MathVisionTools

Medical Image Analysis Prototyping

Bart M. ter Haar Romeny & Markus A. van Almsick

Eindhoven University of Technology
Department of Biomedical Engineering
Biomedical Image Analysis Group
B.M.terHaarRomeny@tue.nl

Abstract

This paper describes a new international collaborative initiative for the assembly of a high level medical image analysis toolkit, based on Mathematica, to do efficient design and development of advanced computer vision algorithms for computer-aided diagnosis. Mathematica has come to a point that the combined symbolic and numeric power makes it a versatile and efficient framework for prototyping of complex algorithms.

This is a first announcement, and call for interest and participation.

Introduction

Medical images are made in huge quantities. The advent of the possibility to make 'slices' of patients at high resolution in a non-invasive way has revolutionized the making of medical diagnoses. Currently, 80% of all diagnoses in Western hospitals are based on and/or are supported by medical images.

It is also big business. Today, about one third of all capital investments in a modern hospital are done for medical imaging. Magnetic Resonance Imaging (MRI) scanners, Computed Tomography (CT) scanners, angiography suites, cardiovascular imaging suites, mammography, ultrasound, just to name a few imaging modalities. Hospitals become fully digital in an increasingly fast pace. It is estimated that in the US and Europe about 75% of the images are acquired directly in a digital form, and are stored in a so-called Picture Archiving and Communication System (PACS). A typical 1000 bed university hospital produces about 10 Terabyte of imaging data per year (this number increases 10-15% per year) and is required to store these for 10 years. Lightboxes hardly exist anymore. They are replaced by digital workstations (hundreds to thousands per hospital), that today mainly do image logistics and tele-radiology, but where a strong need for advanced processing emerges.

The MRI scanner is the one of the most versatile tomographic scanning systems, and is capable of measuring a wide range of physical properties of tissues. CT scanners recently made a full comeback in speed and resolution. Most CT scanners sold today are multi-slice scanners, i.e. they acquire attenuation data of the X-rays with multiple rows of detectors, typically 4-64. A CT scan of the lung can easily be 2000 slices, with (512^2) or (1024^2) in-slice resolution (typically 0.5mm in any direction), acquired in a single breathhold.

The need for computer-assisted diagnosis algorithms

The amount of images becomes so overwhelming, that computer assistance in the diagnostic process is strongly needed. This new field, "Computer Aided Diagnosis" has recently seen some remarkable successes, leading to commercial activity (see e.g. R2 Technology, DeusTech, iCAD). The computer analyses the images, and reports the location and type of the suspected lesion by means of a circle or marker on the original image, as an attention sign for the medical professional. In specific cases, such as in the detection of microcalcifications, masses and stellate tumors in mammography, the computer assisted diagnosis was 15-20% more sensitive than expert reading.

Currently, there are marked developments in the fields of large scale screening activities, i.e. automated lung nodule detection [2], automated analysis of retinal vessel leakage in screening for diabetes (see e.g. EyeCheck), automated detection of colon polyps (which may develop into a cancer) in virtual endoscopy, automated melanoma detection in skin cancer screening, and the above mentioned areas in mammography. Many new fields are currently explored, such as follicle detection for fertility assessment [6], hand bone measurements for bone-age determination, etc. See for the recent developments the proceedings of the important conferences in this field, such as MICCAI, IPMI and CARS.

The big question is how to develop the sophisticated image analysis algorithms needed. These computer vision methods need a highly mathematical approach, in which differential geometry, partial differential equations, statistics, linear algebra, etc. play a crucial role.

Mathematica has rarely be considered for this task, as, many years ago, it used to be slow and memory intensive. This has considerably changed since version 4, and with the current versions the rapid prototyping, the design of the algorithm before they are put into a commercial or specialized application, has become extremely efficient in Mathematica [3]. In Eindhoven we have now 3 years of extensive experience in the role of Mathematica in our research on Biomedical Image Analysis, and in the education of students [4] (see also the accompanying paper at this conference). The strategy of 'here is paper: read it, understand it, and make an implementation in Mathematica' worked amazingly well, even for 3 months projects.

