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Automatic Kaposi's sarcoma detection using texture distinctiveness

机译:自动Kaposi的SARCOMA检测使用质地独特性

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There is a growing emphasis on skin cancer diagnosis and Kaposi's sarcoma has recently received increasing attention. Kaposi's sarcoma is one form of skin cancer. The time and costs required for medical experts to screen all patients for Kaposi's sarcoma are prohibitively expensive. Dermatologists need an automatic diagnosis system to assess a patient's risk of Kaposi's sarcoma without using special or costly equipment. One challenge in implementing such a system is locating the skin lesion. We propose Texture Distinctiveness Lesion Segmentation Algorithm (TDS-KS) to automatically locate skin lesions from the photograph. TDS-KS algorithm consists of two main steps. First a set of representative texture distributions are learned from the input skin lesion image and texture distinctiveness metric is calculated for each distribution. Then a texture-based segmentation algorithm classifies regions the input image as normal skin or lesion based on the occurrence of representative texture distributions. The input images are taken from dermquest database which has images of different skin diseases.
机译:越来越强调皮肤癌诊断,Kaposi的肉瘤最近受到了越来越多的关注。 Kaposi的肉瘤是一种皮肤癌的一种形式。医学专家为筛选所有患者的Kaposi的肉瘤所需的时间和成本都非常昂贵。皮肤科医生需要自动诊断系统,以评估患者Kaposi的Sarcoma的风险,而无需使用特殊或昂贵的设备。实施这种系统的一个挑战是定位皮肤病变。我们提出纹理明显的病变分割算法(TDS-KS),以自动定位来自照片的皮肤病变。 TDS-KS算法由两个主要步骤组成。首先,从输入的皮肤病变图像中学习一组代表性纹理分布,并且针对每个分布计算纹理独特性度量。然后,基于纹理的分割算法将输入图像分类为正常的皮肤或病变基于代表纹理分布的发生。输入图像是从DermQuest数据库中的,该数据库具有不同皮肤病的图像。

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