Koprowski Robert
Department of Biomedical Computer Systems, University of Silesia, Faculty of Computer Science and Materials Science, Institute of Computer Science, ul, Będzińska 39, Sosnowiec 41-200, Poland.
Biomed Eng Online. 2014 Jul 4;13:93. doi: 10.1186/1475-925X-13-93.
Dedicated, automatic algorithms for image analysis and processing are becoming more and more common in medical diagnosis. When creating dedicated algorithms, many factors must be taken into consideration. They are associated with selecting the appropriate algorithm parameters and taking into account the impact of data acquisition on the results obtained. An important feature of algorithms is the possibility of their use in other medical units by other operators. This problem, namely operator's (acquisition) impact on the results obtained from image analysis and processing, has been shown on a few examples.
The analysed images were obtained from a variety of medical devices such as thermal imaging, tomography devices and those working in visible light. The objects of imaging were cellular elements, the anterior segment and fundus of the eye, postural defects and others. In total, almost 200'000 images coming from 8 different medical units were analysed. All image analysis algorithms were implemented in C and Matlab.
For various algorithms and methods of medical imaging, the impact of image acquisition on the results obtained is different. There are different levels of algorithm sensitivity to changes in the parameters, for example: (1) for microscope settings and the brightness assessment of cellular elements there is a difference of 8%; (2) for the thyroid ultrasound images there is a difference in marking the thyroid lobe area which results in a brightness assessment difference of 2%. The method of image acquisition in image analysis and processing also affects: (3) the accuracy of determining the temperature in the characteristic areas on the patient's back for the thermal method - error of 31%; (4) the accuracy of finding characteristic points in photogrammetric images when evaluating postural defects - error of 11%; (5) the accuracy of performing ablative and non-ablative treatments in cosmetology - error of 18% for the nose, 10% for the cheeks, and 7% for the forehead. Similarly, when: (7) measuring the anterior eye chamber - there is an error of 20%; (8) measuring the tooth enamel thickness - error of 15%; (9) evaluating the mechanical properties of the cornea during pressure measurement - error of 47%.
The paper presents vital, selected issues occurring when assessing the accuracy of designed automatic algorithms for image analysis and processing in bioengineering. The impact of acquisition of images on the problems arising in their analysis has been shown on selected examples. It has also been indicated to which elements of image analysis and processing special attention should be paid in their design.
在医学诊断中，用于图像分析和处理的专用自动算法正变得越来越普遍。在创建专用算法时，必须考虑许多因素。这些因素与选择合适的算法参数以及考虑数据采集对所得结果的影响有关。算法的一个重要特性是其他操作人员可在其他医疗单位使用它们。关于操作人员（采集）对图像分析和处理所得结果的影响这一问题，已通过一些实例得以展现。
所分析的图像来自多种医疗设备，如热成像设备、断层扫描设备以及可见光下工作的设备。成像对象包括细胞成分、眼睛的前段和眼底、姿势缺陷等。总共分析了来自8个不同医疗单位的近200,000张图像。所有图像分析算法均用C语言和Matlab实现。
对于各种医学成像算法和方法，图像采集对所得结果的影响各不相同。算法对参数变化的敏感度存在不同水平，例如：（1）对于显微镜设置和细胞成分的亮度评估，差异为8%；（2）对于甲状腺超声图像，在标记甲状腺叶面积方面存在差异，这导致亮度评估差异为2%。图像分析和处理中的图像采集方法还会影响：（3）热成像法中确定患者背部特征区域温度的准确性——误差为31%；（4）评估姿势缺陷时在摄影测量图像中寻找特征点的准确性——误差为11%；（5）美容学中进行消融和非消融治疗的准确性——鼻子部位误差为18%，脸颊部位误差为10%，额头部位误差为7%。同样，当：（7）测量眼前房时——误差为20%；（8）测量牙釉质厚度时——误差为15%；（9）在压力测量期间评估角膜的力学性能时——误差为47%。
本文呈现了在评估生物工程中设计的图像分析和处理自动算法准确性时出现的重要且经过挑选的问题。通过选定实例展示了图像采集对其分析中出现问题的影响。还指出了在图像分析和处理设计中应特别关注的要素。
