of a computer-aided detection system applied to full-field digital mammograms

PURPOSE: To retrospectively evaluate the sensitivity of the performance of a computer-aided detection (CAD) system applied to full-field digital mammograms for detection of breast cancers in a screening group, with histologic findings as the reference standard. MATERIALS AND METHODS: This study had institutional review board approval, and patient informed consent was waived. A commercially available CAD system was applied to the digital mammograms of 103 women (mean age, 51 years; range, 35-69 years) with 103 breast cancers detected with screening. Sensitivity values of the CAD system according to mammographic appearance, breast composition, and histologic findings were analyzed. Normal mammograms from 100 women (mean age, 54 years; age range, 35-75 years) with no mammographic and clinical abnormality during 2-year follow-up were used to determine false-positive CAD system marks. Differences between the cancer detection rates in fatty and dense breasts for the CAD system were compared by using the chi(2) test. RESULTS: The CAD system correctly marked 99 (96.1%) of 103 breast cancers. The CAD system marked all 44 breast cancers that manifested as microcalcifications only, all 23 breast cancers that manifested as a mass with microcalcifications, and 32 (89%) of 36 lesions that appeared as a mass only. The sensitivity of the CAD system in the fatty breast group was 95% (59 of 62) and in the dense breast group was 98% (40 of 41) (P = .537). The CAD system correctly marked all 31 lesions of ductal carcinoma in situ (DCIS), all 22 lesions of invasive ductal carcinoma with DCIS, the single invasive lobular carcinoma lesion, and 45 (92%) of 49 lesions of invasive ductal carcinoma. On normal mammograms, the mean number of false-positive marks per patient was 1.80 (range, 0-10 marks; median, 1 mark). CONCLUSION: The CAD system can correctly mark most (96.1%) asymptomatic breast cancers detected with digital mammographic screening, with acceptable false-positive marks (1.80 per patient).     

Screening mammograms: interpretation with computer-aided detection--prospective evaluation

PURPOSE: To prospectively determine the effect of a commercially available computer-aided detection (CAD) system on interpretations of screening mammograms. MATERIALS AND METHODS: Institutional review board approval was granted; informed consent and HIPAA compliance were waived. A total of 21 349 screening mammograms obtained in 18 096 women were interpreted first without and then with review of CAD images to determine the effect of CAD analysis on the screening breast cancer detection rate, recall rate, and positive predictive value (PPV) for biopsy. The percentage of total cancers detected by the radiologists independent of CAD and the percentage correctly marked by the CAD system were determined. RESULTS: On the basis of pre-CAD interpretations, 2101 patients were recalled for diagnostic evaluation, 256 biopsies were performed, and 105 breast cancers were diagnosed. The breast cancer detection rate per 1000 screening mammograms was 4.92 (105 of 21 349 mammograms), the recall rate was 9.84% (2101 of 21 349 mammograms), and the PPV for biopsy was 41.0% (105 of 256 biopsies). After CAD image review, 199 additional patients were recalled, 21 additional biopsies were performed, and eight additional cancers were detected. The effect was a 7.62% (eight of 105) increase in the number of breast cancers detected, an increase in the recall rate to 10.77% (2300 of 21 349 mammograms), and a slight decrease in the PPV to 40.8% (113 of 277 biopsies). Radiologists detected 92.9% (105 of 113 cancers) of the total cancers, and CAD correctly marked 76.1% (86 of 113 cancers). CONCLUSION: The use of CAD improved the detection of breast cancer, with an acceptable increase in the recall rate and a minimal increase in the number of biopsies with benign results.     

