Skin color varies depending upon the individual's melanin content; the more Ultraviolet (UV) radiation exposure, the more increases in melanin, and the darker the skin color. Protection from UV exposure is important because it is associated with the risk of pigmentation and skin cancer. Currently, Sun Protection Factor (SPF) and Protection of A (PA) indicated on sunscreen bottles are difficult to apply universally; a method of evaluating sunscreen performance that is customized to individual skins is required. This study confirmed that differences in skin color can be distinguished based on the pixels of images acquired with a UV camera; thus differences in the degree of UV protection could be analyzed according to skin color. The 36 skin sample images acquired with a UV camera were divided into 'Light', 'Medium', and 'Dark' according to the average pixel value of each image. A one-way analysis of variance (ANOVA) revealed a significant difference in the average pixel value between the three groups. The differences in average pixel value between the skin images immediately after applying sunscreen and 150 minutes after application was 3.91 in the 'Light', 3.27 in the 'Medium', and 3.65 in the 'Dark' group.
In clinical practice, peripheral blood smears are observed with the naked eye to confirm the number and morphology of White Blood Cells (WBC). The pathologist's proficiency has a great influence on the results because WBCs have diverse morphology. Many studies have been conducted on diagnostic assistance system for pathologists. However, these are still difficult to apply to images of various extracellular environments that change according to acquired conditions. Added to this is the limitation that only one WBC can be classified in one image. Therefore, in this study, we propose a robust segmentation algorithm for WBC nucleus and cytoplasm used color space, superpixel and watershed algorithm. Further, we propose a classification algorithm that classifies five types of normal WBCs using 16 morphological and texture features as well as the K-Nearest Neighbor (KNN) model. The accuracy of segmentation WBC nucleus and cytoplasm was 95.83% and 93.66%, respectively. The p-value for all the 16 features was significant (<0.001). In addition, through this study, research on a diagnostic assistance system that can classify more types of WBCs will be possible in the future.
The type and ratio of abnormal red blood cells (RBCs) in blood can be identified through peripheral blood smear test. Accurate classification is important because the accompanying diseases indicated by abnormal RBCs vary. In clinical practice, this task is time-consuming because the RBCs are manually classified. In addition, because the classification depends on the subjective criteria of pathologists, objective classification is difficult to achieve. In this paper, an automatic classification method that is solely based on images of RBCs captured under a microscope and processed using machine learning (ML) is proposed. The size and hemoglobin abnormalities of RBCs were classified by optimizing the criteria used in clinical practice. For morphologically abnormal RBCs classification, used seven geometric features information (major axis, minor axis, ratio of major and minor axis, perimeter, circularity, number of convex hulls, difference between area and convex area) and five types of multiple classifiers (Support Vector Machine, Decision Tree, K-Nearest Neighbor, Random Forest, and Adaboost models). Among was categorized using SVM, highly accurate results (99.9%) were obtained. The classification is performed simultaneously, and results are provided to the user through a graphical user interface (GUI).
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