基于皮肤镜图像的黑色素瘤形态模式识别研究
发布时间：2018-08-17 12:27
【摘要】：黑色素瘤是在临床中经常遇到的一种恶性皮肤肿瘤,同时也是世界上增长最快的癌症之一。通常皮肤科医生通过肉眼观察和组织病理学活检的方法来对黑色素瘤进行早期的筛查和确诊。盲目进行活检常常会对患者造成经济上的压力和身体上不必要的创伤。因此,非创伤性的黑色素瘤自动诊断技术成为了医学界急需解决的问题。皮肤镜图像形态模式识别一直是分辨良性肿瘤和恶性素瘤的一项具有挑战性的任务。因此针对黑色素瘤皮肤镜图像的多种形态模式,本文对基于多标签学习的恶性黑色素瘤形态模式识别进行了深入的研究。创新点主要包括以下部分:1.深入研究了黑色素瘤的分割与特征提取方法。为了更好的提取皮肤镜图像的特征,提出一种基于区域一致性的融合算法来对图像进行分割,将多个分割算法结果进行融合,依据区域大小、灰度值、纹理的一致性原则移除与融合结果相矛盾的子区域,从而得到最终的分割结果。对分割结果分别提取皮损内区域、皮损区域和皮损外区域的颜色特征、形状特征和纹理特征。2.提出了基于手工特征提取的多标签分类在黑色素瘤模式识别中的应用。对黑色素瘤的形态模式种类进行了深入研究分析,可明确定义的全局形态特征主要有八种,其中涉及七种基本模式和一个多成分模式。这七种基本模式包括:网状模式、球状模式、鹅卵石模式、星爆模式、平行模式、腔洞模式、均匀模式。通过黑色素瘤的七种基本模式来建立多标签分类模型,以达到自动识别皮肤镜图像中所包含的模式类别的目的。使用Binary Reference算法和ML-kNN算法对黑色素瘤特征向量进行多标签分类,对两种算法的多标签分类结果对比分析发现ML-kNN算法对黑色素瘤的多种形态模式的识别相比于Binary Reference算法具有更好的效果。3.提出了基于特征学习的卷积神经网络多标签分类在黑色素瘤模式识别中的应用。在深度学习框架的基础上提出了一种改进的方法来实现多标签分类,将图像数据和多标签数据分别作为网络的输入层,然后通过在网络结构添加Slice层达到多标签分类的目的,最终得到多标签分类模型。在利用卷积神经网络对黑色素瘤的形态模式进行特征自动学习的实验中,实验结果表明本文提出的利用卷积神经网络进行多标签分类效果比基于手工特征的多标签分类具有显著的提升。
[Abstract]:Melanoma is a malignant skin tumor often encountered in clinical practice, and it is also one of the fastest growing cancers in the world. Dermatologists usually screen and diagnose melanoma early through naked eye observation and histopathological biopsy. Blind biopsies often cause financial stress and unnecessary physical trauma to patients. Therefore, non-traumatic automatic diagnosis of melanoma has become an urgent problem in medical field. Pattern recognition of dermatoscopic images has been a challenging task in distinguishing benign and malignant tumors. Therefore, for the multiple morphologic patterns of melanoma dermoscope images, this paper studies the morphological pattern recognition of malignant melanoma based on multi-label learning. Innovations include the following parts: 1. The segmentation and feature extraction of melanoma were studied. In order to extract the features of skin mirror image better, a fusion algorithm based on region consistency is proposed to segment the image. The results of multiple segmentation algorithms are fused according to the region size and gray value. The consistency principle of texture removes the subregions which are contradictory to the fusion results, and the final segmentation results are obtained. The color features, shape features and texture features of the inner region, the lesion region and the outer area of the skin lesions were extracted from the segmentation results. The application of multi-label classification based on manual feature extraction in melanoma pattern recognition is proposed. The morphological patterns of melanoma were studied and analyzed. There are eight kinds of global morphological features which can be clearly defined, including seven basic patterns and one multi-component pattern. The seven basic models include reticular model, spherical model, cobblestone model, starburst model, parallel mode, cavity model, and uniform mode. Through seven basic patterns of melanoma, a multi-label classification model is established to automatically identify the pattern categories contained in the dermatoscopic image. Binary Reference algorithm and ML-kNN algorithm are used to classify melanoma feature vectors. The comparison and analysis of the multi-label classification results of the two algorithms show that the ML-kNN algorithm has a better effect than the Binary Reference algorithm in the recognition of multiple morphologic patterns of melanoma. This paper presents the application of convolution neural network multi-label classification based on feature learning in melanoma pattern recognition. Based on the deep learning framework, an improved method is proposed to realize multi-label classification. Image data and multi-label data are used as the input layer of the network, and then the multi-label classification is achieved by adding the Slice layer to the network structure. Finally, multi-label classification model is obtained. In the experiment of using convolutional neural network to study the morphologic pattern of melanoma, The experimental results show that the effectiveness of multi-label classification based on convolution neural network is significantly improved than that of multi-label classification based on manual features.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R739.5;TP391.41
