Computerized mass detection in whole breast ultrasound images: reduction of false positives using bilateral subtraction technique

Ikedo, Yuji; Fukuoka, Daisuke; Hara, Takeshi; Fujita, Hiroshi; Takada, Etsuo; Endo, Tokiko; Morita, Takako

doi:10.1117/12.709225

Cited by 13 publications

(5 citation statements)

References 15 publications

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“…Similar to earlier approaches (12,13), the scan geometry planning module was based on the previously generated training database: during the training period of the current study protocol, landmarks were extracted for each patient and each cardiac geometry and were rigidly registered into common reference frames. From these reference frames, atlas landmarks were calculated taking anatomic variability into account.…”

Section: Anatomy Recognition and Automated Cardiac Mr Geometry Planningmentioning

confidence: 99%

Fully automatic geometry planning for cardiac MR imaging and reproducibility of functional cardiac parameters

Frick¹,

Paetsch

Harder

et al. 2011

Magnetic Resonance Imaging

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Purpose: To establish operator-independent, fully automated planning of standard cardiac geometries and to determine the impact on interstudy reproducibility of cardiac functional parameters.Materials and Methods: Cardiac MR imaging was done in 50 patients referred for left-ventricular function assessment. In all patients, first standard manual planning was performed followed by automatic planning (AUTO1) and repeat automatic planning (AUTO2) after repositioning the patient to investigate interstudy reproducibility. Cardiac functional parameters were assessed and cine scans were visually graded on a 4-point scale from nondiagnostic to excellent.Results: Overall success rate of AUTO was 94% with good to excellent geometry planning in >94% of cine standard views. Comparing manual versus fully automated planning, a high agreement of cardiac functional parameters (Lin's concordance correlation coefficient, 0.91 to 0.99) with minimal percent bias (0.24 to 3.84%) was found. In addition, a high interstudy reproducibility of automatic planning was demonstrated (Lin's concordance correlation coefficient, 0.89 to 0.99; percent bias, 0.38 to 5.04%; precision, 3.46 to 9.09%). Conclusion:Fully automated planning of cardiac geometries could reliably be performed in patients showing a variety of cardiovascular pathologies. Standard cardiac geometries were precisely replicated and functional parameters were highly accurate.

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Section: Anatomy Recognition and Automated Cardiac Mr Geometry Planningmentioning

confidence: 99%

Fully automatic geometry planning for cardiac MR imaging and reproducibility of functional cardiac parameters

Frick¹,

Paetsch

Harder

et al. 2011

Magnetic Resonance Imaging

View full text Add to dashboard Cite

show abstract

“…It is widely accepted that digital mammograms or tomosynthesis mammograms [123] are associated with quite limited use in cases of small breast masses within dense breast tissues owing to the obscure visualization of small masses when using this method, whereas ultrasonography (US) [456] has been clinically proven to be capable of detecting masses even in dense breasts and is widely used in Asia for the detection as well as the classification of breast masses. Typically, handheld probes for US scanners have been used for breast cancer screening with conventional US imaging devices having disadvantages such as operator dependence and poor image quality levels.…”

Section: Introductionmentioning

confidence: 99%

Automated Detection Algorithm of Breast Masses in Three-Dimensional Ultrasound Images

Jeong

Yu²,

Lee

et al. 2016

Healthc Inform Res

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ObjectivesWe propose an automatic breast mass detection algorithm in three-dimensional (3D) ultrasound (US) images using the Hough transform technique.MethodsOne hundred twenty-five cropped images containing 68 benign and 60 malignant masses are acquired with clinical diagnosis by an experienced radiologist. The 3D US images are masked, subsampled, contrast-adjusted, and median-filtered as preprocessing steps before the Hough transform is used. Thereafter, we perform 3D Hough transform to detect spherical hyperplanes in 3D US breast image volumes, generate Hough spheres, and sort them in the order of votes. In order to reduce the number of the false positives in the breast mass detection algorithm, the Hough sphere with a mean or grey level value of the centroid higher than the mean of the 3D US image is excluded, and the remaining Hough sphere is converted into a circumscribing parallelepiped cube as breast mass lesion candidates. Finally, we examine whether or not the generated Hough cubes were overlapping each other geometrically, and the resulting Hough cubes are suggested as detected breast mass candidates.ResultsAn automatic breast mass detection algorithm is applied with mass detection sensitivity of 96.1% at 0.84 false positives per case, quite comparable to the results in previous research, and we note that in the case of malignant breast mass detection, every malignant mass is detected with false positives per case at a rate of 0.62.ConclusionsThe breast mass detection efficiency of our algorithm is assessed by performing a ROC analysis.

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“…Several imaging modalities have been used to assess soft tissue tumors and the ultrasound modality would be among the most useful and powerful technique thank to its effectiveness for imaging organs and soft tissue structures in the human body (Taylor et al, 2002). Nowadays, an essential and important medical image processing scientific research domain has been emerged from medical imaging analysis which is referred to as Computer Aided Diagnosis (CAD) (Joo et al, 2004;Ikedo et al, 2007;Shen et al, 2007). CAD systems have been intensively developed for different breast imaging modalities offering and providing several essential aids and and more accuracy during clinical exploration even in the case limited performances apparatus.…”

Section: Problem Statementmentioning

confidence: 99%

“…For the second synthetic image, corrupted with salt & pepper (5% noise level), Gaussian (variance = 0.02) or uniform (variance = 0.01) noises, However, artifacts are still present in the case of uniform noise, but it still outperforms the other algorithms. To evaluate the clustering performances, we use two functions: partition entropy Vpe and partition coefficient Vpc [29]They are defined as follows …”

Section: Findings Of Researchmentioning

confidence: 99%

An Weighted Particle Swarm Optimization (Wpso) Technique for Accurate Breast Cancer Detection

2017

IJAERD

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Computerized mass detection in whole breast ultrasound images: reduction of false positives using bilateral subtraction technique

Cited by 13 publications

References 15 publications

Fully automatic geometry planning for cardiac MR imaging and reproducibility of functional cardiac parameters

Fully automatic geometry planning for cardiac MR imaging and reproducibility of functional cardiac parameters

Automated Detection Algorithm of Breast Masses in Three-Dimensional Ultrasound Images

An Weighted Particle Swarm Optimization (Wpso) Technique for Accurate Breast Cancer Detection

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