The segmentation of the heart is usually demanded in the clinical practice for computing functional parameters in patients, such as ejection fraction, cardiac output, peak ejection rate, or filling rate. Because of the time required, the manual delineation is typically limited to the left ventricle at the end-diastolic and end-systolic phases, which is insufficient for computing some of these parameters (e.g., peak ejection rate or filling rate). Common computer-aided (semi-)automated approaches for the segmentation task are computationally demanding, and an initialization step is frequently needed. This work is intended to address the aforementioned problems by providing an image-driven method for the accurate segmentation of the heart from computed tomography scans. The resulting algorithm is fast and fully automatic (even the region of interest is delimited without human intervention). The proposed methodology relies on image processing and analysis techniques (such as multi-thresholding based on statistical local and global parameters, mathematical morphology, and image filtering) and also on prior knowledge about the cardiac structures involved. Segmentation results are validated through the comparison with manually delineated ground truth, both qualitatively (no noticeable errors found after visual inspection) and quantitatively (mass overlapping over 90%).
