Research on Automatic Identification and Localization of Medical Imagery Morphology and Its 3-Dimensional Image Reconstruction(2/3)

The objective of this three-year proposal is to develop an automatic methodology for precisely identification, localization, and 3-dimensional visualization of tiny multi-lesion from large-scale medical imageries using the data density functional method with low computational complexity. The research consequences will benefit planning and guidance of modern surgery, clinical investigations, rehabilitation, and so forth. Based on our previous research work, we had successfully connected the quantum mechanics and the technique of artificial intelligence then created an automatic methodology called data density functional method. Also based on the research consequences from our MOST proposal this year, three contemporary techniques, the Lucas-Kanade optic flow, the pixel connectivity, and the generalized gradient approximation, had been integrated into our proposed method. The hybridized method eventually resolves the issues from image film alignment and labeling of pixels, the common problems in medical imaging setting. Then 3-dimensional brain tumor morphologies were successfully reconstructed from sets of magnetic resonance (MR) imagery automatically. Relevant automatic algorithms had also been manipulated on the platforms of MATLAB and Python programming environments for specific applications and problem solutions. A series of SCI journal papers and conference papers were published in interdisciplinary fields including machine vision, rehabilitation assistance, gait analysis, dynamic object tracking, and so forth. However, the research consequences and the relevant automatic algorithms also pointed out an inevitable inconvenience from the proposed method. The proposed method was constructed by connecting the quantum mechanics and machine learning methods. All information of the employed medical imagery was globally mapped into a specific energy space to estimate the relevant energy functionals. This means that while measuring the similarity of an employed medical imagery of interest, whole of pixel information should be taken into the estimation processes. The larger the image size, the higher the computational complexity. The computational complexity of the proposed method is about O(n^2), where n represents the image dimension. Additionally, the proposed method could not be directly applied on the cases of multi-tumor detection of a medical imagery. Auxiliary detection and additional algorithmic estimates are inevitable. To conquer the aforementioned predicaments and reinforce the data density functional method, thus, in this three-year proposal we would like to extend those fundamental studies to pragmatic applications by providing a new automatic methodology for precisely identification, localization, and 3-dimensional visualization of multi-lesion from large-scale medical imageries with low computational complexity. It should be emphasized that even though the preliminary researches were successfully achieved on the problem of automatic brain tumor detection as well as its 3-dimensional visualization, the precisely automatic identification, localization, and 3-dimensional visualization of the target tiny tissues, subthalamic nucleus, the corresponding neighboring regions, and the other brain tissues are still tough problems in clinical investigations and surgery guidance.