Morphology is one of the important and basic tools for image processing. Its mathematical operation is in general defined in multi-dimensional space and therefore it can be applied to three-dimensional CT and MR images. In this article, morphological methods for segmentation, detection of lung cancers, analysis of pulmonary vascular tree structure, and shape-based interpolation are introduced. Computational load is another important factor in applying mathematical morphology. Fast algorithms are also introduced.

Morphology processing has been applied to medical image processing and analysis since around 1990 and has become an indispensable tool for various image processing pre-processing. In this paper, we will introduce some applications of morphology processing to medical image processing and analysis, mainly from the Medical Imaging Technology (MIT) journal and future developments in the field. This article will provide an opportunity for students and researchers currently starting work on medical image processing to learn about the significance and usefulness of morphology processing.

Perfusion-based lung blood flow analysis can be divided into two types: with and without a model. While the model-based approach provides physiologically accurate results, the conditions are strict and difficult to handle. On the other hand, the model-free approach is simple, but it is limited to single-input analysis where the impulse response representing the system's properties is solved from input-output relationships. In this study, a model-free method that combines simplicity and accuracy was proposed to enable analysis of multiple-input systems and aimed to standardize analysis. In the proposed method, the impulse response was formulated in a forward problem using a deep learning algorithm and directly estimated, enabling multiple-input analysis. The results of comparative experiments showed that while the proposed method is susceptible to noise, it is easy to implement and has high convergence in the range of actual SNR. However, for multiple-input analysis, since there is no model, the blood flow components interfere with each other, causing a decrease in accuracy.

This paper describes the history of the development of the software as a medical device (SaMD) for bone scintigram analysis developed by the authors, its practical application, and beyond. The process of developing databases and programs for practical use will be presented in chronological order. Updates of the post-market version and the applications of the prototype system to a regulatory science research are also described.