Clinical performance of computer-assisted detection (CAD system in detecting carcinoma in breasts of different densities

OBJECTIVES: To determine the clinical performance of a computer-assisted detection (CAD) system in detecting carcinoma in breasts of different densities. MATERIALS AND METHODS: A total of 264 sets of bilateral screening mammograms taken in craniocaudal and medial-lateral oblique projections during the year 1997 were divided into four groups according to the BI-RADS density classification: fatty (pattern 1), scattered fibroglandular (pattern 2), heterogeneously dense (pattern 3) and extremely dense (pattern 4). Each group contained about 60% normal and 40% biopsy-proven cancer cases. Of the malignant cases, there were a mixture of mammographic findings including focal masses (<2.5 cm), asymmetrical density, architectural distortion or microcalcifications. Films with artefacts and obvious masses>2.5 cm were not included. The chosen cases were then digitized and analysed by the CAD system. Sensitivity was calculated as detection of cancer by at least one marker in at least one view. Specificity was calculated as the number of false-positive marks per image on normal cases. Statistical tests of significance were performed by using contingency tables and Chi square test. RESULTS: The CAD system detected 14 out of the total 15 cancer cases in totally fatty breasts with a sensitivity of 93.3% at a specificity of 1.3 false-positive marks per image. In breasts with scattered fibroglandular pattern, the sensitivity was 93.9% (31/33) and the specificity was 1.6 false-positive marks per image while in heterogeneously dense breasts, the sensitivity of the CAD system fell to 84.8% at a specificity of 1.6 false-positive marks per image. The sensitivity of the CAD system further dropped to 64.3% in markedly dense breasts while maintaining a specificity of 1.2 false-positive marks per image. The decrease in sensitivity in dense breast was found to be significant (p=0.046). CONCLUSION: The sensitivity of the CAD system deteriorated significantly as the density of the breast increased while the specificity of the system remained relatively constant.     

Accuracy of assigned BI-RADS breast density category definitions

RATIONALE AND OBJECTIVES: Quantitative criteria for the Breast Imaging Reporting and Data System (BI-RADS) mammographic density categories have recently been defined as <25% dense for almost entirely fatty, 25%-50% dense for scattered fibroglandular densities, 51%-75% for heterogeneously dense, and >75% dense for the extremely dense category. The purpose of this study is to compare the range of percent mammographic densities with radiologist-assigned BI-RADS mammographic density categories and compare with the recently issued definitions. MATERIALS AND METHODS: In this study, 200 consecutive negative analog screening mammograms were assigned BI-RADS mammographic density categories independently by three radiologists blinded to the other readers' density assignment. Quantitative assessment of percent mammographic density was performed using previously validated software. RESULTS: All three readers agreed on BI-RADS mammographic density categories in 98 cases (49%), and two of three readers agreed in all 200 cases. Using two reader's consensus, median mammographic density (range) was 6.0% (0.5%-19.2%) for fatty, 14.8% (1.2%-52.7%) for scattered densities, 51.2% (15.9%-82.2%) for heterogeneously dense, and 78.4% (60.1%-87.9%) for extremely dense breasts. The percent mammographic density ranges for fatty and extremely dense breasts correlated well with BI-RADS definitions, whereas the ranges of densities in the scattered and heterogeneously dense categories were considerably broader. CONCLUSION: Fatty and extremely dense BI-RADS categories compare relatively well to defined criteria, and therefore may be helpful in breast cancer risk models. Scattered fibroglandular densities and heterogeneously dense categories have broad percent mammographic density ranges and may not function well in breast cancer risk models.     

Impact of breast density on computer-aided detection for breast cancer

OBJECTIVE: Our aim was to determine whether breast density affects the performance of a computer-aided detection (CAD) system for the detection of breast cancer. MATERIALS AND METHODS: Nine hundred six sequential mammographically detected breast cancers and 147 normal screening mammograms from 18 facilities were classified by mammographic density. BI-RADS 1 and 2 density cases were classified as nondense breasts; BI-RADS 3 and 4 density cases were classified as dense breasts. Cancers were classified as either masses or microcalcifications. All mammograms from the cancer and normal cases were evaluated by the CAD system. The sensitivity and false-positive rates from CAD in dense and nondense breasts were evaluated and compared. RESULTS: Overall, 809 (89%) of 906 cancer cases were detected by CAD; 455/505 (90%) cancers in nondense breasts and 354/401 (88%) cancers in dense breasts were detected. CAD sensitivity was not affected by breast density (p=0.38). Across both breast density categories, 280/296 (95%) microcalcification cases and 529/610 (87%) mass cases were detected. One hundred fourteen (93%) of the 122 microcalcifications in nondense breasts and 166 (95%) of 174 microcalcifications in dense breasts were detected, showing that CAD sensitivity to microcalcifications is not dependent on breast density (p=0.46). Three hundred forty-one (89%) of 383 masses in nondense breasts, and 188 (83%) of 227 masses in dense breasts were detected-that is, CAD sensitivity to masses is affected by breast density (p=0.03). There were more false-positive marks on dense versus nondense mammograms (p=0.04). CONCLUSION: Breast density does not impact overall CAD detection of breast cancer. There is no statistically significant difference in breast cancer detection in dense and nondense breasts. However, the detection of breast cancer manifesting as masses is impacted by breast density. The false-positive rate is lower in nondense versus dense breasts. CAD may be particularly advantageous in patients with dense breasts, in which mammography is most challenging.     

