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Corroboration of Skin Diseases: Measuring the Severity of Vitiligo Using Transfer Learning
http://doi.org/10.5626/JOK.2023.50.1.72
Vitiligo is a commonly acquired skin disorder that results from the loss of melanin pigment from the epidermis and is clinically indicated by pale or white patches on the body. Preliminary treatment is essential for vitiligo, but vitiligo does not cause pain or health problems. Therefore, vitiligo patents are treated when skin lesions are visible on the outside. The subjective judgment treats vitiligo of dermatologist’s, and there is no quantitative and objective analysis method through imaging, because it is difficult to obtain a medical image. Several diagnostic methods have been developed through a few medical studies. In this paper, we propose a method for area of vitiligo through image segmentation using metastasis learning to overcome the limitations of vitiligo medical data collection. The transfer learning model was selected by experimenting with the possibility of application to deep learning models such as U-net, FCN, and Deeplab. In addition, the severity of Vitiligo was measured using the VASI score used in the medical field, converting the skin image into an RGB skin image representing skin areas. In the experimental results, when trained with an imbalanced vitiligo image dataset, the performance of Deeplab, measured by F1-score and IoU, was superior to that of U-net and the image processing method. Additionally, the method for calculating the VASI score in vitiligo image proposed in this paper showed the possibility of being used for vitiligo diagnosis.
VACS: Virtual Try-on Artifact Correction System using the Fashion Object Segmentation Method
Wonjung Park, Youjin Chung, Soonchan Park, Jinah Park
http://doi.org/10.5626/JOK.2022.49.10.802
Virtual try-on (VITON) technology is receiving a lot of attention with the development of Generative Adversarial Networks (GANs) [1]. Previous approaches to VITON synthesized 2D model images and in-shop clothing images using a generative model. However, when synthesizing the top, VITON erroneously changes pixels in unintended areas, such as the background and pants. In this study, we propose the VITON Artifact Correction System (VACS), which divides and protects targeted clothes synthesized in VITON by fashion object segmentation, and replaces the pixels corresponding to the remaining areas with the original model image to increase the realism of the final composition.
Color Scheme Extraction Based on Image Segmentation and Saliency Map
http://doi.org/10.5626/JOK.2021.48.3.302
Color is the primary visual element that has the greatest amount of influence on the recognition of images. A color scheme is arranged through selection of a certain number of colors. Color schemes are used in various visual fields, such as fashion, cosmetics, interior design, media, and the arts. In this study, we introduce a method of automatically extracting color schemes from images. To overcome the shortcomings of previous methods, our method extracts a color scheme based on image segmentation and saliency map extraction. It also has the advantage of scalability, as it extracts schemes depending on their relative levels of importance in the image.
End-to-end Bone Tumor Segmentation and Classification from X-ray Images by Using Multi-level Seg-Unet Model
Nhu-Tai Do, Sung-Taek Jung, Hyung-Jeong Yang, Soo-Hyung Kim
http://doi.org/10.5626/JOK.2020.47.2.170
Knee bone tumor detection plays an essential role in assisting the clinical diagnosis process. To the best of our knowledge, there is no method to integrate end-to-end segmentation and classification for this problem. In this paper, we propose a multi-task deep learning architecture for classification and segmentation of the tumor regions in the knee bone. Also, we introduce multi-level distance masks from the distance transform of tumor region, and these multi-level distance masks have a role as a guided filter in enabling the network to capture semantic data around tumor regions. Besides, the architecture has a regularizing effect on the learning process between segmentation and classification. Our model was evaluated on the Chonnam National University Hospital dataset and achieved good performance compared to other methods.
Topological Analysis of the Feasibility and Initial-value Assignment of Image Segmentation
This paper introduces and analyzes the theoretical basis and method of the conventional initial-value assignment problem and feasibility of image segmentation. The paper presents topological evidence and a method of appropriate initial-value assignment based on topology theory. Subsequently, the paper shows minimum conditions for feasibility of image segmentation based on separation axiom theory of topology and a validation method of effectiveness for image modeling. As a summary, this paper shows image segmentation with its mathematical validity based on topological analysis rather than statistical analysis. Finally, the paper applies the theory and methods to conventional Gaussian random field model and examines effectiveness of GRF modeling.
Anterior Cruciate Ligament Segmentation in Knee MRI with Locally-aligned Probabilistic Atlas and Iterative Graph Cuts
Segmentation of the anterior cruciate ligament (ACL) in knee MRI remains a challenging task due to its inhomogeneous signal intensity and low contrast with surrounding soft tissues. In this paper, we propose a multi-atlas-based segmentation of the ACL in knee MRI with locally-aligned probabilistic atlas (PA) in an iterative graph cuts framework. First, a novel PA generation method is proposed with global and local multi-atlas alignment by means of rigid registration. Second, with the generated PA, segmentation of the ACL is performed by maximum-aposteriori (MAP) estimation and then by graph cuts. Third, refinement of ACL segmentation is performed by improving shape prior through mask-based PA generation and iterative graph cuts. Experiments were performed with a Dice similarity coefficients of 75.0%, an average surface distance of 1.7 pixels, and a root mean squared distance of 2.7 pixels, which increased accuracy by 12.8%, 22.7%, and 22.9%, respectively, from the graph cuts with patient-specific shape constraints.
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