Iakovidis D K, Goudas T, Smailis C, Maglogiannis I
Department of Informatics and Computer Technology, Technological Educational Institute of Lamia, 35100 Lamia, Greece.
Department of Digital Systems, University of Piraeus, 18534 Piraeus, Greece.
ScientificWorldJournal. 2014 Jan 27;2014:286856. doi: 10.1155/2014/286856. eCollection 2014.
Image segmentation and annotation are key components of image-based medical computer-aided diagnosis (CAD) systems. In this paper we present Ratsnake, a publicly available generic image annotation tool providing annotation efficiency, semantic awareness, versatility, and extensibility, features that can be exploited to transform it into an effective CAD system. In order to demonstrate this unique capability, we present its novel application for the evaluation and quantification of salient objects and structures of interest in kidney biopsy images. Accurate annotation identifying and quantifying such structures in microscopy images can provide an estimation of pathogenesis in obstructive nephropathy, which is a rather common disease with severe implication in children and infants. However a tool for detecting and quantifying the disease is not yet available. A machine learning-based approach, which utilizes prior domain knowledge and textural image features, is considered for the generation of an image force field customizing the presented tool for automatic evaluation of kidney biopsy images. The experimental evaluation of the proposed application of Ratsnake demonstrates its efficiency and effectiveness and promises its wide applicability across a variety of medical imaging domains.
图像分割和标注是基于图像的医学计算机辅助诊断（CAD）系统的关键组成部分。在本文中，我们介绍了Ratsnake，这是一个公开可用的通用图像标注工具，具有标注效率、语义感知、通用性和可扩展性等特点，这些特性可用于将其转变为一个有效的CAD系统。为了展示这一独特能力，我们介绍了它在评估和量化肾活检图像中显著感兴趣的物体和结构方面的新颖应用。在显微镜图像中准确标注识别和量化此类结构，可以为梗阻性肾病的发病机制提供估计，梗阻性肾病是一种在儿童和婴儿中相当常见且具有严重影响的疾病。然而，目前还没有用于检测和量化该疾病的工具。一种基于机器学习的方法，利用先验领域知识和纹理图像特征，被考虑用于生成图像力场，从而定制所提出的工具以自动评估肾活检图像。对Ratsnake所提出应用的实验评估证明了其效率和有效性，并预示了其在各种医学成像领域的广泛适用性。
