Adhesion Pulmonary Nodules Detection Based on Dot-Filter and Extracting Centerline Algorithm

Liwei Liu,Jianghui Dong,Xin Wang,Liping Wang,Yang Li

doi:10.1155/2015/597313

Abstract

A suspected pulmonary nodule detection method was proposed based on dot-filter and extracting centerline algorithm. In this paper, we focus on the distinguishing adhesion pulmonary nodules attached to vessels in two-dimensional (2D) lung computed tomography (CT) images. Firstly, the dot-filter based on Hessian matrix was constructed to enhance the circular area of the pulmonary CT images, which enhanced the circular suspected pulmonary nodule and suppresses the line-like areas. Secondly, to detect the nondistinguishable attached pulmonary nodules by the dot-filter, an algorithm based on extracting centerline was developed to enhance the circle area formed by the end or head of the vessels including the intersection of the lines. 20 sets of CT images were used in the experiments. In addition, 20 true/false nodules extracted were used to test the function of classifier. The experimental results show that the method based on dot-filter and extracting centerline algorithm can detect the attached pulmonary nodules accurately, which is a basis for further studies on the pulmonary nodule detection and diagnose.

Highlights

Pulmonary nodules are small masses of tissue in the lung, are prevalent findings on chest and abdominal computed tomography (CT) scans, and can be cancerous, though most of them are benign [1]
Guo et al developed a pulmonary nodule detection algorithm based on multiscale enhancement filtering of Hessian matrix and selecting of grads entropy, where Hessian matrix is relative to the gray scale of the pixel in the CT image, and grads entropy is relative to gray scale of the pixel [16]
In this paper we first use 2D Hessian matrix to construct dotfilter constructed to extract dot-like region

Summary

Introduction

Pulmonary nodules are small masses of tissue in the lung, are prevalent findings on chest and abdominal CT scans, and can be cancerous, though most of them are benign [1]. Guo et al developed a pulmonary nodule detection algorithm based on multiscale enhancement filtering of Hessian matrix and selecting of grads entropy, where Hessian matrix is relative to the gray scale of the pixel in the CT image, and grads entropy is relative to gray scale of the pixel [16] It worked well in the solitary pulmonary nodules detection, but it can only detect most suspect nodules and cannot exclude the false positives, especially the ends and the cross sections of the vessels or tracheas. In this paper, starting from the relationship of their position, we combine dot-Filter and algorithm of extracting centerline, using which to identify which is the end or head of the vessel and which the circle formed by the intersection of the lines In this way, we can separate the nodules from vessels and tracheas effectively with fewer steps

Materials and Methods

Enhancement of Nodules by Dot-Filter

Results and Discussion

Conclusion