Segmentation of tumor region in respiratory disease by extended algorithm

Abstract

Introduction: The expansion of pulmonary tumors and their alterations take place in a dynamic manner, so that vigorous segmentation of the obtained images is accurately required. Methods: In this research, an extended algorithm in region growing was executed on CT lung tumors to investigate precise tumor region and edges. First, a new threshold via definition of greater target region around the initial tumor was implemented in MATLAB software. Second, nearby points were settled in an array and then these points were updated established upon the tumor growth to delineate the fresh tumor edges. Here, farthest distance from the center of color intensity point of the initial tumor was selected to grow the region in the algorithm. Third, fresh tumor boundary was determined via an interpolation between these fresh points by sketching lines from the tumor midpoint. Then, the edge correction was implemented and the fresh region was attached to the principal region to attain a segmented tumor exterior. Results: The proposed technique enhanced the tumor recognition by 96% and 91% maximum and minimum accuracy, respectively, in comparison with basilar method. In inclusive algorithm, the percentage of conformity had a positive effect on realization of the threshold value and renewal of the relative amount by 13% enhancement over accuracy assessment. Also when compared to basilar algorithm, it was found that at least 12% of the percentage differences in conformity segment the tumor area in lung CT images. The proposed algorithm with sufficient accuracy accelerates the segmentation process to delineate and improve the tumor edges by growing multiple selected regions. The algorithm also guarantees the independence of the results from the starting point. Conclusion: According to the definition of the center of mass of the tumor color intensity, the proposed extended algorithm may be generalized to the 3D images regardless of the matrix size and the image thickness. The combination of techniques such as machine learning is expected to improve segmentation accuracy for different types of nodule and tumor CT images. Implications for practice: Proposed extended algorithm with sufficient accuracy accelerates the segmentation process to delineate and improve the tumor edges by growing multiple selected regions.