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A hybrid lung segmentation algorithm based on histogram-based fuzzy C-means clustering

Doganay, Emine; Kara, Sada; Ozcelik, Hatice Kutbay; Kart, Levent


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        "affiliation": "Fatih Univ, Biomed Inst Engn, Buyukcekmece Campuss, Istanbul, Turkey", 
        "name": "Doganay, Emine"
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      {
        "affiliation": "Fatih Univ, Biomed Inst Engn, Buyukcekmece Campuss, Istanbul, Turkey", 
        "name": "Kara, Sada"
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      {
        "affiliation": "Yedikule Pulmonol & Thorac Surg Hosp, Pulmonol Dept, Istanbul, Turkey", 
        "name": "Ozcelik, Hatice Kutbay"
      }, 
      {
        "affiliation": "Fatih Univ, Med Fac, Pulm Dept, Buyukcekmece Campuss, Istanbul, Turkey", 
        "name": "Kart, Levent"
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    "description": "The lung is an essential organ and is dark in Computed Tomography (CT) images because of air. Lung segmentation and correct lung region separation is a prerequisite for the development of computer-aided diagnostic algorithms and disease treatment planning. However, this remains a nontrivial problem because of lung anatomical structures. Here, we addressed this problem and proposed a reliable and robust solution that is based on a histogram-based fuzzy C-means (FCM) algorithm and morphological mathematical algorithms. There were 1632 high resolution CT slices with 1 mm thickness used from asthma patients with low dose; right and left lungs were classified using the proposed algorithm. We extracted right lung regions with 96.05% accuracy and left lung regions at 96.32%. The computation time is 1.3 s per slice.", 
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      "issue": "6", 
      "pages": "638-648", 
      "title": "COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION", 
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