Portable system for identifying potential cases of diabetic macular oedema using image processing and artificial intelligence

A portable system for detecting potential cases of diabetic macular oedema using image processing, characterized in that it comprises:

• a. For the capturing of the image, a portable ophthalmoscopy device is required that has at least the following technical characteristics:
• i. A continuous field angle of 45° to 30°+35 D . . . −35 D,
• ii. A 40 mm lens (patient's eye—objective), at least 4 IR LEDs (each max. 100 mW),
• iii. A 3.97″ TFT LCD touch screen, 800×480 pixels, 16.7 million colors, anti-reflective film,
• iv. USB connection and/or Bluetooth-type wireless connection,
• v. Capture button and power button;
• this equipment captures the image that is sent to the system for processing and analysis,
• b. A device with a development card is required in order to process the image, which includes:
• i. An electrical connector,
• ii. An LCD screen,
• iii. An LED power indicator,
• iv. A power button,
• v. A wireless Bluetooth-, infrared-, or Zigbee-type module for wireless data reception and one or more USB and/or micro-USB ports for wired data transmission,
• vi. A microprocessor with at least 1 GB of RAM at 1.4 GHz and a 64-bit processor,
• c. For the image processing, an algorithm for image processing for the pre-diagnosis of diabetic macular oedema comprises the following steps:
• i. Reading of the image by wired or wireless means,
• ii. Conversion of the image to grayscale,
• iii. Automatic selection of a region through detection of characteristics corresponding to vascular intersections from which the image is cropped;
• the automatic detection of vascular intersections is performed using a search algorithm based on changes in intensity gradients in the image components and an error minimization process, for which the following steps are performed:
• 1. Decomposition, scaling, and skeletonization of image components,
• 2. Search for changes in intensity through gradient changes, and morphological search for branches,
• 3. Filling the vector of characteristics,
• 4. Minimization of error,
• 5. Counting of intersection points
• iv. Binarization of the image,
• v. A decision is made as to whether the noise has been eliminated,
• vi. Removal of noise in the image through morphological operations and automatic identification of objects referred to as noise (objects with an area smaller than 200 pixels),
• vii. Skeletonization of the noise-free image through morphological operations, identification of vascular intersections and measurement of the distance between them,
• viii. Rigid body transformation consisting of image scaling, rotation, and translation,
• ix. Image feature segmentation and extraction,
• x. Application of the artificial intelligence system:
• 1. Extraction of statistical characteristics, thereby obtaining:
• energy, entropy, homogeneity, contrast, brightness, correlation, average RGB, average R, average G, and average B of the image,
• 2. Classifier that makes use of a neural network, first estimating the thickness of the macula and then classifying the result obtained,
• 3. Pre-diagnosis of diabetic macular oedema, which is carried out after the estimation of the macular thickness.