Automated lung segmentation algorithm for CAD system of thoracic CT

Objective: To design and test the accuracy and efficiency of our lung segmentation algorithm on thoracic CT image in computer-aided diagnostic (CAD) system, especially on the segmentation between left and right lungs. Methods: We put forward the base frame of our lung segmentation firstly. Then, using optimal thresholding and mathematical morphologic methods, we acquired the rough image of lung segmentation. Finally, we presented a fast self-fit segmentation refinement algorithm, adapting to the unsuccessful left-right lung segmentation of thredsholding. Then our algorithm was used to CT scan images of 30 patients and the results were compared with those made by experts. Results: Experiments on clinical 2-D pulmonary images showed the results of our algorithm were very close to the expert’s manual outlines, and it was very effective for the separation of left and right lungs with a successful segmentation ratio 94.8%. Conclusion: It is a practicable fast lung segmentation algorithm for CAD system on thoracic CT image.

An Improved Lung Cancer Segmentation Based on Nature-Inspired Optimization Approaches

The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and Dice.The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by researchers for improved segmentation processes of medicalimage datasets for identifying the targeted region of interest.

Operation room nursing based on humanized nursing mode combined with nitric oxide on rehabilitation effect after lung surgery

BACKGROUND With advancements in the diagnosis and treatment of lung diseases,lung segment surgery has become increasingly common.Postoperative rehabilitation is critical for patient recovery,yet challenges such as complications and adverse outcomes persist.Incorporating humanized nursing modes and novel treatments like nitric oxide inhalation may enhance recovery and reduce postoperative complications.AIM To evaluate the effects of a humanized nursing mode combined with nitric oxide inhalation on the rehabilitation outcomes of patients undergoing lung surgery,focusing on pulmonary function,recovery speed,and overall treatment costs.METHODS A total of 79 patients who underwent lung surgery at a tertiary hospital from March 2021 to December 2021 were divided into a control group(n=39)receiving a routine nursing program and an experimental group(n=40)receiving additional humanized nursing interventions and atomized inhalation of nitric oxide.Key indicators were compared between the two groups alongside an analysis of treatment costs.RESULTS The experimental group demonstrated significant improvements in pulmonary function,reduced average recovery time,and lower total treatment costs compared to the control group.Moreover,the quality of life in the experimental group was significantly better in the 3 months post-surgery,indicating a more effective rehabilitation process.CONCLUSION The combination of humanized nursing mode and nitric oxide inhalation in postoperative care for lung surgery patients significantly enhances pulmonary rehabilitation outcomes,accelerates recovery,and reduces economic burden.This approach offers a promising reference for improving patient care and rehabilitation efficiency following lung surgery.

3D segmentation and visualization of lung and its structures using CT images of the thorax

Computing systems have been playing an important role in various medical fields, notably in image diagnosis. Studies in the field of Computational Vision aim at developing techniques and systems capable of detecting various illnesses automatically. What has been highlighted among the existing exams that allow diagnosis aid and the application of computing systems in parallel is Computed Tomography (CT). CT enables the visualization of internal organs, such as the lung and its structures. Computational Vision systems extract information from the CT images by segmenting the regions of interest, and then recognize and identify details in those images. This work focuses on the segmentation phase of CT lung images with singularity-based techniques. Among these methods are the region growing (RG) technique and its 3D RG variations and the thresholding technique with multi-thresholding. The 3D RG method is applied to lung segmentation and from the 3D RG segments of the lung hilum, the multi-thresholding can segment the blood vessels, lung emphysema and the bones. The results of lung segmentation in this work were evaluated by two pulmonologists. The results obtained showed that these methods can integrate aid systems for medical diagnosis in the pulmonology field.

Erratum to: An advanced segmentation using area and boundary tracing technique in extraction of lungs region

Erratum to：J.Cent.South Univ.（2014）21：3811-3820DOI：10.1007/s11771-014-2366-9The original version of this article unfortunately contained three mistakes.The mistakes are corrected as follows：1）The spelling of third author is incorrect.The correct name is Jae-Young PYUN.2）The information of corresponding author is incorrect.The correct information should be Goo-Rak KWON,Professor,PhD;Tel/Fax：＋98-711-7264102;E-mail：grkwon@chosun.ac.kr

肺段隔离症的X线及CT分析

目的探讨肺段隔离症的X线及CT的诊断及鉴别诊断方法对12例肺段隔离症的X线及CT资料进行分析结果12例肺段隔离症X线及CT表现为片状影,圆形或椭圆形及三角形团块状影,蜂窝状影。临床表现多数无症状,或者咳嗽、咳痰甚至痰中带血,胸痛等。结论本病易误诊,提高诊断率的关键在于加深认识,综合考虑。

Automatic Detection of Airways in CT Scans of Cystic Fibrosis Patients

This paper describes a prototype of an automatic system for the detection and evalua-tion of airways of Cystic Fibrosis (CF) patients from Computed Tomography (CT) Scans. The aim of the study is to present a prototype of an automatic system which could serve as a decision support for radiologists. The area percentages of airway in lung regions have been calculated in CT slices to represent Bronchiectasis stages of CF patients. The proposed automatic system has been tested on a dataset comprising of four CF patients belonging to different stages of Bronchiectasis.