Image segmentation in digital mammography: comparison of local thresholding and region growing algorithms.

Local thresholding and region-growing algorithms are developed and applied to digitized mammograms to quantify the parenchymal densities. The algorithms are first evaluated and optimized on phantom images reflecting varying image contrast, X-ray exposure conditions, and time-related changes. The difference between the segmentation results of the two techniques is less than 6% on the phantom images and 11% on the mammograms. The agreement between the computerized procedures and a manual one is in the range of 74-98%, depending on the breast parenchymal pattern and segmentation algorithm. The results show that computerized parenchymal classification of digitized mammograms is possible and independent of exposure.     

Ductal carcinoma in situ: computer-aided detection in screening mammography

PURPOSE: To retrospectively evaluate the sensitivity of computer-aided detection (CAD) in depicting ductal carcinoma in situ (DCIS) on screening mammograms by using biopsy proved lesion location as the reference standard. MATERIALS AND METHODS: Institutional review board approval was obtained, with a waiver of patient informed consent for this HIPAA-compliant study. Findings of all image-guided biopsies with a pathologic diagnosis of DCIS during a 1-year period were reviewed. Fifty-eight lesions in 55 women (average age, 61.41 years +/- 12.89 [standard deviation]) were available for review. The screening mammogram of the affected breast and, if available, the prior screening mammogram were digitized by the CAD system. An assessment was then made as to whether the CAD system marked the area of DCIS on the current and prior mammograms. Patient age, location and mammographic size of the lesion, type of lesion, and breast density were recorded and were analyzed by using chi2, Fisher exact, or Cochran-Mantel-Haenzel tests, where applicable. RESULTS: CAD identified DCIS in 53 (91%) of 58 lesions on craniocaudal (CC) and mediolateral oblique (MLO) views of screening mammograms obtained in the year of the diagnosis. On screening mammograms obtained prior to the year of the diagnosis (34 patients), no radiologically or CAD-detected lesion was present on 11 (32%) of 34 mammograms. CAD identified DCIS in 16 (70%) of 23 lesions on one of the two views. Seven (30%) of 23 lesions had mammographic findings at retrospective review that were not identified with CAD. CONCLUSION: CAD had a high sensitivity in the depiction of DCIS.     

计算机辅助检测在乳腺癌X线诊断中的应用

目的评价计算机辅助检测(CAD)系统在乳腺癌X线诊断中的临床应用价值。方法使用CAD系统对136例乳腺癌钼靶X线片进行检测，以手术病理结果为标准对CAD标记结果进行统计分析，并与放射科医生的检测结果进行比较。结果CAD系统对微小钙化的敏感性为93．2％(41／44)，特异性为22．8％(21／92)，准确性为45．6％(62／136)；CAD系统对肿块及结构紊乱病灶敏感性为82．9％(107／129)，特异性为71．4％(5／7)，准确性为82．4％(112／136)。经Kappa检验，CAD系统与放射科医生的检测结果一致性具有显著性意义(Kappa肿块=0．424，P肿块=0．000；Kappa钙化=0．365，P钙化=0．013)。结论CAD系统具有敏感性高、特异性较低的特点。在临床乳腺钼靶X线片的影像诊断中CAD系统可作为辅助检测工具。     

Computed tomography of breast lesions: comparison with x-ray mammography

Thirty-three patients with breast lesions demonstrated by mammography were examined with computed tomography (CT) using a standard whole body scanner. Although the CT images were of good diagnostic quality, the amount of new information gained was limited. The diagnostic accuracy of mammography in the hands of an experienced reader was higher than that with CT. We conclude that, although technically a whole body scanner is capable of producing good images of the breast, the number of patients in whom CT should be used instead of or in addition to mammography is limited. The indication for its use was primarily for patients in whom quality mammograms could not be produced because either the breast was unusually dense or extensive breast disease caused technical difficulties in performing mammograms. Computed tomography was also useful if the interpretation of the mammogram was equivocal, regional lymph node enlargement was questioned, invasion of the chest wall by tumor was suspected, and for planning radiotherapy treatment.     

