Meme Kanserinin K-Ortalama Kümeleme ve Otsu Eşikleme Segmentasyon Yöntemleri İle Teşhisi

Meme kanseri, kadınlar arasında büyük oranda artış göstermiştir. Ancak erken teşhisiyle, tedaviye olumlu cevap verilebilmektedir. Araştırmacılar, hastalığı erken ve doğru tespit edebilme adına görüntüleme yöntemlerinde çeşitli çalışmalar yapmaktadır. Bu çalışmada; TCİA görüntü veri bankasından alınan 9 kanserli görüntüde K ortalama kümeleme ve otsu eşikleme yöntemi ile tümör tespiti yapılmıştır. Radyolog tarafından işaretli referans görüntüleri ile (ground truth) ile karşılaştırarak, başarım (performans) metrikleri değerlendirilmiştir. Kümeleme işlemi için sırasıyla TPR (Doğru Pozitif Oranı) 0.89, FPR (Yanlış Pozitif Oranı) 0.14, benzerlik 0.67, doğruluk 0.87, duyarlılık 0.89, hassasiyet 0.86, özgüllük 0.87, F puanı 0.87 bulunmuştur. Otsu için TPR (Doğru Pozitif Oranı) 0.84, FPR (Yanlış Pozitif Oranı) 0.12, benzerlik 0.73, doğruluk 0.84, duyarlılık 0.84, hassasiyet 0.86, özgüllük 0.87, F puanı 0.84 olarak hesaplanmıştır. Bu çalışmada, daha az veri kümesi ile daha kısa sürede, görüntü işleme yöntemlerini kullanarak, piksel tabanlı segmentasyon ile tümör sınırlarının daha doğru belirlenmesi, insana duyulan ihtiyacın azalması ve sağlık alanında sahada görüntülemede kullanılan tıbbi cihazların bilgisayar destekliyazılımlarla geliştirilmesi, mamografik tarama sistemlerinin doğru ve hızlı bir şekilde yapılabilmesi amaçlanmıştır. Sonuç olarak, her iki yöntem de başarılı, sahada kullanılabilir ve birbirine yakın başarım değerleri bulunmuştur.

Anahtar Kelimeler:

Mamografi, Görüntü işleme, K ortalama kümeleme

Diagnosis of Breast Cancer by K-Mean Clustering and Otsu Thresholding Segmentation Methods

Breast cancer has increased decidedly among women. But with early diagnosis, a positive response to treatment can be given. Researchers are conducting various studies in imaging methods to detect the disease early and accurately. In this study, 9 cancerous images taken from the TCİA image data bank were detected by K-mean clustering and the Otsu threshold method. Performance metrics were evaluated by comparing them with marked reference images (ground truth) by the radiologist. For the clustering process, TPR (True Positive Rate) 0.89, FPR (False Positive Rate) 0.14, similarity 0.67, accuracy 0.87, sensitivity 0.89, exact hit ratio 0.86, specificity 0.87, F Score 0.87 were found, respectively. For Otsu, TPR (True Positive Rate) 0.84, FPR (False Positive Rate) 0.12, similarity 0.73, accuracy 0.84, sensitivity 0.84, exact hit 0.86, specificity 0.87, F Score 0.84 were calculated. The aim of this study is to determine the tumor boundaries more accurately and to use them in imaging devices in the field of health with pixel-based segmentation. As a result, both methods were successful can be used in the field and close to each other.

Keywords:

Mammography, Image processing, K-mean clustering,

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