Method for Automated Selection of the Trabecular Area in Digital Periapical Radiographic Images Using Morphological Operations / 대한의료정보학회지

OBJECTIVES: The aim of this study is to propose a method that automatically select the trabecular bone area in digital periapical radiographic images using a sequence of morphological operations. METHODS: The study involved 50 digital periapical radiographic images of women aged from 36 to 58 years old. The proposed method consists of three stages: teeth detection, trabecular identification, and validation. A series of morphological operations-top-hat and bottom-hat filtering, automatic thresholding, closing, labeling, global thresholding, and image subtraction-are performed to automatically obtain the trabecular bone area in images. For validation, the results of the proposed method were compared with those of two dentists pixel by pixel. Three parameters were used in the validation: trabecular area, percentage of agreed area, and percentage of disagreed area. RESULTS: The proposed method obtains the trabecular bone area in a polygon. The obtained trabecular bone area is usually larger than that of previous studies, but is usually smaller than the dentists'. On average over all images, the trabecular area produced by the proposed method is 5.83% smaller than that identified by dentists. Furthermore, the average percentage of agreed area and the average percentage of disagreed area of the proposed method against the dentists' results were 75.22% and 8.75%, respectively. CONCLUSIONS: The shape of the trabecular bone area produced by the proposed method is similar and closer to that identified by dentists. The method, which consists of only simple morphological operations on digital periapical radiographic images, can be considered for selecting the trabecular bone area automatically.

Automated Detection of Retinal Nerve Fiber Layer by Texture-Based Analysis for Glaucoma Evaluation / 대한의료정보학회지

OBJECTIVES: The retinal nerve fiber layer (RNFL) is a site of glaucomatous optic neuropathy whose early changes need to be detected because glaucoma is one of the most common causes of blindness. This paper proposes an automated RNFL detection method based on the texture feature by forming a co-occurrence matrix and a backpropagation neural network as the classifier. METHODS: We propose two texture features, namely, correlation and autocorrelation based on a co-occurrence matrix. Those features are selected by using a correlation feature selection method. Then the backpropagation neural network is applied as the classifier to implement RNFL detection in a retinal fundus image. RESULTS: We used 40 retinal fundus images as testing data and 160 sub-images (80 showing a normal RNFL and 80 showing RNFL loss) as training data to evaluate the performance of our proposed method. Overall, this work achieved an accuracy of 94.52%. CONCLUSIONS: Our results demonstrated that the proposed method achieved a high accuracy, which indicates good performance.