Does computer-aided detection assist in the early detection of breast cancer?

PURPOSE: To evaluate whether breast cancers detected at screening are visible in previous mammograms, and to assess the performance of a computer-aided detection (CAD) system in detecting lesions in preoperative and previous mammograms. MATERIAL AND METHODS: Initial screening detected 67 women with 69 surgically verified breast cancers (Group A). An experienced screening radiologist retrospectively analyzed previous mammograms for visible lesions (Group B), noting in particular their size and morphology. Preoperative and previous mammograms were analyzed with CAD; a relatively inexperienced resident also analyzed previous mammograms. The performances of CAD and resident were then compared. RESULTS: Of the 69 lesions identified, 36 were visible in previous mammograms. Of these 36 "missed" lesions, 14 were under 10 mm in diameter and 29 were mass lesions. The sensitivity of CAD was 81% in Group A and 64% in Group B. Small mass lesions were harder for CAD to detect. The specificity of CAD was 3% in Group A and 9% in Group B. Together, CAD and the resident found more "missed" lesions than separately. CONCLUSION: Of the 69 breast cancers, 36 were visible in previous mammograms. CAD's sensitivity in detecting cancer lesions ranged from 64% to 81%, while specificity ranged from 9% to as low as 3%. CAD may be helpful if the radiologist is less subspecialized in mammography.     

Reproducibility of Computer-Aided Detection Marks in Digital Mammography

Objective

To evaluate the performance and reproducibility of a computer-aided detection (CAD) system in mediolateral oblique (MLO) digital mammograms taken serially, without release of breast compression.

Materials and Methods

A CAD system was applied preoperatively to the full-field digital mammograms of two MLO views taken without release of breast compression in 82 patients (age range: 33 - 83 years; mean age: 49 years) with previously diagnosed breast cancers. The total number of visible lesion components in 82 patients was 101: 66 masses and 35 microcalcifications. We analyzed the sensitivity and reproducibility of the CAD marks.

Results

The sensitivity of the CAD system for first MLO views was 71% (47/66) for masses and 80% (28/35) for microcalcifications. The sensitivity of the CAD system for second MLO views was 68% (45/66) for masses and 17% (6/35) for microcalcifications. In 84 ipsilateral serial MLO image sets (two patients had bilateral cancers), identical images, regardless of the existence of CAD marks, were obtained for 35% (29/84) and identical images with CAD marks were obtained for 29% (23/78). Identical images, regardless of the existence of CAD marks, for contralateral MLO images were 65% (52/80) and identical images with CAD marks were obtained for 28% (11/39). The reproducibility of CAD marks for the true positive masses in serial MLO views was 84% (42/50) and that for the true positive microcalcifications was 0% (0/34).

Conclusion

The CAD system in digital mammograms showed a high sensitivity for detecting masses and microcalcifications. However, reproducibility of microcalcification marks was very low in MLO views taken serially without release of breast compression. Minute positional change and patient movement can alter the images and result in a significant effect on the algorithm utilized by the CAD for detecting microcalcifications.     

Computerized analysis of tissue density effect on missed cancer detection in digital mammography.

This paper presents a study of the analysis of breast density in missed cancer cases and the effect of tissue density on cancer detection. A total of 100 missed cancer cases were collected. The breast density tissue was segmented with a statistical-based method. A set of tests was then applied to examine: (1) the differences in density between the mammograms at the detected stage and that at missed stage; (2) the density difference between the cancerous mammograms and their contra-lateral normal mammograms in the missed cancer cases; (3) the effect of breast density on CAD cancer detection. The results demonstrate that breast density is an important factor affecting not only radiologist's reading but also CAD performance. In order to improve early detection of breast cancer, a special effort should be directed to the high dense breast cases in CAD system design.     

