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

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 3D extraction of pleural plaques and diffuse pleural thickening from lung MDCT images

Pleural plaques (PPs) and diffuse pleural thickening (DPT) are very common asbestos related pleural diseases (ARPD). They are currently identified non-invasively using medical imaging techniques. A fully automatic algorithm for 3D detection of calcified pleura in the diaphragmatic area and thickened pleura on the costal surfaces from multi detector computed tomography (MDCT) images has been developed and tested. The algorithm for detecting diaphragmatic pleura includes estimation of the diaphragm top surface in 3D and identifying those voxels at a certain vertical distance from the estimated diaphragm, and with intensities close to that of bone, as calcified pleura. The algorithm for detecting thickened pleura on the costal surfaces includes: estimation of the pleural costal surface in 3D, estimation of the centrelines of ribs and costal cartilages and the surfaces that they lie on, calculating the mean distance between the two surfaces, and identifying any space between the two surfaces whose distance exceeds the mean distance as thickened pleura. The accuracy and performance of the proposed algorithm was tested on 20 MDCT datasets from patients diagnosed with existing PPs and/or DPT and the results were compared against the ground truth provided by an experienced radiologist. Several metrics were employed and evaluations indicate high performance of both calcified pleura detection in the diaphragmatic area and thickened pleura on the costal surfaces. This work has made significant contributions to both medical image analysis and medicine. For the first time in medical image analysis, the approach uses other stable organs such as the ribs and costal cartilage, besides the lungs themselves, for referencing and landmarking in 3D. It also estimates fat thickness between the rib surface and pleura (which is usually very thin) and excludes it from the detected areas, when identifying the thickened pleura. It also distinguishes the calcified pleura attached to the rib(s), separates them in 3D and detects calcified pleura on the lung diaphragmatic surfaces. The key contribution to medicine is effective detection of pleural thickening of any size and recognition of any changes, however small. This could have a significant impact on managing patient risks