Lesion Segmentation — Patent Granted!

MRI_Body.261135135_stdBack in 2008, I (along with colleagues at Siemens Corporate Research) invented a system to find and segment tumors in full-body MRI scans. It’s challenging to find all types of tumors across the entire body, but the ability to automatically detect tumors wherever they are can aid early detection and save lives.

We patented the findings in 2008 and received confirmation today that the patent has been granted (#8,155,405). Read on for a quick overview of the approach and a few useful links if you’re interested in seeing how it works!

System and Method For Lesion Segmentation In Whole Body MRI.
Gozde Unal, Gregory G. Slabaugh, Tong Fang, Shawn Lankton, Valer Canda, Stefan Thesen, and Shuping Qing. US Patent Number: 8,155,405. Filed March 2008. Granted April 2012.

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Making Active Contours Fast

Active contours are a method of image segmentation. They are well-loved for their accuracy, ease of implementation, and nice mathematical underpinnings. However, a full level-set implementation can be quite slow, especially when dealing with large data! Here are some tips to speed things up. By combining these ideas and solid programming techniques I’ve been able to get active contour trackers running at hundreds of frames per second!
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CVPR 2008 Wrap-Up and Selected Papers

I return today from a week-long trip to Anchorage, Alaska. I spent the week enjoying the beautiful mountains, and the exciting science being presentented at the Conference for Computer Vision and Pattern Recognition (CVPR 2008) [here are some links to lots of papers from the conference]. This was my first trip to this conference, and I must say that I was impressed with the quality of the work presented. Below, I list some of my favorite papers and give a (very) brief overview:

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Tracking Through Changes in Scale

I will be presenting “Tracking Through Changes in Scale” at the International Conference on Image Processing (ICIP) in San Diego in October, 2008. This tracker uses a two-phase template matching algorithm in conjunction with a novel template update scheme to keep track of objects as their appearance and size changes drastically over the course of a video sequence.

The pdf, presentation material, and citation information will be available on the publications page after the conference. Below are videos of the experiments shown in the paper:

 
LEAVES Sequence (High Resolution Download – 11.2Mb)

 
VEHICLE Sequence (High Resolution Download – 34.8Mb)

 
BOAT Sequence (Hi Resolution Download – 2.34Mb)

Tracking and Surveillance Projects

I took a special topics course in Spring 2008 at Georgia Tech, ECE 8893: Embedded Video Surveillance Systems. The course included three projects, each shown below. Detailed information about the algorithm is in the source code comments. (All the source is in Python)

Project 1: Activity Density Estimation

Use background subtraction to find moving foreground objects in a video sequence. Then, color-code regions with the most activity. Here is the result:

Source: p1.py

Project 2: Styrofoam Airplane Tracking

Find all white styrofoam planes in the scene and track them throughout the scene. We used color thresholding and simple dynamics to do the tracking.

Source: p2.py

Project 3: Pedestrian Tracking

Count and track the pedestrians that cross on a busy sidewalk. We use a combination of motion estimation via background subtraction and feature matching using the Bhattacharyya measure.

Source: p3.py
Final Report: p3.pdf

Most of this code is very hack-y because it was done quickly. However, it was
fun to learn Python, and the class was enjoyable overall.

Decoupling Camera and Target Motion

Video tracking is widely used for surveillance, security, and defense purposes. In cases where the camera is not fixed due to pans and tilts, or due to being fixed on a moving platform, tracking can become more difficult. Camera motion must be taken into account, and objects that come and go from the field of view should be continuously and uniquely tracked. We propose a tracking system that can meet these needs by using a frame registration technique to estimate camera motion. This estimate is then used as the input control signal to a Kalman filter which estimates the target’s motion model based on measurements from a mean-shift localization scheme. Thus we decouple the camera and object motion and recast the problem in terms of a principled control theory solution.

Our experiments show that using a system built on these principles we are able to track videos with multiple objects in sequences with moving cameras. Furthermore, the techniques are computationally efficient and allow us to accomplish these results in real-time. Of specific importance is that when objects are lost off-frame they can still be uniquely identified and reacquired when they return to the field of view.

This work was published in the Proceedings of the SPIE on Electronic Imaging in this paper: Improved Tracking by Decoupling Camera and Target Motion.

See this paper and more on the publications page.

Vision Research Report

Recently I wrote about some startup companies in computer vision. However, this is only part of a good industry analysis. I also want to explore some of the interesting research going on in the field. Below is a list of some of the vision research that I’ve come across that seems most interesting (and applicable/marketable).

Seam Carving

This is brilliant (and brilliantly simple work). It solves a problem, and in doing so gives us tools to solve problems we didn’t even know we had! Its hard to explain, check the video out.

Dr. Ariel Shamir has a host of other interesting research as well: link.

Read on for more great research: Continue reading “Vision Research Report”

Computer Vision Startups

I have spent some time researching startup companies involved in computer vision. This has largely been in an effort to understand the marketability of computer vision research (which I spend much of my time learning about and contributing to). In this post, you’ll find a list of some notable companies. Let me know if you know of some other good ones. (Of course this doesn’t include the big, big companies like Siemens, GE, Phillips, and HP that are working on medical image processing every day! Continue reading “Computer Vision Startups”