Quantitative analysis of breast parenchymal density: correlation with women's age

RATIONALE AND OBJECTIVES: The authors evaluated the relationship between a woman's breast parenchymal density and her age by means of a quantitative method for measuring density from digitized mammograms. MATERIALS AND METHODS: The percentage of the breast considered to be dense was evaluated from mammograms of 50 women stratified by age. Quantitative analysis based on the computer segmentation of tissue in digitized mammograms was performed by three expert mammographers. The results of this analysis were compared with results from a review of the film mammograms by three expert mammographers. RESULTS: A slight decrease in the percentage of breast considered to be dense with increased age was observed. The average difference in the percentage of dense breast tissue between the youngest and the oldest age groups was 6.4% based on the digital review and 14.6% based on the film review. Within each age group, the total variability was on the order of 75%. CONCLUSION: The difference in mean magnitude between the youngest and oldest age groups was small and may not be clinically important. The variability within an age group was large, which suggests that age is not a reliable indicator of percentage of dense breast tissue.     

Computer-aided diagnosis in full digital mammography

RATIONALE AND OBJECTIVES: The authors clarify the detection rates for breast cancerous tumors and clustered microcalcifications with computer-aided diagnosis (CAD) based on Fuji Computed Radiography. The authors also determine whether mammographic reading with CAD contributes to the discovery of breast cancer. METHODS: Data acquired by Fuji Computed Radiography 9000, which consisted of 4148 digital mammograms including 267 cases of breast cancer, was transferred directly to an analysis workstation where an original software program determined extraction rates for breast tumors and clustered microcalcifications. Furthermore, using another 344 mammograms from 86 women, observer performance studies were conducted on five doctors for receiver operating characteristic (ROC) analysis. RESULTS: Sensitivity to breast cancerous tumors and clustered microcalcifications were 89.9% and 92.8%, respectively false-positive rates were 1.35 and 0.40 per image, respectively. The observer performance studies indicate that an average Az value for the five doctors was greater with the CAD system than with a film-only reading without CAD, and that a reading with CAD was significantly superior at P < 0.022. CONCLUSIONS: It has been shown that CAD based on Fuji Computed Radiography offers good detection rates for both breast cancerous tumors and clustered microcalcifications, and that the reading of mammograms with this CAD system would provide potential improvement in diagnostic accuracy for breast cancer.     

Mammographic characteristics of 115 missed cancers later detected with screening mammography and the potential utility of computer-aided detection

PURPOSE: To retrospectively determine the mammographic characteristics of cancers missed at screening mammography and assess the ability of computer-aided detection (CAD) to mark the missed cancers. MATERIALS AND METHODS: A multicenter retrospective study accrued 1,083 consecutive cases of breast cancer detected at screening mammography. Prior mammograms were available in 427 cases. Of these, 286 had lesions visible in retrospect. The 286 cases underwent blinded review by panels of radiologists; a majority recommended recall for 112 cases. Two experienced radiologists compared prior mammograms in 110 of these cases with the subsequent screening mammograms (when cancer was detected), noting mammographic characteristics of breast density, lesion type, size, morphology, and subjective reasons for possible miss. The prior mammograms were then analyzed with a CAD program. RESULTS: There were 110 patients with 115 cancers. On the prior mammograms with missed cancers, 35 (30%) of the 115 lesions were calcifications, with 17 of 35 (49%) clustered or pleomorphic. Eighty of the 115 (70%) were mass lesions, with 32 of 80 (40%) spiculated or irregular. For calcifications and masses, the most frequently suggested reasons for possible miss were dense breasts (12 of 35; 34%) and distracting lesions (35 of 80; 44%), respectively. CAD marked 30 (86%) of 35 missed calcifications and 58 (73%) of 80 missed masses. CONCLUSION: Detection errors affected cases with calcifications and masses. CAD marked most (77%; 88 of 115) cancers missed at screening mammography that radiologists retrospectively judged to merit recall.     

