Research ArticleOpen Access

Investigating the Influence of Rois Selection in Breast Ultrasound Segmentation Using the Eicamm Technique

Volume 8 - Issue 2

Karem D Marcomini*

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- Department of Electrical Engineering -EESC-University of São Paulo, Brazil
*Corresponding author: Karem D Marcomini, Department of Electrical Engineering -EESC-University of São Paulo, São Carlos (SP), Brazil

Received: August 15, 2018; Published: August 21, 2018

DOI: 10.26717/BJSTR.2018.08.001617

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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.

Keywords: Breast Ultrasound Images; Visual Subjectivity; Nodules Segmentation; Eicamm; Computer Aided Detection

Abbrevation: ROIs: Regions Of Interest; CAD: Computer Aided Detection; EICAMM: Enhanced Independent Component Analysis Mixture Model; AOM: Area Overlap Measure; AUM: Under Segmentation Measure; AVM: Over Segmentation Measure; CM: Combination Measure; CP: completeness; CR: correctness; Q: Quality; A: Accuracy; Err: Error; FPR: False Positive Ratio