CVPR Unit, Indian Statistical Institute, Kolkata 700108, India.
Human Genetics Unit, Indian Statistical Unit, Kolkata, West Bengal 700108, India.
Comput Methods Programs Biomed. 2018 Jun;159:59-69. doi: 10.1016/j.cmpb.2018.01.027. Epub 2018 Feb 6.
Development of machine assisted tools for automatic analysis of psoriasis skin biopsy image plays an important role in clinical assistance. Development of automatic approach for accurate segmentation of psoriasis skin biopsy image is the initial prerequisite for developing such system. However, the complex cellular structure, presence of imaging artifacts, uneven staining variation make the task challenging. This paper presents a pioneering attempt for automatic segmentation of psoriasis skin biopsy images.
Several deep neural architectures are tried for segmenting psoriasis skin biopsy images. Deep models are used for classifying the super-pixels generated by Simple Linear Iterative Clustering (SLIC) and the segmentation performance of these architectures is compared with the traditional hand-crafted feature based classifiers built on popularly used classifiers like K-Nearest Neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). A U-shaped Fully Convolutional Neural Network (FCN) is also used in an end to end learning fashion where input is the original color image and the output is the segmentation class map for the skin layers.
An annotated real psoriasis skin biopsy image data set of ninety (90) images is developed and used for this research. The segmentation performance is evaluated with two metrics namely, Jaccard's Coefficient (JC) and the Ratio of Correct Pixel Classification (RCPC) accuracy. The experimental results show that the CNN based approaches outperform the traditional hand-crafted feature based classification approaches.
The present research shows that practical system can be developed for machine assisted analysis of psoriasis disease.
开发用于自动分析银屑病皮肤活检图像的机器辅助工具对于临床辅助具有重要意义。开发用于准确分割银屑病皮肤活检图像的自动方法是开发此类系统的初始前提。然而，复杂的细胞结构、成像伪影的存在、不均匀的染色变化使得这项任务具有挑战性。本文首次尝试自动分割银屑病皮肤活检图像。
尝试了几种深度学习架构来分割银屑病皮肤活检图像。深度模型用于对简单线性迭代聚类 (SLIC) 生成的超像素进行分类，并将这些架构的分割性能与基于流行分类器（如 K-最近邻 (KNN)、支持向量机 (SVM) 和随机森林 (RF)）构建的传统手工特征分类器进行比较。还使用了端到端学习方式的 U 形全卷积神经网络 (FCN)，其中输入是原始彩色图像，输出是皮肤层的分割类别图。
开发并使用了一个具有九十 (90) 张图像的标注真实银屑病皮肤活检图像数据集进行这项研究。使用两个度量标准，即 Jaccard 系数 (JC) 和正确像素分类率 (RCPC) 准确性来评估分割性能。实验结果表明，基于 CNN 的方法优于传统的手工特征分类方法。
本研究表明，可以开发实用的系统来辅助分析银屑病疾病。
