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Students win prizes for improving image processing techniques for liver cancer detection and much more

Students in EECS 556: Image Processing, explore methods to improve image processing in applications such as biomedical imaging and video and image compression| Medium Read

Students in EECS 556: Image Processing, explore methods to improve image processing in applications such as biomedical imaging and video and image compression. The techniques are fundamental to companies such as KLA-Tencor, which offered prizes to two teams of students. The winning teams are highlighted below.

The course is taught by Jeff Fessler, William L. Root Collegiate Professor of Electrical Engineering and Computer Science, a leading expert in medical imaging with current and past projects in X-ray CT, MRI, PET, SPECT, radiation therapy, and image registration.

 

First place
Convolutional operator learning for imaging
Siying Li, Haowei Xiang, Alexander Zaitzeff, Xiyu Zhang

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IMAGE:  (L) Jing Zhang, Yong Zhang, Sean Park (all KLA Tencor), Alexander Zaitzeff, Siying Li, Haowei Xiang, Xiyu Zhang, Jeff Fessler

According to the team, Convolutional Analysis Operator Learning (CAOL) is a method to learn kernels/features from large datasets, which have many applications in signal/image processing, computer vision and machine learning. Designing computationally efficient and fast-convergence algorithms is a subject of great interest.

This project proposed an innovative method to reduce the problem dimension of CAOL and speed up its computation.  The team’s method shows faster convergence than the original CAOL acceleration method using majorizers.  When applying the learned kernels to sparse-view CT reconstruction, their proposed method gives better image quality than the original method.

Enlargebrain imaging
IMAGE:  a) True image b) FBP c) Original majorizer method d) Proposed method

Second place
Automatic segmentation of tumorous liver CT scans
Sang Choi, Caroline Crockett, Alexander Ritchie, Rebecca Shen

Enlargesecond place team
IMAGE:  (L) Jing Zhang, Yong Zhang, Sean Park (all KLA Tencor), Sang Choi, Alexander Ritchie, Caroline Crockett, Rebecca Shen, Jeff Fessler

“Liver cancer is among the leading causes of cancer deaths,” explained Rebecca Shen. “Many methods of treatment planning and measuring liver cancer require segmentation of tumorous regions by trained professionals. However, this segmentation is manual, tedious, and prone to differences between medical practitioners. Our project explored the implementation of semi-automated segmentation algorithms using the Potts model and the watershed algorithm.”

Data courtesy MIDAS: “Livers and liver tumors with expert hand segmentations

Enlargeidentifying tumors
IMAGE:  

Past projects: 2017 – 2016 – 2015 – 2014 – 2013 – 2011

brain imaging
second place team
identifying tumors
Portrait of Catharine June

Contact

Catharine June
ECE Communications and Marketing Manager

Electrical Engineering and Computer Science

(734) 936-2965

3301 EECS

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