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2D Statistical Lung Shape Analysis Using Chest Radiographs: Modelling and Segmentation.

Accurate information of the lung shape analysis and its anatomical variations is very noticeable in medical imaging. The normal variations of the lung shape can be interpreted as a normal lung. In contrast, abnormal variations of the lung shape can be a result of one of the pulmonary diseases. The g...

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Published in:Journal of Digital Imaging Vol. 34; no. 3; pp. 523 - 541
Main Authors: Afzali, Ali, Babapour Mofrad, Farshid, Pouladian, Majid
Format: diagnostic images equations & formulas pictorial research tables/charts Journal Article
Published: Springer Nature Jun2021
Online Access:View this record in EBSCOhost
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      jtl: Journal of Digital Imaging
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      dt: Jun2021
      vid: 34
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s10278-021-00440-7
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        atl: 2D Statistical Lung Shape Analysis Using Chest Radiographs: Modelling and Segmentation.
      aug:
        au:
          Afzali, Ali
          Babapour Mofrad, Farshid
          Pouladian, Majid
        affil: Department of Medical Radiation Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
      sug:
        subj:
          Lung Anatomy and Histology
          Lung Radiography
          Radiography, Thoracic Methods
          Human
          Factor Analysis
          Lung Diseases Radiography
      ab: Accurate information of the lung shape analysis and its anatomical variations is very noticeable in medical imaging. The normal variations of the lung shape can be interpreted as a normal lung. In contrast, abnormal variations of the lung shape can be a result of one of the pulmonary diseases. The goal of this study is twofold: (1) represent two lung shape models which are different at the reference points in registration process considering to show their impact on estimating the inter-patient 2D lung shape variations and (2) using the obtained models in lung field segmentation by utilizing active shape model (ASM) technique. The represented models which showed the inter-patient 2D lung shape variations in two different forms are fully compared and evaluated. The results show that the models along with standard principal component analysis (PCA) can be able to explain more than 95% of total variations in all cases using only first 7 principal component (PC) modes for both lungs. Both models are used in ASM-based segmentation technique for lung field segmentation. The segmentation results are evaluated using leave-one-out cross validation technique. According to the experimental results, the proposed method has average dice similarity coefficient of 97.1% and 96.1% for the right and the left lung, respectively. The results show that the proposed segmentation method is more stable and accurate than other model-based techniques to inter-patient lung field segmentation.
      pubtype: Academic Journal
      doctype:
        diagnostic images
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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