Design Computer-Aided Diagnosis System Based on Chest CT Evaluation of Pulmonary Nodules.

Hui Wang,Xianwen Yue,Shanshan Liu,Yanying Li,Osamah Ibrahim Khalaf

doi:10.1155/2022/7729524

Abstract

At present, the diagnosis and treatment of lung cancer have always been one of the research hotspots in the medical field. Early diagnosis and treatment of this disease are necessary means to improve the survival rate of lung cancer patients and reduce their mortality. The introduction of computer-aided diagnosis technology can easily, quickly, and accurately identify the lung nodule area as an imaging feature of early lung cancer for the clinical diagnosis of lung cancer and is helpful for the quantitative analysis of the characteristics of lung nodules and is useful for distinguishing benign and malignant lung nodules. Growth provides an objective diagnostic reference standard. This paper studies ITK and VTK toolkits and builds a system platform with MFC. By studying the process of doctors diagnosing lung nodules, the whole system is divided into seven modules: suspected lung shadow detection, image display and image annotation, and interaction. The system passes through the entire lung nodule auxiliary diagnosis process and obtains the number of nodules, the number of malignant nodules, and the number of false positives in each set of lung CT images to analyze the performance of the auxiliary diagnosis system. In this paper, a lung region segmentation method is proposed, which makes use of the obvious differences between the lung parenchyma and other human tissues connected with it, as well as the position relationship and shape characteristics of each human tissue in the image. Experiments are carried out to solve the problems of lung boundary, inaccurate segmentation of lung wall, and depression caused by noise and pleural nodule adhesion. Experiments show that there are 2316 CT images in 8 sets of images of different patients, and the number of nodules is 56. A total of 49 nodules were detected by the system, 7 were missed, and the detection rate was 87.5%. A total of 64 false-positive nodules were detected, with an average of 8 per set of images. This shows that the system is effective for CT images of different devices, pixel pitch, and slice pitch and has high sensitivity, which can provide doctors with good advice.

Highlights

Pulmonary nodules are one of the most important clinical manifestations of early lung cancer
The imaging principle of Computed tomography (CT) is established based on the detection of the corresponding absorption degree of the radiation reflected by the tissues of different densities in patient’s human body
This paper mainly studies the pulmonary nodule diagnosis system based on CT image and designs and implements it

Summary

Introduction

Pulmonary nodules are one of the most important clinical manifestations of early lung cancer. Computer-aided diagnosis of benign and malignant pulmonary nodules is very important in clinic. Most of the early stage lung cancer is mainly assisted in diagnosis by computer tomography imaging technology. The imaging principle of CT is established based on the detection of the corresponding absorption degree of the radiation reflected by the tissues of different densities in patient’s human body. The advantages of CT diagnosis are convenient and rapid examination, high-density resolution, quantitative measurement of CT value of tissue, clear CT image, and clear anatomical relationship. The disadvantage is that CT diagnosis has certain radiation, and there are still great limitations in the diagnostic efficiency of soft tissue tumors, especially in qualitative diagnosis. After the radiation penetrates the human body, due to a certain attenuation

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Conclusion