6533b83afe1ef96bd12a6f4e
RESEARCH PRODUCT
Breast Ultra-Sound image segmentation: an optimization approach based on super-pixels and high-level descriptors
Joan MartíGuillaume LemaitreFabrice MeriaudeauJoan Massichsubject
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONCAD02 engineering and technology[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processingBI-RADS lexiconOptimization based Segmentation030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicineBreast cancerCut0202 electrical engineering electronic engineering information engineeringMedical imagingMedicineComputer visionBreast ultrasound[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processingPixelmedicine.diagnostic_testbusiness.industryBreast Ultra-SoundGraph-CutsImage segmentationmedicine.disease3. Good healthComputingMethodologies_PATTERNRECOGNITIONComputer-aided diagnosis020201 artificial intelligence & image processingMachine-Learning based SegmentationArtificial intelligencebusiness[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingdescription
International audience; Breast cancer is the second most common cancer and the leading cause of cancer death among women. Medical imaging has become an indispensable tool for its diagnosis and follow up. During the last decade, the medical community has promoted to incorporate Ultra-Sound (US) screening as part of the standard routine. The main reason for using US imaging is its capability to differentiate benign from malignant masses, when compared to other imaging techniques. The increasing usage of US imaging encourages the development of Computer Aided Diagnosis (CAD) systems applied to Breast Ultra-Sound (BUS) images. However accurate delineations of the lesions and structures of the breast are essential for CAD systems in order to extract information needed to perform diagnosis. This article proposes a highly modular and flexible framework for segmenting lesions and tissues present in BUS images. The proposal takes advantage of optimization strategies using super-pixels and high-level de-scriptors, which are analogous to the visual cues used by radiologists. Qualitative and quantitative results are provided stating a performance within the range of the state-of-the-art.
year | journal | country | edition | language |
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2015-06-03 |