# Image Processing and Analysis for Biotechnology and Bioprocess Engineering
###### Abstract  
The development of high-performance computing hardware and image processing algorithms has led to the widespread application of image analysis in various fields [1; 2]. In particular, image analysis is frequently applied in the fields of biotechnology and bioprocess engineering, where various imaging devices are used for research and development [3]. Compared to manual methods performed by humans, automated image analysis allows for fast, accurate, and reliable quantitative analysis. The classical approach to image analysis involves the sequential application of multiple image processing algorithms at the pixel level. The color value of a specific part of the final image is then analyzed, or an object is detected, and morphology such as coordinates, area, shape, and number is analyzed [4; 5]. One of the important applications of image analysis in the field of biotechnology is probably high-throughput research that requires analyzing large amounts of images and videos, which is difficult for humans to perform [6]. For example, while exposing zebrafish to a large number of different chemicals, videos are recorded and analyzed to quantify the animal's growth, movement, organ morphology, social behavior, or feeding habits. Interpreting these results can reveal the function of the compound molecule and the efficacy and toxicity of the chemical or drug [7; 8; 9]. Recent advancements in machine learning techniques have made it possible to better utilize existing image analysis results. Rather than just using the area or color values of objects as endpoints, these image analysis results can now be used to classify objects according to their characteristics and predict important phenomena. For example, when obtaining multiple image analysis endpoints through image analysis, interpretation can become challenging. In such cases, applying dimensionality reduction techniques such as PCA (Principal Component Analysis), LDA (Linear Discriminant Analysis), T-SNE (t-distributed Stochastic Neighbor Embedding), and UMAP (Uniform Manifold Approximation and Projection) can significantly reduce the number of variables to be analyzed. This makes classification or prediction more feasible and enables visualization on a two-dimensional chart for easier human comprehension [10; 11]. Moreover, tools like artificial neural networks (ANN), decision trees, and SVM (Support Vector Machine) have been employed for classification using multiple image analysis parameters [12; 13; 14]. Above all, the development of various deep learning architectures and training techniques suitable for image processing, along with the emergence of high-performance GPUs (Graphics Processing Units) capable of handling them, has led to the application of more sophisticated image processing technologies. Convolutional Neural Networks (CNN) and Transformers have shown excellent performance in object detection and classification. For instance, using CNN, it was revealed that female flies (_Drosophila_) were more attracted to sucrose during egg-laying [15], and CNN was employed for the detection, segmentation, and analysis of plants or plant stress [16]. This analysis could not be carried out with pixel-based classical image processing. Meanwhile, ordinary biologists familiar with wet lab experiments often lack computer knowledge for conducting image analysis. Therefore, there is a need for the developmentof user-friendly image analysis software. Numerous GUI (Graphical User Interface) applications utilizing image processing tools such as ImageJ [17], MATLAB (The MathWorks, Inc., Natick, MA, USA), openCV [18], PlantCV [19], and scikit-image [20] have been developed to cater to their needs. As documented in review papers, various software has been developed and used to analyze the locomotion of animals like zebrafish (_Danio rerio_) and _C. elegans_, as well as mice [21; 22]. In addition, there is a variety of software that can detect plants, classify phenotypes [23; 24], and classify cell types [25].