MIC

Medical Image Computing Module Development

Medical Image Enhancement (MIE)

The source code of MIE is in MedicalImageEnhancement.py.

Table 1 compares the three filters, including smoothing, sharpening, edge detection in the aspects of the kernel, experiment demo, and time cost of running. The way of achieving a filter is to use the filter to perform convolution operations on 3D images.

Table 1: Comparison of different filters

Medical Image Segmentation (MIS)

The source code of MIS is in MedicalImageSegmentation.py.

Figure 1 shows the demonstration of 3D segmentation results achieved using three multiple viewing angles.

Figure 1: 3D segmentation result of the tumor

Table 2 shows the demonstration of the experiments on different global and local parameter combinations.

Table 2: Part experiments on different global and local parameters

Based on experiment results, the best global and local parameters are among experiments with ID from 2 to 3 considering whether the classification is true or false, positive or negative. The best way to find them is to define an indicator of the best result, then define a distance between the result indicator in current parameters and the best outcome, and finally search for the settings of minimum distance by deep learning. In that way, we do not need to search for the best parameters manually.

Chapter I - Medical image computing

Welcome to the MIC wiki!

Medical image computing (MIC) is an interdisciplinary field, there are three essential medical image analysis techniques: medical image enhancement (MIE) and medical image segmentation (MIS).

医学图像计算 (MIC) 是计算机科学、信息工程、电气工程、物理学、数学和医学交叉的交叉学科领域。在 MIC 领域内，存在三种基本的医学图像分析技术：医学图像增强 (MIE) 和医学图像分割（MIS）。

Introduction

Medical image computing (MIC) is an interdisciplinary field at the intersection of computer science, information engineering, electrical engineering, physics, mathematics, and medicine. This field develops computational and mathematical methods for solving medical images' problems and their use for biomedical research and clinical care. Within the MIC domain, there are three essential medical image analysis techniques: medical image enhancement (MIE) and medical image segmentation (MIS).

Downloads

3D Slicer download at https://download.slicer.org

Sample data download at https://github.com/MuGemSt/Medical-Image-Computing/releases/tag/nrrd

Usage

MIE

You are expected to program an image filtering algorithm with Python, which performs a convolution on the 3D volume MRHead.nrrd. The filters to be used, including the smoothing, sharpening, and edge detection filters.

1. Load data MRHead.nrrd to Slicer;
2. Import the source code MedicalImageEnhancement.py to Slicer. Then restart Slicer, and find Task A - MIE module in Assignment;
3. Open source code MedicalImageEnhancement.py;
4. After modifying your code, save it and then click on the Reload button to reload the module, so you don’t need to restart Slicer;

5. Change the layout to displace Red Slice only. Superimpose the MRHead onto MRHead_filtered, and then change the opacity to see the difference between them.

MIS

Region-growing algorithms can perform medical image segmentation tasks via delineating ROIs iteratively.

1. Load MRBrainTumor.nrrd from the files provided. Use the Editor module to draw a single dot in the slice which tumor has a clear boundary;

2. Import the source code MedicalImageSegmentation.py to Slicer. Then restart Slicer, and find Task B - MIS module in Assignment;
3. Open source code MedicalImageSegmentation.py;
4. After modifying your code, save it and then click on the Reload button to reload the module, so you don’t need to restart Slicer;
5. Click Apply to see the results. Tune the global and local parameters to find the best segmentation result.