Age-related macular degeneration (AMD) andDiabetic macular edema (DME) are to lead causes to make a visual loss in people. People are suffered from the use of many time to diagnose and to wait for treatment both of diseases. This paper proposes a step of image segmentation to be divided the optical coherence tomography (OCT) to find the retinal pigment epithelium (RPE) layer and to detect a shape of drusen in RPE layer. Then, the RPE layer is used for finding retinal nerve fiber layer (RNFL) and for detecting a bubble of blood area in RNFL complex. Finally, this method uses a binary classification to classify two diseases characteristic between AMD and DME. We use 16 OCT images of a case study to segmentation and classify two diseases. In the experimental results, 10 images of AMD and 6 images of DME can be detected and classified to accuracy of 87.5%.
This paper presents counting number of points for the P.acne vulgaris using UV Fluorescence and image processing. This proposed method uses a process of image processing as follows. Cropping a UV image is to select a region of interest and, then the cropped image is resized for a suitable size and it (or the color image) is converted to a gray image. Quality of this gray image will be improved for image enhancement using adaptive histogram equalization. Finally, extended maxima transform, i.e. the regional maxima of the H-maxima transform, is used for counting number of the P.acne points. The experimental results show that the accuracy, sensitivity and precision are approximately at 83.75%, 98.22% and 85.04%, respectively. It saves time to analysis of the acne points.
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