本文编号：2187624
[Abstract]:Melanoma is a malignant skin tumor often encountered in clinical practice, and it is also one of the fastest growing cancers in the world. Dermatologists usually screen and diagnose melanoma early through naked eye observation and histopathological biopsy. Blind biopsies often cause financial stress and unnecessary physical trauma to patients. Therefore, non-traumatic automatic diagnosis of melanoma has become an urgent problem in medical field. Pattern recognition of dermatoscopic images has been a challenging task in distinguishing benign and malignant tumors. Therefore, for the multiple morphologic patterns of melanoma dermoscope images, this paper studies the morphological pattern recognition of malignant melanoma based on multi-label learning. Innovations include the following parts: 1. The segmentation and feature extraction of melanoma were studied. In order to extract the features of skin mirror image better, a fusion algorithm based on region consistency is proposed to segment the image. The results of multiple segmentation algorithms are fused according to the region size and gray value. The consistency principle of texture removes the subregions which are contradictory to the fusion results, and the final segmentation results are obtained. The color features, shape features and texture features of the inner region, the lesion region and the outer area of the skin lesions were extracted from the segmentation results. The application of multi-label classification based on manual feature extraction in melanoma pattern recognition is proposed. The morphological patterns of melanoma were studied and analyzed. There are eight kinds of global morphological features which can be clearly defined, including seven basic patterns and one multi-component pattern. The seven basic models include reticular model, spherical model, cobblestone model, starburst model, parallel mode, cavity model, and uniform mode. Through seven basic patterns of melanoma, a multi-label classification model is established to automatically identify the pattern categories contained in the dermatoscopic image. Binary Reference algorithm and ML-kNN algorithm are used to classify melanoma feature vectors. The comparison and analysis of the multi-label classification results of the two algorithms show that the ML-kNN algorithm has a better effect than the Binary Reference algorithm in the recognition of multiple morphologic patterns of melanoma. This paper presents the application of convolution neural network multi-label classification based on feature learning in melanoma pattern recognition. Based on the deep learning framework, an improved method is proposed to realize multi-label classification. Image data and multi-label data are used as the input layer of the network, and then the multi-label classification is achieved by adding the Slice layer to the network structure. Finally, multi-label classification model is obtained. In the experiment of using convolutional neural network to study the morphologic pattern of melanoma, The experimental results show that the effectiveness of multi-label classification based on convolution neural network is significantly improved than that of multi-label classification based on manual features.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：R739.5;TP391.41
【参考文献】
相关期刊论文 前2条
1 孟如松;孟晓;姜志国;谢凤英;刘玮;罗卫;郭广进;蔡瑞康;;基于国人皮肤镜黑素细胞肿瘤图像的智能化分类与识别研究[J];中国体视学与图像分析;2012年03期
2 孟如松;赵广;;皮肤镜图像分析技术的基础与临床应用[J];临床皮肤科杂志;2008年04期
，本文编号：2187624
本文链接：https://www.wllwen.com/yixuelunwen/zlx/2187624.html
