Enhancement of Computed Tomography (CT) Images of the Human Spine and Spine Segmentation

Year 2023, Issue: 52, 95 - 103, 15.12.2023

Murat Türkmen , Zeynep Orman

Abstract

Today, there are many health problems associated with the human spine. Biomedical images are frequently used in the detection of spinal disorders. Chief among these is IT technology. Accurate and rapid detection of spinal disorders from CT images plays an important role in patient treatment. For this, quality images are needed in the evaluation phase of CT images. In this study, the discrete wavelet transform method was used for image enhancement using VerSe dataset. The vertebrae in the image are labeled with the connected component method. The vertebrae in the enhanced CT images were segmented by adapting the convolutional neural network U-Net method. With respect to the enhancement and segmentation method, the proposed model had 99.4% accuracy rate, 99.8% specificity rate, and 99.2% sensitivity rate. The dice segmentation score was increased from 65.1% to 74.5% as a result of improving the raw images. The results of the study were compared with other segmentation results made with the VerSe data set in the literature; differences were stated and more successful results were shown.

Keywords

Image processing, CNN U-Net model, computed tomography, spine segmentation

İnsan Omurgasına Ait Bilgisayarlı Tomografi Görüntülerinin İyileştirilmesi ve Omur Segmentasyonu

Abstract

Günümüzde insan omurgası ile ilişkili birçok sağlık sorunu mevcuttur. Omurga rahatsızlıklarının tespitinde biyomedikal görüntüler sıklıkla kullanılmaktadır. Bunların önde geleni ise Bilgisayarlı Tomografi (BT) teknolojisidir. BT görüntülerinden omurga rahatsızlıklarının doğru ve hızlı tespit edilmesi hasta tedavisinde önemli rol oynar. Bunun için BT görüntülerinin değerlendirme aşamasında kaliteli görüntülere ihtiyaç duyulmaktadır. Bu çalışmada VerSe veri kümesi kullanılarak görüntü iyileştirilmesi amacıyla ayrık dalgacık dönüşüm (discrete wavelet transform) yöntemi kullanılmıştır. Bağlı bileşen yöntemi ile görüntüdeki omurlar etiketlenmiştir. İyileştirilmiş BT görüntülerindeki omurlar evrişimli sinir ağı olan U-Net yöntemi uyarlanarak segmente edilmiştir. İyileştirme ve segmentasyon yöntemi uygulandıktan sonra doğruluk oranı %99.4, özgüllük oranı %99.8 ve hassasiyet oranı %99.2 olarak elde edilmiştir. Dice segmentasyon skoru ham görüntülerin iyileştirilmesi sonucunda %65.1’den %74.5’e yükseltilmiştir. Çalışmanın sonuçları literatürde VerSe veri kümesi ile yapılan diğer segmentasyon sonuçları ile kıyaslanmış; farklılıkları belirtilmiş ve daha başarılı sonuçlar elde edildiği gösterilmiştir.

Keywords

Görüntü işleme, CNN U-Net modeli, bilgisayarlı tomografi, omur segmentasyonu

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