Center Deviation Measurement of Color Contact Lenses Based on a Deep Learning Model and Hough Circle Transform.

Ensuring the quality of color contact lenses is vital, particularly in detecting defects during their production since they are directly worn on the eyes. One significant defect is the "center deviation (CD) defect", where the colored area (CA) deviates from the center point. Measuring the extent of deviation of the CA from the center point is necessary to detect these CD defects. In this study, we propose a method that utilizes image processing and analysis techniques for detecting such defects. Our approach involves employing semantic segmentation to simplify the image and reduce noise interference and utilizing the Hough circle transform algorithm to measure the deviation of the center point of the CA in color contact lenses. Experimental results demonstrated that our proposed method achieved a 71.2% reduction in error compared with existing research methods.

Differential Screening of Herniated Lumbar Discs Based on Bag of Visual Words Image Classification Using Digital Infrared Thermographic Images.

Doctors in primary hospitals can obtain the impression of lumbosacral radiculopathy with a physical exam and need to acquire medical images, such as an expensive MRI, for diagnosis. Then, doctors will perform a foraminal root block to the target root for pain control. However, there was insufficient screening medical image examination for precise L5 and S1 lumbosacral radiculopathy, which is most prevalent in the clinical field. Therefore, to perform differential screening of L5 and S1 lumbosacral radiculopathy, the authors applied digital infrared thermographic images (DITI) to the machine learning (ML) algorithm, which is the bag of visual words method. DITI dataset included data from the healthy population and radiculopathy patients with herniated lumbar discs (HLDs) L4/5 and L5/S1. A total of 842 patients were enrolled and the dataset was split into a 7:3 ratio as the training algorithm and test dataset to evaluate model performance. The average accuracy was 0.72 and 0.67, the average precision was 0.71 and 0.77, the average recall was 0.69 and 0.74, and the F1 score was 0.70 and 0.75 for the training and test datasets. Application of the bag of visual words algorithm to DITI classification will aid in the differential screening of lumbosacral radiculopathy and increase the therapeutic effect of primary pain interventions with economical cost.