تشخیص خودکار ندول‌های ریوی با استفاده از آنتروپی فازی-تیسالیس و ماشین بردار پشتیبان

نویسندگان

1 گروه مهندسی کامپیوتر - پردیس فنی و مهندسی - دانشگاه یزد

2 دانشکده علوم پزشکی - دانشگاه شیراز

چکیده

ندول‌های ریوی بافت اولیه سرطان ریه هستند. طراحی سیستم تشخیص به­کمک کامپیوتر می‌تواند جهت بالا بردن دقت متخصص در زمینه­ی تشخیص و توصیف این بافت به­کار رود. در سال­های اخیر پژوهش­هایی در این زمینه صورت گرفته با این حال سیستم های CAD کنونی با حساسیت کم دارای مثبت کاذب بالایی هستند. بنابراین هدف اصلی این پژوهش توسعه ی سیستم CAD ای است که قادر باشد مکان اکثر ندول ها را تا جایی که امکان دارد تشخیص دهد و در کنار این امر تعداد مثبت کاذب آن کم باشد. قطعه‌بندی تصویر ریه و تشخیص ندول گام­های اصلی این پژوهش هستند. در گام قطعه‌بندی، ترکیب روش­های آنتروپی فازی-تیسالیس و آستانه‌گذاری مورد استفاده قرار می­گیرند. در مرحله تشخیص ندول، ویژگی‌های شدت روشنایی و هندسی ندول استخراج می­شوند و نواحی مشکوک با کمک ماشین بردار پشتیبان مشخص می­گردد. استفاده از ویژگی­های شدت روشنایی باعث افزایش حساسیت می­شود درحالی­که به­کارگیری ویژگی­های هندسی باعث کاهش مثبت کاذب می­شود.  به‌منظور ارزیابی روش پیشنهادی، از تصاویر مجموعه داده­های LIDC و تابا استفاده شده است. حساسیت طبقه‌بندی حاصل 92% به­دست­آمده است. نتایج به­دست­آمده در مقایسه با نتایج گزارش­شده توسط سایر مقاله­ها، مفید بودن این پژوهش را نشان می‌دهد.

کلیدواژه‌ها

عنوان مقاله [English]

Lung Nodule Detection Using Fuzzy-Tsallis Entropy and SVM

نویسندگان [English]

  • A. Ghanbari 1
  • A.M . Latif 1
  • M. Rezaeian 1
  • A. R. Shakibafard 2
1 Faculty of Electrical and Computer Engineering, University of Yazd, Yazd, Iran
2 School of Medical Sciences, Shiraz University of Medical Sciences, Shiraz, Iran,
چکیده [English]

Lung nodules are the primary tissue of lung cancer. Designing a computer-aided diagnosis system can be used in order to enhance the accuracy of the radiologist in the detection and description of these tissues. The aim of this study is to provide an approach to detection of lung nodules on CT scan images. This study was conducted in two main stages of nodule segmentation and detection.
 In the segmentation stage, combination procedures such as Fuzzy-Tsallis entropy and thresholding were used. In the nodule detection stage, intensity and geometric features are extracted and suspicious areas are determined by support vector machine. Using intensity features causes increasing true positive rate while using geometrical features causes decreasing false positive rate. To evaluate the proposed method, images of LIDC and Taba datasets were used. Classification sensitivity is 92%. The obtained results comparing to those reported by other articles, indicate the usefulness of the research.

کلیدواژه‌ها [English]

  • Lung nodule
  • segmentation
  • as Fuzzy-Tsallis Entropy
  • thresholding
  • support vector machine

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