Burçin KURT, Kemal TURHAN, Vasif V. NABİYEV

Mamografide Meme Mikrokalsifikasyonları için Otomatik Bilgisayar Destekli Tespit (CADe) ve Tanı (CADx) Sistemi

Mamografide mikrokalsifikasyon (MC) kümelerinin saptanması için otomatik bir bilgisayar destekli tanı sistemi önerilmiştir. Önerilen sistem şüpheli bölgelerin tanımlanması, MC'lerin tespiti, yanlış pozitif indirgeme ve iyi huylu/kötü huylu sınıflamayı içeren bütün bir sistemdir. Şüpheli mikrokalsifikasyon bölgelerinin sınıflandırılması için, gri seviye eş-oluşum matrisi (GLCM) ve istatistiksel özellikler ile çok tabakalı bir perceptron (MLP) sinir ağı kullanıldı. Daha sonra, yanlış pozitif sınıflandırma oranını azaltmak için, gri seviye çalışma uzunluğu matrisi (GLRLM) özellikli kademeli korelasyon sinir ağı (CCNN) kullanılmıştır. Son adımda, tespit edilen MC kümelerinin iyi huylu/kötü huylu sınıflandırması için GLRLM özellikleri ile hibrid yapıda diskriminant analizi ve destek vektör makinesi (SVM) yöntemleri kullanıldı. Çalışma için açık erişimli Mamografik Görüntü Analizi Derneği (MIAS) veri tabanı kullanılmıştır. Deneysel sonuçlar, önerilen algoritmanın meme kanseri tespiti için %86 duyarlılık, %98.3 özgüllük ve 1.163 FPpI oranları elde ettiğini ve meme kanseri tanısı için elde edilen duyarlılık ve özgüllük değerlerinin sırasıyla %100 ve %100 olduğunu ortaya koymuştur. MC kümelenmelerinin görme zorluğu olsa da, önerilen sistem çok tatmin edici sonuçlar vermektedir. Bununla birlikte, gelişmiş sistem; çıktıları yüzdeler ve dönüştürülmüş değerlendirme kategorileri olarak veren tam otomatik bir bütün sistemdir.

AN AUTOMATED COMPUTER-AIDED DETECTION (CADe) AND DIAGNOSIS (CADx) SYSTEM FOR BREAST MICROCALCIFICATIONS IN MAMMOGRAMS

An automated computer aided diagnosis system has been proposed for detection ofmicrocalcification (MC) clusters in mammograms. The proposed system is a whole system includingsuspicious regions identification, MCs detection, false positive reduction and benign/malignclassification. For classification of suspicious microcalcification regions, a multilayer perceptron (MLP)neural network was used with grey level co-occurrence matrix (GLCM) and statistical features. Then todecrease the false positive classification ratio, we used cascade correlation neural network (CCNN) withgrey level run length matrix (GLRLM) features. In the last step, hybrid form of discriminant analysis andsupport vector machine (SVM) methods were used with GLRLM features for benign/malignclassification of detected MC clusters. The open access Mammographic Image Analysis Society (MIAS)database was used for the study. Experimental results show that the proposed algorithm obtained 86%sensitivity, 98.3% specificity and 1.163 FPpI rates for detection an for diagnosis of breast cancer, theobtained sensitivity and specificity values are 100% and 100% respectively. Despite the vision difficultyof MC clusters, the novel system provides very satisfactory results. Furthermore, the developed systemis fully automatic whole system which gives outputs as percentages and transformed assessmentcategories.

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