A Novel Method for Cataract Detection and Segmentation Using Nakagami Distribution
Martin Joel Rathnam, M. C. Jobin Christ · Journal of Medical Imaging and Health Informatics · 2022
Early detection of cataract is considered as an important solution to prevent vision loss. An automatic detection of cataract is proposed in this work with the help of histogram approach. In the beginning, noises occur in an image which is also referred to as impulse noise. To eliminate this noise a non-linear type of median filter is matched especially for the morphological filter. These filtering methods help to extract the content of the image by edge detection and segmentation. The quality of the image is evaluated the image enhancing can be obtained by a histogram approach. A normalization method can be used to enhance the image which is also called Contrast stretching. To make morphological functions effective a top-hat filter is used to segment the cataract part in the given image. Nakagami distributions are usually used for extracting required important information of ultrasound details by matching histograms from the radio frequency signals. The extracted information from the Nakagami distribution is obtained by parameter values. The recent techniques used to improve the given image quality in histogram modification method are done by Intentional Camera Movement (ICM) and Unintentional Camera Movement (UCM) to recognize the real image more precisely. In the proposed method the result shows the noise reduction and a better contrast in the output image through parameters values such as Mean Squared Error (MSE) obtained as 17.23 and Peak-Signal-to-Noise Ratio (PSNR) obtained as 35.8.