Tooth Localization with Coarse-to-Fine Auto-Encoders

Year 2018, , 29 - 34, 31.01.2018

Ayşe Betül Oktay

https://doi.org/10.17671/gazibtd.317893

Abstract

 Localization of teeth is a prerequisite task for
most of the computerized methods for dental images such as medical diagnosis
and human identification. Classical deep learning architectures like
convolutional neural networks and auto-encoders seem to work well for tooth
detection, however, it is non-trivial because of the large image size. In this study,
a coarse-to-fine stacked auto-encoder architecture is presented for detection
of teeth in dental panoramic images. The proposed architecture involves
cascaded stacked auto-encoders where sizes of the input patches increase with
the successive steps. Only the detected candidate tooth patches are fed into
the successive layers, thus the irrelevant patches are eliminated. The proposed
architecture decreases the cost of detection process while providing precise
localization. The method is tested and validated on a dataset containing 206
dental panoramic images and the results are promising.

Keywords

Coarse-to-Fine auto-encoder, detection, deep learning

Büyükten Küçüğe Oto-kodlayıcılar ile dişlerin konumlandırılması*

Abstract

Dişlerin
lokalizasyonu, bilgisayar destekli gerçekleştirilen dental görüntülerden insan
kimliklendirme ve medikal tanı için bir önşarttır. Konvolusyonel sinir ağları
ve oto-kodlayıcılar gibi klasik derin öğrenme mimarileri diş tanıma işlemi için
başarılı gözükse de dental görüntülerin çok büyük olması nedeniyle tüm arama
uzayının taranması mümkün gözükmemektedir. Bu çalışmada, büyükten-küçüğe
yığınlanmış bir oto-kodlayıcı yapısı ile dental görüntülerden dişleri tanıyan
bir sistem sunulmuştur. Önerilen mimari, girdi görüntü yamalarının boyutlarının
her kademede arttığı bir kademeli yığınlanmış oto-kodlayıcı yapısı içerir.
İlerdeki katmanlara sadece bulunan aday diş yamaları verilir; böylece alakasız
yamalar elimine edilmiş olur. Önerilen mimari tanıma aşamasındaki maliyeti
düşürmekle beraber hassas konumlandırma imkanı sunar. Geliştirilen metot, 206
dental panoramik görüntü içeren bir veri kümesi üzerinde test edilmiştir ve
sonuçlar ümit vericidir.

Keywords

Büyükten –küçüğe oto-kodlayıcı, tanıma, derin öğrenme

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