Medical-Image-Processing

This repository contains some works that I have done in Computation Radiology Lab UR Medical Center.

It mainly contains the following parts:

Phase contrast X-ray computed tomography (PCI-CT) deep learning classifier.
Vertebrae Segmentation in Pathological Spine CT via Fully Convolutional Neural Network.
Preprocessing of Raw image data (.mhd/.raw/.dicom) of Annotated lymph node CT dataset and Microsoft's Spine Web dataset

Keypoints of PCT-CT deep learning project

This project got accepted at SPIE2017 conference for oral representation. You can check out our publication here. A refined version of this paper is uploaded here.
It makes fully use of CaffeNet and Inception-v3 Net to classify CT samples. Please check out Deep_Transfer_Learning_PCI_CT.pdf for full detail.
Some result is shown below:

The classification result we got from a fine-tuned CaffeNet.

Keypoints of Vertebrea Segmentation project
Preprocessing raw spine dataset (About 20GB), you can check out the sample spine CT scan before and after preprocessing below.
The preprocessing techniques we have explored include: Histogram Equalization, Adaptive Histogram Equalization and Constrast Stretching

For full detail, please refers to our report here.
Prepared the lmdb file so that the data can be directly fed into Convolutional Neural Network.
Used fully convolutional neural network to segment the image.