JBINS 2017 December

Showing 9–10 of 10 results

  • Automatic Diabetic Retinopathy Screening Based on Morphological Operation and Support Vector Machine Integration

    $15.00

    Abstract: Diabetic retinopathy is one of the most frequent causes of blindness due to diabetes. Primary screening is essential due to prerequisite step toward the diagnosis of diabetic retinopathy in order to prevent vision loss or blindness. This paper presents the methods to discriminate between healthy images and diabetic retinopathy images on the retinal images. The proposed method involves three main steps. Initially, the image is preprocessed to remove small noises and enhance the contrast of the image. Secondly, Kirsch edge detection is utilized to detect the bright lesions. Subsequently, the red lesions are detected depending on top-hat morphological filtering methods. Then the bright and dark lesions are combined by using logical AND operator. In order to be left only pathological signs, the noises near the vicinity of the optic disc and blood vessels are further removed using blob analysis. Finally, morphological features are extracted and fed to the SVM classifier. The proposed method was evaluated with three datasets containing 229 images. It achieved the accuracy of 90%, sensitivity of 86.33% and specificity of 98.55% with the average computational time 8 seconds per image. The method is simple and fast, easy to implement and the result is promising.

  • Accuracy verification of knife tip positioning with position and orientation estimation of the actual liver for liver surgery support system

    $15.00

    Abstract: We are developing a surgical support system for liver abdominal surgery to prevent surgical accidents. Our support system can detect proximity of certain body parts and can make a warning when the knife approaches the critical part to be excised, such as large blood vessels. The system has two distance cameras with different features, and these cameras are located above the operating table. One camera measures the liver shape and the other tracks the position of a surgical knife during surgery. In this report, to evaluate the accuracy of the distance between the position of the knife tip and the position of the blood vessels, the position and orientation of the liver were estimated using depth images of mock liver and virtual liver by simulated annealing algorithm, and the distance between the knife tip and the blood vessel in the mock liver was measured. The experimental results showed the maximum average error of the measured distance was 5.76 mm.