Breast Lesion Segmentation and Area Calculation for MR Images

Abstract

In this paper, our goal is to determine the boundaries of lesion and then calculate the area of existing lesion in breast magnetic resonance (MR) images to provide a useful information to the radiologists. For this purpose, at first stage region growing (RG) method and active contour model (Snake) is applied to the images to make the boundaries of lesion visible. RG method is one of the simplest approaches for image segmentation and provides accurate results with lower computation time due to its seed point initialization step. Snake method molds a closed contour to the boundary of a region in an image and is also popular in medical image segmentation studies. In the presented study, both of these methods are utilized to determine the lesion boundaries. After determining the boundaries of lesion accurately in the second stage of the study, bit-quad method is applied to the segmented images. Bit quad method is used to compute the area and perimeter of binary lesion images based on matching the logical state of regions of image to binary patterns. Finally, to evaluate the performance of the proposed study, computer simulations are performed. It is demonstrated via computer simulations that the lesion area and parameter values are very close to real values. By means of this study it is aimed to support radiologists during diagnosis and assessment of breast lesions.

Detection of Breast Region of Interest via Breast MR Scan on an Axial Slice

“…Determining the region of interest that may include the lesion area is a crucial step for breast cancer diagnosis via various imaging modalities such as ultrasound (US), mammography (MG), and magnetic resonance imaging (MRI) [3]. In recent years, the researchers studying in biomedical image processing, radiology and cancer diagnosis areas are interested in the detection of the region of interest (ROI) to enhance the success of medical treatments.…”