# The Active Segmentation Platform for Microscopic Image Classification and Segmentation
## 1 Introduction
Texture features have been used to classify images since 1970s [12]. Recent applications include [3; 13]. In a similar way, image classification can be classified into 3 main categories [14]:  
* numerical feature engineering method (NFE);
* Artificial Neural Networks (ANNs);
* Transport based morphometry (TBM);  
The use of NFE is the traditional way to classify images. Based on expert knowledge or automatic algorithms, several morphological and polynomial features are extracted an image. Once the features are computed, classification algorithms, such as Support Vector Machines (SVM), or random forests, are then applied to the feature vectors. The main advantage of using NFE over neural networks are that the former algorithms are fast and do not need supervision. On the other hand, ANNs bypass the step of feature extraction and use several fully connected convolution layers to learn features from the source images. There are numerous open-source programs for biomedical image processing. This can be attributed in part to the fact that in the past every imaging facility developedits own analysis tools. The situation changed gradually after 2005 with the gaining of popularity of ImageJ and its offshoots Fiji and ImageJ2 [15], so at present the ImageJ tool ecosystem is a _de facto_ standard for biological applications [16; 17]. What made ImageJ ecosystem unique is the fact that the original ImageJ design was minimalistic, focusing only on the essential features, which could nevertheless be extended in almost any direction by contributed plugins that seamlessly integrate into the main user interface. So that without emphasis on programming paradigms and particular frameworks the entry point for new users and developers remained low. Further development of the ImageJ family included Fiji [18], where one of the authors ([PERSON]) was an early contributor, as well as CellProfiler [10], Icy [9], and ImageJ2 [15]. All these platforms have varying entry thresholds for new users and developers. Therefore, to truly make an impact, a new solution should be at least interoperable with some of these platforms or preferably be implemented in one of them. The aim of the present article is to demonstrate a useful addition to the ImageJ tool ecosystem. The Active Segmentation for ImageJ (AS/IJ) has been developed since 2016 under the auspices of the Google Summer of Code Program as an activity of the Belgian Neuroinformatics Node, affiliated to the International Neuroinformatics Coordination Facility (INCF). AS/IJ is not a mere incremental improvement of TWS, which served as an inspiration and starting point of the project. The platform architecture exploits ImageJ's modularity of design and even goes one step further: filters, compatible with the platform, are themselves plugins of AS/IJ. They could even be independent ImageJ plugins, although this is not necessary. AS/IJ supports rich and extensible metadata. The metadata structure ensures reproducibility of the classification and segmentation results across sessions. This design choice is made specifically to ensure support of the Findability, Accessibility, Interoperability, and Reusability (FAIR) data management principles. In particular, we implement the R-part of the principle in the way that all filtering settings are stored in the project file in JavaScript Object Notation (JSON) format. Furthermore, the file format is transparent for both human eyes and algorithms as JSON is a subset of the standard web language JavaScript. The present paper applies the AS/IJ platform on two related problems: automatic classification and segmentation of microscopic images. We have employed generic approaches applicable to a wide variety of microscopic images by leveraging the power of differential geometry combined with scale space theory. The paper is organized as follows. Section 2 presents the minimal necessary background on differential geometry, scale space theory, image moments, and machine learning. Section 3 describes the user interface and data format.