Automatic and fast segmentation of breast region-of-interest (ROI) and density in MRIs

Abstract

Accurate segmentation of the breast region of interest (BROI) and breast density (BD) is a significant challenge during the analysis of breast MR images. Most of the existing methods for breast segmentation are semi-automatic and limited in their ability to achieve accurate results. This is because of difficulties in removing landmarks from noisy magnetic resonance images (MRI) due to similar intensity levels and the close connection to BROI. This study proposes an innovative, fully automatic and fast segmentation approach to identify and remove landmarks such as the heart and pectoral muscles. The BROI segmentation is carried out with a framework consisting of three major steps. Firstly, we use adaptive wiener filtering and k-means clustering to minimize the influence of noises, preserve edges and remove unwanted artefacts. The second step systematically excludes the heart area by utilizing active contour based level sets where initial contour points are determined by the maximum entropy thresholding and convolution method. Finally, a pectoral muscle is removed by using morphological operations and local adaptive thresholding on MR images. Prior to the elimination of the pectoral muscle, the MR image is sub divided into three sections: left, right, and central based on the geometrical information. Subsequently, a BD segmentation is achieved with 4 level fuzzy c-means (FCM) thresholding on the denoised BROI segmentation. The proposed method is validated using the 1350 breast images from 15 female subjects. The pixel-based quantitative analysis showed excellent segmentation results when compared with manually drawn BROI and BD. Furthermore, the presented results in terms of evaluation matrices: Acc, Sp, AUC, MR, P, Se and DSC demonstrate the high quality of segmentations using the proposed method. The average computational time for the segmentation of BROI and BD is 1 minute and 50 seconds.

Automatic Detection of Diabetic Eye Disease Through Deep Learning Using Fundus Images: A Survey

“…ML, already has a rich history in biology [99], [100] and chemistry [101], and it has recently gained prominence in the field of solid state materials science. Presently, DL models in ML are effectively used in imaging for classification, detection [102], segmentation [103] and prepossessing. The most famous and commonly employed DL architecture in the selected 65 studies is CNN, which is used in 64 cases, while DBN is implemented once.…”

Machine Learning in Computer Vision: A Review

“…Morphological processing is one of the pivotal steps of IP and excerpt information of components and sends it to segmentation phases were partitioning the image into objects (the difficult task is to segment an autonomous picture) [30,102]. These objects represent the processed data transform from the solution space of segmentation [30,103]. At last, object detection and recognition assign a label for those objects which use in future processing.…”

Automated Skin Lesion Detection towards Melanoma

“…Moreover, not only for the detection of skin lesion researchers also proposed solutions for the detection of breast cancer in which Pandey et al [21] proposed different segmentation techniques to automatically segment-out the breast region of interest, a comparative analysis is performed by Tripathy et al [22] in which different techniques of segmentation and classification were applied for the detection of lung cancer, Panday et al [24] proposed an automatic for the detection of retinal vessel segmentation in which background is separated from the images to minimize noise and other intrinsic problems to accurately segment out the region of interest. Dutta et al [23] implemented watershed method is used to segment out the cancer cell in liver.…”