Biomedical Science Letters (대한의생명과학회지)

The Korean Society for Biomedical Laboratory Sciences (대한의생명과학회)
권호 표지

Statistical Techniques based Computer-aided Diagnosis (CAD) using Texture Feature Analysis: Applied of Cerebral Infarction in Computed Tomography (CT) Images

  • Lee, Jaeseung (Department of Radiological Science, Dongeui University) ;
  • Im, Inchul (Department of Radiological Science, Dongeui University) ;
  • Yu, Yunsik (Department of Radiological Science, Dongeui University) ;
  • Park, Hyonghu (Department of Radiology, Bong-Seng Memorial Hospital) ;
  • Kwak, Byungjoon (Department of Faculty Health, Graduate School, Daegu Haany University)
  • Received : 2012.08.06
  • Accepted : 2012.08.22
  • Published : 2012.12.31
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Abstract

The brain is the body's most organized and controlled organ, and it governs various psychological and mental functions. A brain abnormality could greatly affect one's physical and mental abilities, and consequently one's social life. Brain disorders can be broadly categorized into three main afflictions: stroke, brain tumor, and dementia. Among these, stroke is a common disease that occurs owing to a disorder in blood flow, and it is accompanied by a sudden loss of consciousness and motor paralysis. The main types of strokes are infarction and hemorrhage. The exact diagnosis and early treatment of an infarction are very important for the patient's prognosis and for the determination of the treatment direction. In this study, texture features were analyzed in order to develop a prototype auto-diagnostic system for infarction using computer auto-diagnostic software. The analysis results indicate that of the six parameters measured, the average brightness, average contrast, flatness, and uniformity show a high cognition rate whereas the degree of skewness and entropy show a low cognition rate. On the basis of these results, it was suggested that a digital CT image obtained using the computer auto-diagnostic software can be used to provide valuable information for general CT image auto-detection and diagnosis for pre-reading. This system is highly advantageous because it can achieve early diagnosis of the disease and it can be used as supplementary data in image reading. Further, it is expected to enable accurate medical image detection and reduced diagnostic time in final-reading.

Keywords

References

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