Title :
Breast Cancer Ultrasound Images´ Sequence Exploration Using BI-RADS Features´ Extraction: Towards an Advanced Clinical Aided Tool for Precise Lesion Characterization
Author :
Sellami, Lamia ; Ben Sassi, O. ; Chtourou, Khalil ; Ben Hamida, A.
Author_Institution :
Higher Inst. of Biotechnol., Univ. of Sfax, Sfax, Tunisia
Abstract :
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.
Keywords :
biomedical ultrasonics; cancer; feature extraction; image sequences; mammography; medical image processing; BI-RADS feature extraction; breast cancer lesion characterization; breast cancer ultrasound analysis; breast cancer ultrasound image sequence exploration; computer aided diagnosis system; multislice ultrasound breast cancer lesion; textural feature; Breast; Cancer; Feature extraction; Image segmentation; Lesions; Speckle; Ultrasonic imaging; Breast cancer ultrasound images´ sequence; feature extraction; image segmentation; lesion characterization; speckle reduction;
Journal_Title :
NanoBioscience, IEEE Transactions on
DOI :
10.1109/TNB.2015.2486621