Most current Digital Image Processing toolkits are rather basic, and contain the elementary operations on images, such as filters, geometric transformations, histogram operations, mathematical morphology and edge detection. There is however a great need for a toolkit with high level building blocks for CAD and quantitative extraction of parameters. We list a short list of exemplary needs:

- registration methods for intra- and multi-modality image matching
- texture analysis and statistical pattern recognition tools
- model based (atlas) shape recognition methods
- edge-preserving enhancement methods for denoising
- retrieval methods for similar images in huge databases
- robust analysis of optic flow dynamics, e.g. in dynamic heart wall studies

MathVisionTools

MathVisionTools is a library of such high level functions, which is initiated and managed by Eindhoven University of Technology. We have started

See for a more extensive technical description the accompanying paper by M.A. van Almsick at this conference [8]. 
So far, the package contains:

- an extensive package for doing high order differential geometry on images of any dimension;
- a series of versatile conversion routines from specific image formats from medical devices (Kretz ultrasound, Biorad microscopy, MRL high field microscopy) to the universal DICOM format;
- multi-modality image registration routines;
- multi-scale optic flow detection on 2D-time image sequences [9];
- multi-scale edge focusing techniques for robust segmentation [6];
- active shape and active appearance algorithms for shape variability analysis;
- deblurring Gaussian noise [11];
- maximum intensity projection of black/white and color images;
- detection of stellate tumors in mammography (based on [10]);
- geometry-driven diffusion techniques for enhancement by edge-preserving smoothing;
- and many in development.

Many of the functions included are based on the many functions released in the recent textbooks on multi-scale image analysis [1, 12], and the output of a wide range of successful student projects at Eindhoven University [4] (see also  http://www.bmi2.bmt.tue.nl/image-analysis/Education/index.html). The course "Front-End Vision & Multi-Scale Image Analysis" (FEV) is a popular national course in the Netherlands, and is completely given in Mathematica.

We opt for the following:

- keep a central moderated base of computer-aided diagnosis functions, available through GetURL and Web-Mathematica;
- organize periodically dedicated workshops between collaborating developer institutes;
- make available a huge web repository with a wide variety of (anonymized) medical images for testing and mutual comparisons. Our contacts with university hospitals are excellent;
- set up a common GUI by means of the new GUI toolkit in Mathematica;
- validation of our algorithms between collaborating members, and exchange the results;
- collaboration and exchange with the NLM Segmentation and Registration Toolkit (ITK);

On the longer term, this may lead to (commercial) webservices for interested medical parties to have their images given a second opinion diagnosis for a small fee per image.

Mathematica is an excellent tool for image analysis prototyping, but may not be the best tool for operations on huge datasets. As medical images become larger and larger, this is a point of serious concern. MathVisionTools is not intended to be a industrial quality and speed toolkit, but a research tool for efficient and fast design and prototyping. We will include many existing pieces of code in C++, Java and Matlab by MathLink inclusion. The developments of hardware graphics cards support for close to real-time calculations on huge datasets for a very low cost are exciting. MathVisionTools focuses on the core business of proper CAD: the development of high quality, high level, mathematically well underpinned, image analysis algorithms.

Call

We call for participation by interested institutes and companies. Participation will be on an exchange basis. The code will be made available to the partners only in the first stage of the project. During the project we will develop in conjunction a strategy how to distribute the code to the world. The code will be included in the common package after publication in the scientific literature by the authors.

Please contact:

Prof. Bart M. ter Haar Romeny, Dipl. Ing. Markus van Almsick
Eindhoven University of Technology
Department of Biomedical Engineering
Den Dolech 2, WH2.106
5600 MB Eindhoven, the Netherlands
Tel. +31-40-2475537
Fax +31-40-2472740
Email: B.M.terHaarRomeny@tue.nl, M.v.Almsick@tue.nl
URL: http://www.bmi2.bmt.tue.nl/image-analysis/
MathVisionTools: www.mathvisiontools.net

References