Machine Learning in Medical Image Processing

Dear Colleagues,

With the rapid improvement of computing power, machine learning-based algorithms have received considerable attention from researchers and academics due to their convincing performance in medical image processing and recognition. There are a variety of medical imaging modalities, including ultrasound, X-Ray, CT, MRI, and pathology imaging, that physicians access to a wealth of data. However, we still lack effective tools to accurately identify important information in these medical images. Machine learning is a technique for recognizing patterns that can be applied to medical image processing, image segmentation, image interpretation, image fusion, image registration, computer-aided diagnosis, and image-guided therapy.

A considerable number of machine learning technologies have been proposed, including support vector machine (SVM), neural network (NN), KNN, convolutional neural network (CNN), recurrent neural network (RNN), long short term memory (LSTM), extreme learning model (ELM), generative adversarial networks (GANs) etc. Through machine learning technology, we can extract information from images and represents information efficiently and efficiently. Machine learning facilitates and assists physicians for more accurate and faster diagnosis of diseases. These techniques also enhance the ability of physicians and researchers to understand how to analyze the generic variations which will lead to disease. Therefore, the purpose of this Special Issue is to present the developments and achievements of the recently popular machine learning algorithms in medical image analysis and processing. Topics of interest include, but are not limited to the following:

1. Certain element detection and recognition
2. Image segmentation and interpretation
3. Image reconstruction
4. Image registration and fusion
5. Computer-aided diagnosis
6. Other applications in medical image analysis

Prof. Chuan-Yu Chang
Guest Editor

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