idneo开发了嵌入式计算机视觉和机器学习算法，用于解释血型结果

The red bands on the MDmulticard are sometimes misshapen or faded due to humidity, temperature, the patient’s transfusion history, the manual process used to dilute the blood sample, or other factors. As a result, the IDNEO team needed to develop algorithms capable of handling significant variations in band patterns and shapes. The team only had access to a limited number of cards at the start of the project. They needed a workflow that supported rapid iterations so that they could easily refine their algorithms as they received more cards with different band patterns and shapes.

Grifols和Idneo希望尽快提供原型，以使临床人员能够在将算法部署到生产硬件之前提供有关软件的反馈。由于团队正在制定短时间线工作，因此他们想采用一种敏捷的开发方法，使他们能够纳入客户的投入并迅速对转移要求做出反应。

IDNEO工程师开发的图像处理,第一版er vision, and machine learning algorithms in MATLAB and then generated code for the production Android implementation of the MDmulticard reader with Embedded Coder^®.

使用MATLAB和Image Processing Toolbox™开发的核心图像分析算法，执行颜色均衡和白色平衡，将图像转换为Cieluv色彩空间，计算颜色差异，然后在卡片上定位图像中的带状图像。Idneo团队在核心算法中添加了频段分析，创建了图像的二进制版本，然后应用形态操作以获取卡上每个频段的骨架图像。

接下来，他们实施了一个线性回归分类器，该分类器训练了从骨架图像中提取的功能。分类器检测出固体带（分类为正），不存在带（分类为负）和混合场带（归类为可疑），这可能会发生在患者先前的输血。

在Grifols提供的图像上测试算法后，工程师设计了使用MATLAB应用程序设计器的用户界面。他们使用Matlab Compiler™制作了独立的MATLAB应用程序，该应用程序Grifols工程师和选定的医院工作人员可以在不安装MATLAB的情况下使用。

The IDNEO team generated production C code from the core image analysis algorithms with Embedded Coder. They tested the C code by comparing the results it produced with the results produced by the original MATLAB algorithms, using MATLAB Profiler to measure code coverage.

The team integrated the generated code into an Android app that provides a touch-screen interface to the Grifols MDmulticard reader.

To comply with the customer’s tight schedule, the IDNEO team used the Scrum process framework and continuous integration throughout development. MATLAB supported this workflow, with Jenkins jobs testing the code generated with Embedded Coder against a database of card images.

A fully validated, preproduction prototype of the card reader is undergoing usability testing at various hospitals in Spain. Meanwhile, IDNEO engineers continue to improve the accuracy of their algorithms, using the Classification Learner app in Statistics and Machine Learning Toolbox™ to evaluate support vector machines and other machine learning models.

• 准确性要求超出.“Our customer set a requirement of greater than 90% accuracy in identifying positive and negative bands,” says Blanch. “The algorithm we developed in MATLAB produced zero false positives or false negatives in the sample data set, so we exceeded that requirement.”
• 项目完成时间减半。布兰奇说：“ MATLAB和嵌入式编码器使我们能够减少完成项目所需的时间从24个月到12个月，而无需为团队增加更多的工程师。”“这种方法使团队成员能够专注于他们的特定角色，使我们更加高效，并减少软件错误的数量。”
• Optimized system delivered.“Using Embedded Coder to generate C code for the device enabled us to concentrate fully on developing and optimizing our algorithms,” says Blanch. “As a result, we were able to deliver a higher quality system in the time that we had.”