نوع مقاله : مقاله کامل پژوهشی
نویسنده
استادیار، گروه بیوالکتریک، دانشکده‌ی مهندسی پزشکی، دانشگاه صنعتی سهند، تبریز، ایران
چکیده
بررسی شهودی لایه‌های شبکیه در تصاویر برش‌نگاری همدوسی اپتیکی حوزه‌ی طیف (SD-OCT) یکی از روش‌های اصلی مورد استفاده‌ی پزشکان برای تشخیص بیماری‌های شبکیه است. این روش با چالش‌هایی مانند نویز، پیچیدگی تصاویر و نزدیکی لایه‌ّهای شبکیه مواجه می‌باشد. در سال‌های اخیر تشخیص خودکار بیماری‌های شبکیه‌ی چشم به یکی از موضوعات مهم بالینی در حوزه‌ی بینایی کامپیوتر تبدیل شده است. در این تحقیق روشی جدید برای طبقه‌بندی کارآمد چندکلاسه‌ی خودکار تصاویر SD-OCT ارائه شده که متشکل از پنج مرحله‌ی پیش‌پردازش، تشخیص لایه‌ها، استخراج ویژگی‌ها، کاهش بعد، و طبقه‌بندی تصویر است. بررسی شکل لایه‌ی RNFL و پیوند IS/OS به عنوان روشی بالینی در تصمیم‌گیری‌های پزشکان برای تشخیص بیماری‌های شبکیه موثر است. از این رو در این پژوهش با الهام از این روش تشخیص بالینی، لایه‌ی RNFL و پیوند IS/OS توسط روشی جدید مبتنی بر الگوریتم بهبود رگ فرنگی و گرادیان تصویر تشخیص داده شده است. سپس با استخراج و انتخاب انواعی از ویژگی‌های موثر از لایه‌ها، الگوریتمی بر پایه‌ی درخت تصمیم ترکیبی برای طبقه‌بندی تصاویر شبکیه پیشنهاد شده و در قالب یک نرم‌افزار کاربردی در متلب ارائه شده است. روش پیشنهادی روی تصاویر دو پایگاه داده‌ی شناخته شده‌ی دوک و کرمنی ارزیابی شده است. بر اساس نتایج، دقت، حساسیت، اختصاصیت، درستی، نرخ منفی نادرست و معیار F1 روش پیشنهادی در پایگاه داده‌ی دوک به ترتیب برابر با 7/98، 8/98، 4/99، 1/99، 3/1 و 7/98 درصد و در پایگاه کرمنی به ترتیب برابر با 8/96، 7/96، 9/98، 4/98، 2/3 و 7/96 درصد است. نتایج نشان‌دهنده‌ی برتری روش پیشنهادی در مقایسه با سایر روش‌های مقایسه‌ای است. در مجموع به کارگیری ویژگی‌های کارآمد از لایه‌های تاثیرگذار شبکیه و توانمندی روش طبقه‌بندی، موجب ارتقای عمل‌کرد روش پیشنهادی در مقایسه با روش‌های پیچیده‌تر پیشین شده است.
کلیدواژه‌ها
موضوعات
عنوان مقاله [English]
An Efficient Method for Automatic Multi-Class Classification of SD-OCT Images of Human Eye Based on RNFL layer and the IS/OS Junction Detection and Ensemble Decision Tree
نویسنده [English]
- Sina Shamekhi
Assistant Professor, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran
چکیده [English]
Intuitive examination of retinal layers in Spectral-Domain Optical Coherence Tomography (SD-OCT) images is one of the main methods used by physicians to diagnose retinal diseases. This method faces challenges such as noise and image complexity and the proximity of retinal layers. In recent years, the automatic diagnosis of retinal diseases has become an important clinical issue in computer vision. In this research, a new method for efficient multi-class automatic classification of SD-OCT images has been proposed. This method consists of five stages, preprocessing, layer recognition, feature extraction, and image classification. Examination of the shape of the RNFL layer and IS/OS junction as a clinical method is influential in physicians' decisions to diagnose retinal diseases. Therefore, in this study, inspired by this clinical diagnosis method, the RNFL layer, and the IS/OS junction have been detected by a new method based on the Frangi vessel enhancement algorithm and the gradient of the image. Then, by extracting and selecting several efficient features from the curves of the layers, an algorithm based on the ensemble decision tree has been proposed for classifying SD-OCT images of the retina and presented as a MATLAB application. The proposed method has been evaluated using images of two well-known databases of Duke and Kermany. Based on the results, precision, sensitivity, specificity, accuracy, miss rate and F1-score of the proposed method in Duke database were equal to 98.7, 98.8, 99.4, 99.1, 1.3, and 98.7, respectively, and in Kermany database were 96.8, 96.7, 98.9, 98.4, 3.2 and 96.7 respectively. The results show the superiority of the proposed method compared to other comparative methods. In summary, the use of efficient features of retinal effective layers and a powerful algorithm for classification has improved the performance of the proposed method compared to previous more complex methods.
کلیدواژه‌ها [English]
- SD-OCT
- Retina
- Frangi
- Ensemble Decision Tree
- Classification
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