Sintigrafik Görüntülerden Tiroid Nodülleri için Bilgisayar Destekli Tanı Sistemi

Year 2023, , 559 - 567, 27.09.2023

Aysun Sezer , Emre Alptekin

https://doi.org/10.21205/deufmd.2023257504

Abstract

Modern tıpta, anatomik bölgelerin segmentasyonu yoluyla görüntü tanıma ve tıbbi görüntüler kullanılarak hastalıkların otomatik olarak sınıflandırılması, çeşitli hastalıkların teşhisinde artan bir potansiyel role sahiptir. Tiroid sintigrafisi, tiroid bezi bozukluklarının teşhisi için kullanılan görüntüleme yöntemlerinden biridir. Çalışmamızda optimize edilmiş Bayesian yerel olmayan ortalama filtresi ile sintigrafi görüntülerinde benek gürültüsü azaltılmıştır. Tiroid bezi lokal bazlı aktif kontur yöntemi ile otomatik olarak segmentlere ayrıldı ve tiroid bezi patolojileri konvolüsyonel sinir ağları (CNN) ile sınıflandırıldı. Önerilen bilgisayar destekli tanı (CAD) sistemi, Histogramlar Piramidi Oryantasyon Gradyanları (PHOG), Gri Düzey Ortak Oluşum Matrisi (GLCM), Yerel Yapılandırma Modeli (LCP) ve Özellik Çantası (BoF) yöntemleriyle karşılaştırıldı. Tiroid bezinin sintigrafik görüntülerinin ortak patolojik paternleri, CNN tarafından %91.19 ile başarıyla sınıflandırıldı. Karşılaştırmalı yöntemler sırasıyla %7.61, %86.04, %88.91 ve %85.72 genel başarı oranları sağlayan PHOG, GLCM, LCP ve BoF yöntemleriydi. Önerilen CNN tabanlı otomatik teşhis sistemi, el yapımı yöntemlere kıyasla umut verici sonuçlar vermiştir.

Keywords

Derin öğrenme, görüntü sınıflama, Aktif kontur, gürültü giderme, tiroid nodül, sintigrafi

Computer Aided Diagnosis System of Thyroid Nodules from Scintigraphic Images

Abstract

In modern medicine, image recognition via segmentation of anatomical regions and automatic classification of diseases using medical images has a growing potential role in diagnosis of various diseases. Scintigraphy of thyroid is one of the established imaging modalities for diagnosis of thyroid gland disorders. In our study, the speckle noise was reduced in the scintigraphy images with the optimized Bayesian nonlocal mean filter. The thyroid gland was automatically segmented by local based active contour method and the thyroid gland pathologies were classified with convolutional neural networks (CNN). The proposed computer aided diagnosis (CAD) system was compared with Pyramid of Histograms of Orientation Gradients (PHOG), Gray Level Co occurrence Matrix (GLCM), Local Configuration Pattern (LCP) and Bag of Feature (BoF) methods. The common pathological patterns of scintigraphic images of the thyroid gland were successfully classified by CNN with an overall success rate of 91.19%. The comparative methods were PHOG, GLCM, LCP and BoF methods which provided overall success rates of 7.61%, 86.04%, 88.91% and 85.72% respectively. The proposed CNN based automatic diagnosis system provided promising results compared to handcrafted methods.

Keywords

Deep Learning, image classification, Active contour, noise reduction, thyroid nodules, Scintigraphic images

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