Evolutionary Computing Application to Biomedical Image Segmentation

我们的目标是开发一个进化计算平台的原型， 输入某种类型的图像的随机样本，沿着它们相应的地面实况。它将 输出计算机程序，该计算机程序能够以高的程度分割相同类型的不可见图像， 分割精度和计算效率。这个平台本身可以被描述为一个基因 一种程序设计方法，其使对应于候选者的节点的前馈网络的群体进化 图像分割程序。这种特殊的表现形式受到了笛卡尔遗传学的启发 编程（CGP）。每个节点由其标识号（ID）、图像变换 变换参数和提供其输入的节点的ID。这种转变来自于 开放的转换库，其中包括众所周知的图像预处理，分割和 后处理方法。个人的适应性将从基于像素的和基于 区域视角。整体进化循环搜索所有（小型）前馈空间 网络，不同的结构和参数化，对于那些具有最高相对适应值的网络。 我们工作的最终价值在于它试图模仿和超越高薪工作者的方式。 专业人员开发用于图像分割的分布式程序。因此，我们认为我们的努力 只有当一个功能齐全的和可演示的原型生产成功。

Our objective is the development of a prototype of an evolutionary computing platform, which accepts as input a random sample of images of a certain type, along with their corresponding ground truths. It would output a computer program that is capable of segmenting unseen images of the same type, with a high degree of segmentation accuracy and computational efficiency. The platform itself could be described as a genetic programming method that evolves a population of feed-forward networks of nodes, corresponding to candidate image segmentation programs. The particular form of representation is inspired by Cartesian Genetic Programming (CGP). Each node is defined by its identification number (ID), image transformation, transformation parameters and the IDs of the nodes that provide its input(s). The transformation comes from an open library of transformations, which include well-known image pre-processing, segmentation and post-processing methods. The fitness of an individual will be measured from both a pixel-based and a region-based perspective. The overall evolutionary cycle searches the space of all (small-sized) feed-forward networks, of different structures and parameterizations, for those networks with highest relative fitness values. The ultimate value of our work lies in its attempt to emulate and exceed the manner in which highly-paid professionals develop distributed programs for image segmentation. Hence, we would consider our effort a success only whence a fully-functional and demonstrable prototype is produced.