TR Based Lung Segmentation For Cancer Detection

Abstract

The necessary and critical step is to evaluate the development of lung cancer and nodule segmentation. Immobile challenge in the field of segmentation of pulmonary nodule is particularly to identify the small size nodule. To improve and sustain the diagnosis analysis, this paper put forward and widens a new approach to segmentation method for nodule size smaller than 3mm. In this paper we examined and proposed a new method based on transition region based multiple thresholding and followed by crack code analysis method for segmentation. First we reap the ROI from the input CT image and enhance the region of nodule by linear and nonlinear diffusion filtering algorithm. Second Object contours are obtained by transition region based analysis. Third to extract multiple objects ROI from object contours employ morphological operation. Fourth prepare the images for segmentation by reducing noise and smoothing operations like weighted average filter. Kuwahara filter is used to smooth images and the edge position. Then we make use of crack code analysis to renovate lung boundaries. Finally the result is obtained by overlap the extracted image with the restored lung mask. To analyses the evaluation of the novel segmentation method examines 90 lung nodules with 3mm to 9mm diameter from LIDC database. The presented novel approach attain ground truth rate of 86.93% ±0.09 with false positive rate of 15.09% ±0.06.After evaluation and  investigation the results of segmentation our proposed method outperformed compared to other literature.