Extraction of features from cross correlation in space and frequency domains for classification of skin lesions
Extraction of features from cross correlation in space and frequency domains for classification of skin lesions
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DOI：
10.1016/j.bspc.2019.101581
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发表时间：
2019-08-01
影响因子：
5.1
通讯作者：
Gorai, Surajit
中科院分区：
文献类型：
--
作者：
Chatterjee, Saptarshi;Dey, Debangshu;Gorai, Surajit
本文提出了一种系统的方法，从皮肤镜图像的黑色素细胞和表皮病变类别的良性和恶性皮肤疾病的特征提取和随后的分类。为此，黑色素瘤和痣被认为是黑色素细胞皮肤病变类别的代表，而基底细胞癌（BCC）和脂溢性角化病（SK）被纳入表皮病变类别。本工作阐述了提取的空间和光谱特征的皮肤病变的显眼区域的基础上，类似的视觉影响与适当的内核补丁，使用互相关技术。根据皮肤镜的功能，内核补丁已被选择从一组皮肤镜图像，包括所有的皮肤疾病类别选择这项工作。已经引入了多标签集成多类皮肤病变分类策略，用于恶性和良性黑色素细胞和表皮皮肤病变的分离，沿着它们的子类分类。对黑色素瘤、痣、基底细胞癌和SK等疾病的良恶性鉴别敏感性分别为98.76%、99.01%、98.87%和99.41%。(C)2019爱思唯尔有限公司版权所有。
This paper proposes a systematic approach for the feature extraction and subsequent classification of benign and malignant skin diseases of both the melanocytic and epidermal lesion categories from dermoscopic images. For this purpose, melanoma and nevus are considered as representatives of the melanocytic skin lesion category, whereas basal cell carcinoma (BCC) and seborrheic keratoses (SKs) are included under the epidermal lesion category. The present work explicates the extraction of spatial and the spectral features from conspicuous regions of skin lesions on the basis of similar visual impacts with the appropriate kernel patches, using the Cross-correlation technique. Depending on the dermoscopic features, the kernel patches have been chosen from a set of dermoscopic images comprising all the skin disease categories selected for this work. A multi-label ensemble multiclass skin lesion classification strategy has been introduced for the segregation of malignant and benign melanocytic and epidermal skin lesions, along with their subclass classification. It has been possible to identify both malignant and benign lesions of melanoma, nevus, BCC and SK disease classes, with sensitivities of 98.76%, 99.01%, 98.87% and 99.41% respectively. (C) 2019 Elsevier Ltd. All rights reserved.