2.2.3. Re-segmentation of the mask

In the whole set of head CT sections, some sections are relatively more complex in structure, and it is difficult to ensure high-quality brain extraction by preliminary segmentation only, as shown in Fig 5(a). Through preliminary segmentation, non-brain tissues are still not removed and need further detection and segmentation. We first perform median filtering on the initially segmented image to remove small areas of non-brain tissue, producing mask 2, as in Fig 5(b). Then, the connected component labeling method measures the number of connected regions of the mask. If the number of connected regions equals 1, mask 2 is used as the final mask. If it is greater than 1, different methods are used to re-segment mask 2 according to CNN’s different classification results.

a) Initial segmented image b) Image of Mask 2 c) Final mask image d) Final brain extraction image.

In a set of head CT images, due to the presence of the skull, the brain tissue in some of the images is distributed as a single block, and some of them are distributed in multiple regions. We classify the head CT images into brain single-region distribution and brain multi-region distribution according to the difference in brain tissue distribution. As shown in Fig 6, for the images that require mask re-segmentation, we first use CNN to discriminate. For the images with the single-region distribution of the brain, we use the region growing [32] algorithm to trim the clipping of the mask 2. After much observation, In mask 2, the part to be eliminated originates from the human tissue above the brain parenchyma and not below the brain parenchyma, and the previous steps have eliminated the skull below the brain parenchyma and the extracranial soft tissues. Moreover, the brain is distributed in the middle of the image. Therefore, the first point that is not ’0’ can be used as the seed point for the region growth algorithm by searching from bottom to top within a certain range of the image midline. To ensure the robustness of segmentation, we moved the position of this point up another five lines to ensure that the seed point can fall precisely in the target region, see Fig 5(b) for details, and the seed point falls accurately in the region corresponding to the brain parenchyma. After capturing the seed points, the region growing algorithm is used to realize the re-segmentation of mask 2, and then the final mask is obtained, as in Fig 5(c). After that, the original image is multiplied with the final mask, and then the brain parenchyma extraction is completed, as in Fig 5(d).

For images with the multi-regional distribution of the brain, using the above methods results in small areas of missing brain tissue, so we perform segmentation based on image properties. The image is first transformed into a binary image. Then, the mask is re-segmented with the area size (number of pixels in each region) as the specified attribute and the classification result of CNN as the number of extractions.

In summary, the overall flow chart of the AMBBEM algorithm is shown in Fig 7. The soft tissue and skull are separated by threshold segmentation first, and then the image is filled with the skull as the template to get mask 1. The fill detection and closed operations are designed to overcome the problem of skull gaps, further ensure the fill integrity of mask 1, and increase the algorithm’s robustness. In the whole algorithm process, median filtering can eliminate the small area of non-brain tissue in the image. The connected component labeling method determines whether further segmentation of the mask is needed. The re-segmentation of the mask was done to improve further the accuracy of the final segmentation, details of which are shown in Fig 6 above. In addition, CNN is used throughout the algorithm to classify images that require closed operations and re-segmentation, providing a basis for selecting the appropriate processing method for different images and largely increasing the robustness and accuracy of the algorithm.