Automated detection of breast carcinomas not detected in a screening program.

Brake, G.M. te; Karssemeijer, N.; Hendriks, J.H.C.L.

doi:10.1148/radiology.207.2.9577496

Cited by 76 publications

(28 citation statements)

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“…The diagnostic process of radiologists can be improved by the use of computers [7][8][9][10][11]. Pattern-recognition techniques have been designed to draw the attention of the radiologist to regions in mammograms that need careful scrutiny and interpretation [12]. Fully automated methods can screen chest radiographs for features of tuberculosis [13].…”

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confidence: 99%

Sparseness of the trabecular pattern on dental radiographs: visual assessment compared with semi-automated measurements

Geraets¹,

Lindh²,

Verheij³

2012

The British Journal of Radiology

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Objective: In diagnostic imaging; human perception is the most prominent, yet least studied, source of error. A better understanding of image perception will help to improve diagnostic performance. This study focuses on the perception of coarseness of trabecular patterns on dental radiographs. Comparison of human vision with machine vision should yield knowledge on human perception. Method: In a study on identifying osteoporotic patients, dental radiographs were made from 505 post-menopausal women aged 45-70 years. Intra-oral radiographs of the lower and upper jaws were made. Five observers graded the trabecular pattern as dense, sparse or mixed. The five gradings were combined into a single averaged observer score per jaw. The radiographs were scanned and a region of interest (ROI) was indicated on each. The ROIs were processed with image analysis software measuring 25 image features. Pearson correlation and multiple linear regression were used to compare the averaged observer score with the image features. Results: 14 image features correlated significantly with the observer judgement for both jaws. The strongest correlation was found for the average grey value in the ROI. Other features, describing that osteoporotic patients have fewer but bigger marrow spaces than controls, correlated less with the sparseness of the trabecular pattern than a rather crude measure for structure such as the average grey value. Conclusion: Human perception of the sparseness of trabecular patterns is based more on average grey values of the ROI than on geometric details within the ROI.

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confidence: 99%

Sparseness of the trabecular pattern on dental radiographs: visual assessment compared with semi-automated measurements

Geraets¹,

Lindh²,

Verheij³

2012

The British Journal of Radiology

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“…This means the sensitivity of mammography for detecting breast cancer can be significantly reduced in the case of dense breasts [5]. Retrospective studies have shown that in current breast cancer screening 10% to 25% of tumors are missed by radiologists [6,7] and some of these false negative results can be explained by higher breast density.…”

Section: Introductionmentioning

confidence: 99%

Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction

Llobet

Pollán

Antón

et al. 2014

Computer Methods and Programs in Biomedicine

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Semi-automated and fully-automated mammographic density measurement and breast cancer risk prediction better tissue classification and density measurement on a continuous scale. The fully-automated method presented combines a classification scheme based on local features and thresholding operations that improve the performance of the classifier. A dataset of 655 mammograms was used to test the concordance of both approaches in measuring MD. Three expert radiologists measured MD in each of the mammograms using the semi-automated tool (DM-Scan). It was then measured by the fully-automated system and the correlation between both methods was computed. The relation between MD and breast cancer was then analyzed using a case-control dataset consisting of 230 mammograms.The Intraclass Correlation Coefficient (ICC) was used to compute reliability among raters and between techniques. The results obtained showed an average ICC=0.922 among raters when using the semi-automated tool, whilst the average correlation between the semi-automated and automated measures was ICC=0.838. In the case-control study, the results obtained showed Odds Ratios (OR) of 1.38 and 1.50 per 10% increase in MD when using the semi-automated and fully-automated approaches respectively. It can therefore be concluded that the automated and semi-automated MD assessment present a good correlation.Both methods also found an association between MD and breast cancer risk, which warrants the proposed tools for breast cancer risk prediction and clinical decision making. A full version of the DM-Scan is freely available.

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“…Currently much effort is directed towards the development of Full-Field Digital Mammography (FFDM) and soft-copy reading. Soft-copy reading in combination with digital and digitized mammograms offers opportunities for the integration of Computer-Aided Detection (CAD) 4,5,6 , immediate access to image enhancement 7 , efficient and flexible organization of double reading, tele-mammography and tele-consultation on a regional and international level, and electronic image archiving and retrieval (PACS). In addition, soft-copy reading presents promising new opportunities to increase both the sensitivity and specificity of screening radiologists with CAD, computer-aided interpretation 8,9,10,11 , computer-assisted training, and automated reader in-service monitoring.…”

Section: Introductionmentioning

confidence: 99%

Optimized soft-copy display of digitized mammograms

Roelofs¹,

Woudenberg²,

Hendriks³

et al. 2003

Medical Imaging 2003: Image Perception, Observer Performance, and Technology Assessment

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Digitization and CRT display reduce sharpness of mammograms. To ensure image quality on a CRT, comparable to the quality of original films, a modified unsharp-masking (USM) algorithm is proposed to correct for this reduction. This study evaluates the clinical value of this algorithm and determines the optimal setting of its parameters. Eight complete mammographic cases were processed by a modified USM algorithm with 19 settings for three parameters, resulting in 152 stimuli. All cases showed a clearly visible mass; five also contained microcalcifications. The modification of the standard USM algorithm consisted of selectively improving low contrasts. Moreover, the USM enhancement was made grey value dependent to avoid clipping. Four experienced screening radiologists and four physicists (having experience with mammography imaging) rated all mammograms on a 1-10 point scale, according to image quality and suitability for diagnosis. The images were randomly presented. Before the experiment started, a subset of the images was shown to familiarize the observers to the range of images and parameter settings. For a contrast enhancement factor of about 0.4, the processed mammograms appeared to be significantly better than the original digitized mammograms (p<.001). Differences in the results for the radiologists and the physicists were small.

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Automated detection of breast carcinomas not detected in a screening program.

Abstract: A substantial proportion of cancers that were missed in a screening program, despite double reading, were found with this detection method at less than one false-positive finding per image.

Cited by 76 publications

References 0 publications

Sparseness of the trabecular pattern on dental radiographs: visual assessment compared with semi-automated measurements

Sparseness of the trabecular pattern on dental radiographs: visual assessment compared with semi-automated measurements

Semi-automated and fully automated mammographic density measurement and breast cancer risk prediction

Optimized soft-copy display of digitized mammograms

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