تشخیص، دسته‌بندی و اندازه‌گیری اتوماتیک ندول‌های ریوی با استفاده از دسته‌بند ترکیبی در تصاویر سی تی اسکن

نوع مقاله: علمی-پژوهشی

نویسندگان

1 دانشکده مهندسی - دانشگاه شهید چمران اهواز

2 دانشکده پیراپزشکی - دانشگاه علوم پزشکی جندی شاپور

چکیده

سرطان ریه یکی از سخت‌ترین و خطرناک‌ترین سرطان‌ها به شمار می‌رود که می‌تواند در مراحل اولیه، به صورت یک جسم کوچک با قطری کمتر از سه سانتی متر، بنام ندول، مشاهده شود. این ندول‌ها به دو دسته‌ی خوش‌خیم و بدخیم یا سرطانی تقسیم‌بندی می‌شوند. در این مقاله، یک سیستم تشخیصی جهت شناسایی و دسته‌بندی ندول‌های ریوی پیشنهاد می‌شود، که در فاز اول ریه‌ها از تصویر سی‌تی‌اسکن طی عملیات ناحیه‌بندی کانتور فعال جدا می‌شوند. سپس براساس ویژگی‌های سیفت (SIFT)، دسته‌بند بگینگ پیشنهادی تصاویر ریه را به دو دسته سالم و بیمار دسته‌بندی می‌کند. در فاز دوم، براساس یک ناحیه‌بندی گراف کات تمام خودکار، ندول‌ها از تصویر ریه استخراج شده و قطر آن‌ها اندازه‌گیری می‌شود. در پایان، ندول‌ها براساس اندازه و ویژگی‌های بافتی تصویر (هارالیک) به دو دسته خوش‌خیم و بدخیم طبقه‌بندی می‌شوند. جهت ارزیابی عملکرد روش پیشنهادی، از تصاویر مجموعه داده LIDC استفاده گردید و کارایی آن در شناسایی ندولها و در مقایسه با روش‌های دیگر با معیار دقت 97% و از نظر طبقه‌بندی ندول‌ها به خوش‌خیم و بدخیم با دقت 96% قابل رقابت است. 

کلیدواژه‌ها


عنوان مقاله [English]

Automatic Detection, Classification and Measurement of Lung Nodules using Combined Classifiers in CT Scan Images

نویسندگان [English]

  • M. Naderan 1
  • A. Jamshidnejad 2
  • N. Mirderikvand 1
1 Department of Computer Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran
2 Department of Medical Informatics, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran
چکیده [English]

Lung cancer is one of the hardest and most dangerous types of known cancer in the world which can be detected in its beginning stages as a small mass of tissue, less than 3 cm in diameter, called a nodule. These nodules are classified to two classes of benign or malignant. In this paper, a detection system for detection and classification of lung nodules is proposed which in the first phase, lungs are separated from the CT scan images according to the active contour segmentation method. Next, based on the SIFT features the proposed Bagging classifier, classifies the lung images into two classes of patient and healthy. In the second phase, according to a fully automatic Graph-Cut segmentation method the nodules are extracted from patient images and their diameters are measured. Finally, nodules are classified to two classes of benign and malignant based on their size and texture Haralick features. To evaluate the proposed method, images of the LIDC database are used and its performance in detection of nodules compared to other methods has an accuracy of 97% and in classification of nodules to benign and malignant an accuracy of 96% is reached.  

کلیدواژه‌ها [English]

  • Lung nodules
  • CT images
  • Graph-cut segmentation method
  • SIFT features
  • Haralick features
  • Combined classifiers
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