Sajn Luka, Kukar Matjaz, Kononenko Igor, Milcinski Metka
University of Ljubljana, Faculty of Computer and Information Science, Trzaska 25, SI-1001 Ljubljana, Slovenia.
Comput Methods Programs Biomed. 2005 Oct;80(1):47-55. doi: 10.1016/j.cmpb.2005.06.001.
Bone scintigraphy or whole-body bone scan is one of the most common diagnostic procedures in nuclear medicine used in the last 25 years. Pathological conditions, technically poor image resolution and artefacts necessitate that algorithms use sufficient background knowledge of anatomy and spatial relations of bones in order to work satisfactorily. A robust knowledge based methodology for detecting reference points of the main skeletal regions that is simultaneously applied on anterior and posterior whole-body bone scintigrams is presented. Expert knowledge is represented as a set of parameterized rules which are used to support standard image-processing algorithms. Our study includes 467 consecutive, non-selected scintigrams, which is, to our knowledge the largest number of images ever used in such studies. Automatic analysis of whole-body bone scans using our segmentation algorithm gives more accurate and reliable results than previous studies. Obtained reference points are used for automatic segmentation of the skeleton, which is applied to automatic (machine learning) or manual (expert physicians) diagnostics. Preliminary experiments show that an expert system based on machine learning closely mimics the results of expert physicians.
骨闪烁显像或全身骨扫描是过去25年中核医学最常用的诊断程序之一。病理状况、技术上较差的图像分辨率和伪影使得算法需要具备足够的骨骼解剖学和空间关系背景知识才能令人满意地工作。本文提出了一种基于知识的稳健方法，用于检测主要骨骼区域的参考点，该方法同时应用于前后位全身骨闪烁显像。专家知识以一组参数化规则表示，用于支持标准图像处理算法。我们的研究包括467例连续的、未筛选的闪烁显像，据我们所知，这是此类研究中使用图像数量最多的。使用我们的分割算法对全身骨扫描进行自动分析，比以往研究能给出更准确可靠的结果。获得的参考点用于骨骼的自动分割，应用于自动（机器学习）或手动（专家医师）诊断。初步实验表明，基于机器学习的专家系统与专家医师的结果非常相似。
