Pished Bunnun

Automatic screening of narrow anterior chamber angle and angle-closure glaucoma based on slit-lamp image analysis by using support vector machine

At present, Van Hericks method is a standard technique used to screen a Narrow Anterior Chamber Angle (NACA) and Angle-Closure Glaucoma (ACG). It can identify a patient who suffers from NACA and ACG by considering the width of peripheral anterior chamber depth (PACD) and corneal thickness. However, the screening result of this method often varies among ophthalmologists. So, an automatic screening of NACA and ACG based on slit-lamp image analysis by using Support Vector Machine (SVM) is proposed. SVM can automatically generate the classification model, which is used to classify the result as an angle-closure likely or an angle-closure unlikely. It shows that it can improve the accuracy of the screening result. To develop the classification model, the width of PACD and corneal thickness from many positions are measured and selected to be features. A statistic analysis is also used in the PACD and corneal thickness estimation in order to reduce the error from reflection on the cornea. In this study, it is found that the generated models are evaluated by using 5-fold cross validation and give a better result than the result classified by Van Hericks method.

Headlights classification for traffic surveillance using a structure tensor method with PADI

Headlights classification is the most important part in traffic surveillance at night time. A main problem of those studies is streetlights and light reflections with similar shapes with headlights. Only one frame information may not be enough for the night-traffic surveillance. This paper proposes a method using previous frame information for classification. To classify headlights, a structure tensor is constructed using 3D Sobel operator masks in a given frame sequence. In addition, we use Positive Accumulative Difference Images (PADI) to remove non-moving bright spots in the scenes. The sample data is obtained by recording video in the night time. Experimental results show that the proposed method performs better than a traditional method about 10 % with regard to Recall, Precision, and F1 score.

An automatic glaucoma screening algorithm using cup-to-disc ratio and ISNT rule with support vector machine

This study introduces a glaucoma screening system which is suitable for both normal eyesight and myopia. For myopia eye, only one clinical indicator is not sufficient. Therefore, cup-to-disc ratio and rim width based on ISNT rule are used as indicators in this screening system. First, image segmentation is performed to obtain optic cup, optic disc and rim width. These information will be used as features in glaucoma classification step. The support vector machine (SVM) is selected as a classifier. According to the results, our proposed method is an efficient system for screening glaucoma.