Computerized assessment of tissue composition on digitized mammograms

RATIONALE AND OBJECTIVES: The authors developed a computerized method for the quantitative assessment of breast tissue composition on digitized mammograms. MATERIALS AND METHODS: Three radiologists were asked to review 200 digitized mammograms and independently provide a Breast Imaging Reporting and Data System-like rating for breast tissue composition on a scale of 0 to 4. These values were incorporated into a "consensus" rating that was used as a reference point in the development and evaluation of a computerized method. After tissue segmentation that excluded nontissue areas, a set of quantitative features was computed. A computerized summary index that attempts to reproduce the radiologists' ratings was developed. Correlation coefficients (Pearson r) were used to compare the computerized index with the consensus ratings. RESULTS: Some individual features computed for the relatively dense breast areas showed good correlation (r > 0.8) with the radiologists' subjective ratings. The summary index of tissue composition demonstrated a significant correlation (r = 0.87), as well. CONCLUSION: Computerized methods that show good correlation with radiologists' ratings of breast tissue composition can be developed.     

Breast masses: computer-aided diagnosis with serial mammograms

PURPOSE: To retrospectively evaluate effects of computer-aided diagnosis (CAD) involving an interval change classifier (which uses interval change information extracted from prior and current mammograms and estimates a malignancy rating) on radiologists' accuracy in characterizing masses on two-view serial mammograms as malignant or benign. MATERIALS AND METHODS: The data collection protocol had institutional review board approval. Patient informed consent was waived for this HIPAA-compliant retrospective study. Ninety temporal pairs of two-view serial mammograms (depicting 47 malignant and 43 benign biopsy-proved masses) were obtained from 68 patient files and were digitized. Biopsy was the reference standard. Eight Mammography Quality Standards Act of 1992-accredited radiologists and two breast imaging fellows assessed digitized two-view temporal pairs (in preselected regions of interest only) by estimating likelihood of malignancy and Breast Imaging Reporting and Data System (BI-RADS) category without and with CAD. Observers' rating data were analyzed with Dorfman-Berbaum-Metz (DBM) multireader multicase method. Statistical significance of differences was estimated with the DBM method and Student two-tailed paired t test. RESULTS: Average area under the receiver operating characteristic curve for likelihood of malignancy across the 10 observers was 0.83 (range, 0.74-0.88) without CAD and improved to 0.87 (range, 0.80-0.92) with CAD (P < .05). The average partial area index above a sensitivity of 0.90 for likelihood of malignancy was 0.35 (range, 0.13-0.54) without CAD and 0.49 (range, 0.18-0.73) with CAD--a nonsignificant improvement (P = .11). For BI-RADS assessment, it was estimated that with CAD, six radiologists would correctly recommend additional biopsies for malignant masses (range, 4.3%-10.6%) and five would correctly recommend reduction of biopsy (ie, fewer biopsies) for benign masses (range, 2.3%-9.3%). However, five radiologists would incorrectly recommend additional biopsy for benign masses (range, 2.3%-14.0%), and one would incorrectly recommend reduction of biopsy (4.3%). CONCLUSION: CAD involving interval change analysis of preselected regions of interest can significantly improve radiologists' accuracy in classifying masses on digitized screen-film mammograms as malignant or benign.     

Ipsilateral-mammogram computer-aided detection of breast cancer.

In this paper, an ipsilateral multi-view computer-aided detection (CAD) scheme is presented for mass detection in digital mammograms by exploiting correlative information of suspicious lesions between mammograms of the same breast. After nonlinear tree-structured filtering for image noise suppression, two wavelet-based methods, directional wavelet transform and tree-structured wavelet transform for image enhancement, and adaptive fuzzy C-means algorithm for segmentation are employed on each mammograms of the same breast, respectively, concurrent analysis is developed for iterative analysis of ipsilateral multi-view mammograms by inter-projective feature matching analysis. A supervised artificial neural network is developed as a classifier, in which the back-propagation algorithm combined with Kalman filtering is used as training algorithm, and free-response receiver operating characteristic analysis is used to test the performance of the developed unilateral CAD system. Performance comparison has been conducted between the final ipsilateral multi-view CAD system and our previously developed single-mammogram-based CAD system. The study results demonstrate the advantages of ipsilateral multi-view CAD method combined with concurrent analysis over current single-view CAD system on false positive reduction.