Open Access Clinical and Medical Journal - BJSTR Journal

Abstract

The #false-negative interpretation represents serious problems in #breast lesions diagnosis. In order to reduce the number of these cases and increase the diagnostic sensibility, computational tools have been developed to aid the early detection of breast cancer. However, such computer schemes can be influenced directly or indirectly by the user mainly regarding the selection of the type of image to be processed. In this context, this work evaluates how the non-standardization in cutting regions of interest (ROIs) in the image can affect the computed detection and computer segmentation step. A total of 54 lesions recorded in images from #breast ultrasonography were used for the tests. An experienced radiologist cropped each lesion three times varying the amount of surrounding tissue-three different sets were formed, and a test group was added to the study containing 18 lesions of each case selected. A previous developed segmentation procedure based on the use of the EICAMM technique was applied to the images. The most accurate result with the EICAMM technique was obtained in the first set, in which the clipping was made as close to the lesion, providing greater accuracy in the comparison between the #segmentation by the computational process and the lesion delineation by the radiologist with lower rates of over and under segmentation. Mammography is the best method for early detection of breast cancer, and its interpretation remains a challenge to the specialist [1]. However, in women with dense breast, the #mammography sensitivity may be low, allowing to miss about 10% of all cancers [2-3].

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Investigating the Influence of Rois Selection in Breast Ultrasound Segmentation Using the Eicamm Technique by Karem D Marcomini in BJSTR