Retinal SD-OCT image-based pituitary tumor screening

Min He; Weifang Zhu; Xinjian Chen

doi:10.1117/12.2254199

3 March 2017 Retinal SD-OCT image-based pituitary tumor screening

Min He, Weifang Zhu, Xinjian Chen

Author Affiliations +

Proceedings Volume 10134, Medical Imaging 2017: Computer-Aided Diagnosis; 101343B (2017) https://doi.org/10.1117/12.2254199
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

In most cases, the pituitary tumor compresses optic chiasma and causes optic nerves atrophy, which will reflect in retina. In this paper, an Adaboost classification based method is first proposed to screen pituitary tumor from retinal spectral- domain optical coherence tomography (SD-OCT) image. The method includes four parts: pre-processing, feature extraction and selection, training and testing. First, in the pre-processing step, the retinal OCT image is segmented into 10 layers and the first 5 layers are extracted as our volume of interest (VOI). Second, 19 textural and spatial features are extracted from the VOI. Principal component analysis (PCA) is utilized to select the primary features. Third, in the training step, an Adaboost based classifier is trained using the above features. Finally, in the testing phase, the trained model is utilized to screen pituitary tumor. The proposed method was evaluated on 40 retinal OCT images from 30 patients and 30 OCT images from 15 normal subjects. The accuracy rate for the diseased retina was (85.00±16.58)% and the rate for normal retina was (76.68±21.34)%. Totally average accuracy of the Adaboost classifier was (81.43± 9.15)%. The preliminary results demonstrated the feasibility of the proposed method.

Citation Download Citation

Min He, Weifang Zhu, and Xinjian Chen "Retinal SD-OCT image-based pituitary tumor screening", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101343B (3 March 2017); https://doi.org/10.1117/12.2254199

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KEYWORDS

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Optical coherence tomography

Feature extraction

Retina

Image segmentation

Image classification

Tissue optics

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