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DİJİTAL MAMMOGRAMLARDAKİ MEME KİTLELERİNİN BİLGİSAYAR DESTEKLİ TESBİTİ

COMPUTER AIDED DETECTION OF MAMMOGRAPHIC MASSES ON DIGITAL MAMMOGRAMS

Journal Name:

  • İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi

Publication Year:

  • 2005

Key Words:

Keywords (Original Language):

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
This paper presents an automated system for detecting masses in mammogram images. The proposed method is based on a two-step procedure: a. regionsof interest (ROI) specification, b. rule based classification of regions of interest. In the firststep, the intensity values of pixels in mammogram images are used and scanning the pixels in 8 directions isevaluated. By using various thresholds while scanning the pixels, ROIs are specified. In the second step,all ROIs are labeled using Connected Component Labeling (CCL) and two rules are used to categorizeROIs as true masses or not. These rules are based on euclidean distance and regularity values of the ROIs. To test the system’s efficiency, we applied it to images from the Mammographic Image Analysis Societydatabase. The accuracy of the system reaches 88.37% with 0.292 false positives per image
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Abstract (Original Language): 
Bu çalışmada, mammogram görüntülerindeki kitlelerin otomatik olarak tesbit edilebilmesi için bir sistem geliştirilmiştir. Önerilen yöntem iki basamaklıdır: a. ilgi alanlarının belirlenmesi, b. ilgi alanlarının kural tabanlı sınıflandırılması. İlk aşamada görüntü kesitlerindeki piksellerin yoğunluk değerleri hesaplanmış ve her piksel için 8 yönlü tarama işlemi gerçekleştirilmiştir. Bu tarama işlemi sırasında çeşitli eşik değerleri kullanılarak, ilgi alanları belirlenmiştir. İkinci aşamada, tüm ilgi alanları bağlantılı bileşen etiketleme (BBE) yöntemiyle tanımlanmışve iki kural kullanılarak ilgi alanları sınıflandırılmıştır. Bu kurallar ilgi alanlarının öklid uzaklıkları ve biçim değerlerini sorgulamaktadır. Sistemin performansı Mammogram Görüntü Analizi Topluluğu veritabanına uygulanarak ölçülmüştür. Sistemin duyarlılığı görüntü başına 0.292 yanlışpozitif değeriyle %88.37’ye ulaşmaktadır.
FULL TEXT (PDF): 
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  • 8
87-97
  • English

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