Image Analysis

Projects in the area of image analysis focus on the visualization, enhancement, registration and interpretation of medical images of various types. Examples of activities include methods for automated segmentation of organ tissue areas and parametric modeling of contrast enhancement for dynamic contrast-enhanced MR-image.

Automated Cytology

Cervical cancer is the second most common cancer in women worldwide with about 80% of cases occurring in low-income countries. The Papanicolaou or Pap test is the primary screening test for the disease involving the microscopic examination of cells sampled from in and around the cervix. Precancerous changes can be treated before they have a chance to progress to cancer. Cancer can also be successfully treated provided it is detected early.

In developed countries widespread screening and treatment programmes have significantly reduced the incidence of invasive cervical cancer. Nevertheless the Pap test is not a perfect test with one in every 10 to 20 positive cases missed in routine screening. MedTech West is a partner in developing computerised cell image analysis methods to both improve screening accuracy and to facilitate the production of a low-cost and robust screening solution for the developing world.

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Brain segmentation

Patient specific brain segmentation refers to the automatic labelling of the different tissues/structures in the brain—e.g. white matter, grey-matter, cerebrospinal fluid using information obtained from one or more imaging modalities; e.g. different MRI techniques and CT. The resulting segmentation can then be used to construct patient-specific electromagnetic and biomechanical models. The former find use in applications such as localizing the source of epileptic seizures or localizing hyperthermia treatment for tumours. The latter find use in applications such as the modelling of tissue deformation during and after surgical intervention.

The aim of this project is to develop state-of-the-art algorithms for automatically and accurately performing patient specific brain segmentation. The efficacy of each algorithm will be determined using real clinical data. Automatic segmentation will be quantitatively compared to manual segmentations performed by several radiologists. Efficacy will also be assessed indirectly in terms of the measurable impact on performance in several applications; e.g. localizing the source of epileptic seizures from EEG measurements.

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Improved CAD for breast and prostate MRI

In Sweden the most frequently diagnosed cancer and the leading cause of cancer death in women is breast cancer whilst in men it is prostate cancer. This picture holds more generally for the developed world except that prostate cancer is the third leading cause of cancer death in men*. Magnetic resonance imaging (MRI) is being increasingly used clinically as a supplemental tool in the detection and characterisation of both cancers. In particular it is used for assessing the degree of progression, determining the most appropriate treatment, and for patient follow-up after cancer treatment. The clinical utility of prostate MRI is presently limited by the relatively low sensitivity and specificity (certainty that what was detected is cancer) of the traditionally used T2-weighted images. Similarly the clinical utility of breast MRI is presently limited by the poor to moderate specificity of the traditionally used dynamic contrast-enhanced MRI.

MedTech West is a partner in developing novel CAD techniques to improve the sensitivity and specificity of MRI, and concomitantly its clinical utility, for both breast and prostate cancers.

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DTI tools

Lesions affecting the visual pathways in the human brain are common and may cause reduced visual acuity or visual field defects, either directly or as a result of surgery. These pathways can be visualised using tractography. The procedure is based on a combination of a magnetic resonance imaging technique known as diffusion tensor imaging (DTI) and computer-based image analysis.

MedTech West is developing novel DTI-based software tools for visualising and quantitatively characterising the visual pathways. The aim is to provide a suite of tools for use in neurosurgical planning.


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Image based oral cancer diagnostics

For more information on this project, contact Artur Chodorowski at


Anatomy segmentation in 3D medical images

Locating and segmenting anatomical structures such as the heart, vertebrae or di event regions of the brain in an image is an important step for many clinical applications, including visualization, surgical planning and dose radiation therapy. It is also an important tool in order to obtain quantitative measures for diagnostic purposes. We are interested in, for example, finding biomarkers for determining the risk of myocardial infarction. The rapid development of techniques for producing medical images, like magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound, has enabled high-resolution and ne-detailed 3D images of the human body. This opens up new ways to diagnose and understand diseases, but the technology also creates new challenges. There is a great demand for automated methods as manual analysis is time-consuming and quickly becomes infeasible. To meet this demand, this project aims both at the development of general theory and methods, and at the solution of concrete applied problems in close collaboration with clinical experts.

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