Cortical surface extraction from magnetic resonance (MR) scans is a preliminary, yet crucial step in brain segmentation and analysis. Although there are many algorithms that address this problem, they often sacrifice execution speed for accuracy or they depend on many parameters that have to be tuned manually by an experienced practitioner. Therefore fast, accurate and autonomous cortical surface extraction algorithms are in high demand and they are being actively developed to enable clinicians to appropriately plan a treatment pathway and quantify response in patients with brain lesions based on precise image analysis. In this paper, we present an automated approach for cortical surface extraction from MR images based on 3D image morphology, connected component labeling and edge detection. Our technique allows for real-time processing of MR scans – an average study of 102 slices, each 512x512 pixels, takes approximately 768 ms to process (about 7 ms per slice) with known parameters. To automate the process of tuning the algorithm parameters, we developed a genetic algorithm for this task. Experimental study performed using real-life MR brain images revealed that the proposed algorithm offers very high-quality cortical surface extraction, it works in real-time, and it is competitive with the state of the art.
Computed tomography (CT) imaging became an indispensable modality exploited across a vast spectrum of clinical indications for diagnosis and follow-up, alongside various image-guided procedures, especially in patients with lung cancer. Accurate lung segmentation from whole-body CT scans is an initial, yet extremely important step in such procedures. Therefore, fast and robust (against low-quality data) segmentation techniques are being actively developed. In this paper, we propose a new real-time algorithm for segmenting lungs from the entire body CT scans. Our method benefits from both 2D and 3D analysis of CT images, coupled with several fast pruning strategies to remove false-positive tissue areas, including trachea and bronchi. Also, we developed a new approach for separating lungs which exploits spatial analysis of lung candidates. Our algorithms were implemented in Adaptive Vision Studio (AVS)|a visual-programming software suite based on the data-ow paradigm. Although AVS is extensively used in machine-vision industrial applications (it is equipped with a range of highly optimized image-processing routines), we showed it can be easily utilized in general data analysis applications, including medical imaging. Experimental study performed on a benchmark dataset manually annotated by an experienced reader revealed that our algorithm is very fast (average processing time of an entire CT series is less than 1.5 seconds), and it is competitive against the state of the art, delivering high-quality and consistent results (DICE was above 0.97 for both lungs; 0.96 for the left and 0.95 for the right lung after separation). The quantitative analysis was backed up with thorough qualitative investigation (including 2D and 3D visualizations) and statistical tests.
In the paper, a novel approach to the enhancement of color images corrupted by impulsive noise is presented. The proposed algorithm first calculates for every image pixel the distances in the RGB color space to all elements belonging to the filtering window. Then, a sum of a specified number of smallest distances, which serves as a measure of pixel similarity, is calculated. This generalization of the Rank-Ordered Absolute Difference (ROAD) is robust to outliers, as the high distances are not considered when calculating this measure. Next, for each pixel, a neighbor with smallest ROAD value is searched for. If such a pixel is found, then the filtering window is moved to a new position and again a neighbor, with ROAD measure lower than the initial value is looked for. If it is encountered, the window is moved again, otherwise the process is terminated and the starting pixel is replaced with the last pixel in the path formed by the iterative procedure of the window shifting. The comparison with the filters intended for the removal of noise in color images revealed excellent properties of the new enhancement technique. It is very fast, as the ROAD values can be pre-computed, and the formation of the paths needs only comparisons of scalar values. The proposed technique can be applied for the restoration of color images distorted by impulsive noise and can also be used as a method of edge sharpening. Its low computational complexity allows also for its application in the processing of video sequences.