Automatic computer aided analysis algorithms and system for adrenal tumors on CT images.

Background: The adrenal tumor will disturb the secreting function of adrenocortical cells, leading to many diseases. Different kinds of adrenal tumors require different therapeutic schedules.Objective: In the practical diagnosis, it highly relies on the doctor's experience to judge the tumor type by...

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Publicado en:Technology & Health Care Vol. 25; no. 6; pp. 1105 - 1119
Autores principales: Hanchao Chaia, Yi Guoa, Yuanyuan Wanga, Guohui Zhou, Chai, Hanchao, Guo, Yi, Wang, Yuanyuan, Zhou, Guohui
Formato: Journal Article
Publicado: Sage Publications Inc. 2017
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 2017
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      pub: Sage Publications Inc.
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        atl: Automatic computer aided analysis algorithms and system for adrenal tumors on CT images.
      aug:
        au:
          Hanchao Chaia
          Yi Guoa
          Yuanyuan Wanga
          Guohui Zhou
          Chai, Hanchao
          Guo, Yi
          Wang, Yuanyuan
          Zhou, Guohui
        affil: Department of Electronic Engineering, Fudan University, Shanghai, China
      sug:
        subj:
          Algorithms
          Adrenal Gland Neoplasms Pathology
          Tomography, X-Ray Computed Methods
          Adrenal Gland Neoplasms
          Radiographic Image Interpretation, Computer-Assisted Methods
          Reproducibility of Results
      ab: Background: The adrenal tumor will disturb the secreting function of adrenocortical cells, leading to many diseases. Different kinds of adrenal tumors require different therapeutic schedules.Objective: In the practical diagnosis, it highly relies on the doctor's experience to judge the tumor type by reading the hundreds of CT images.Methods: This paper proposed an automatic computer aided analysis method for adrenal tumors detection and classification. It consisted of the automatic segmentation algorithms, the feature extraction and the classification algorithms. These algorithms were then integrated into a system and conducted on the graphic interface by using MATLAB Graphic user interface (GUI).Results: The accuracy of the automatic computer aided segmentation and classification reached 90% on 436 CT images.Conclusion: The experiments proved the stability and reliability of this automatic computer aided analytic system.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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