IEEE Transactions on NanoBioscience · 2015 · 18 citations · 15 references
MedicineOncologyRadiologyDiagnostic ImagingDiagnostic RadiologyUltrasonographyBreast ImagingBreast Lesion CharacterizationBreast UltrasonographyBreast UltrasoundComputer-aided DiagnosisMedical ImagingBi-rads ClassificationBreast Lesion DiagnosisDiagnostic Breast UltrasonographyDiagnostic Breast UltrasoundLesion DetectionLesion Shape AssessmentSliceUltrasound Lesion Assessment
This research concerned a clinical need for precise breast cancer lesion characterization imaged by ultrasound sequences. Using therefore BI-RADS features that would be carefully extracted, the purpose of this study could be mainly to prove and to demonstrate the possibility of surveying precisely the changing characteristics of a breast cancer lesion within a considered ultrasound images' sequence. This was in fact a clinical need of a computer aided diagnosis (CAD) system permitting flexible and convivial clinical analysis of multi-slices' ultrasound breast cancer lesion with greater precision. The obtained results of our images' sequence breast cancer ultrasound analysis had shown the lesion form changing depending on the treated slice, as well as the values' differences for the morphological and the textural features. This would allow extracting more information about breast cancer lesions helping then radiologist to converge more rapidly and with a certain reinforced precision to the accurate clinical action to conduct. Such results would be reassembled and rearranged for constituting one computer aided diagnosis (CAD) system that could be provided for clinical explorations permitting on the other hand to avoid possible confusion between benign and malignant masses.
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Ahmedin Jemal, Rebecca L. Siegel, Elizabeth M. Ward et al. · CA A Cancer Journal for Clinicians · 2006 · 5.3K citations · Full text
Solid breast nodules: use of sonography to distinguish between benign and malignant lesions.
A. Thomas Stavros, David Thickman, Cindy Rapp et al. · Radiology · 1995 · 1.8K citations
Automated breast cancer detection and classification using ultrasound images: A survey
Heng-Da Cheng, Juan Shan, Wen Ju et al. · Pattern Recognition · 2009 · 730 citations