Overview

This proposal aims to improve Medical X-Ray Imaging using Machine Learning (ML) and Sparse Representation Theory(SRT). The project’s main goal is to combine, state of the art image enhancing techniques in x-ray medical imaging focusing in mammography, and feed them to specially designed ML algorithms leading to novel Computer Assisted Diagnosis (CAD) tools. The emerging state of the art research field of ML, actually provides computers the ability to “learn” without being explicitly programmed. ML has been greatly evolved the last decade due to the immense computational capabilities of the new generation of Graphics Processing Units’ (GPU). First efforts to apply ML in medical x-ray imaging have taken place, with promising results, as in the case of chest x-ray imaging (Zhiyong Lu et al., 2017), computer aided detection of tuberculosis (Philipsen et al., 2015), detection tumour growth within the human skeleton (Idota et al., 2016) and very recently a first attempt for the case of mammography was done by the Department of Physics of Complex Systems in Hungary (Ribli et al., 2018) using the INbreast (Moreira et al., 2012) full-field digital mammographic dataset. The application of ML techniques in medical imaging is therefore appearing as a very promising, novel, state of the art research field for the years to come. On the other hand, SRT founded within the last decade, revolutionized the signal and image processing approaches. The application of SRT-based methods into “classical” problems that puzzles scientists for decades, such as signal or image denoising, provided surprisingly good results. Furthermore, SRT-based methods known as Compressive Sampling (CS) have challenged even the well-established sampling theorem of Shannon. The combination of these two fields is expected to provide new effective tools for image enhancement and diagnostic capabilities in medical imaging.

The application of both ML and SRT in medical imaging is still at an early stage, designating it as an open and very promising field of research and innovation. Considering, in particular, the X-Ray medical imaging field, it is expected that advanced, SRT-based techniques for image processing and enhancement along with ML based analysis and classification, will lead to superior image quality, with improved diagnostic potential.

Steps in the process of breast composition

The proposed research will focus on adaptation, evaluation and testing of a series of medical image enhancement and processing techniques, as well as advanced image analysis approaches. A software library is to be developed, with algorithms for de-noising (filtering), edge and contrast enhancement, super resolution and ML based CAD. This is an innovative approach, where medical images will be enhanced using state of the art SRT techniques and then will be fed to ML algorithms in order to automatically detect lesions and malignancies in Breast imaging. Expect from the innovating approach of combining of SRT and ML, the use of ML for the detection of malignancies in breast x-ray imaging, is a very promising, not yet thoroughly investigated, research field. For the purpose of this research, the existing in-house Monte Carlo XrayImagingSimulator (Bliznakova, 2003; Bliznakova et al., 2010; Lazos et al., 2003, 2000).developed by Biomedical Technology Unit (BITU) and used in numerous research schemes and projects from teams all around the world, will be used. Applications of this simulator, which has been developed the last 15 years, are not limited in the field of medical imaging, as in the case of project QUICOM, where it was employed for the non-destructive testing of airplane carbon fibre parts.

Breast 3D model

The specific objectives of the proposed project are:

  1. Development/Implementation of an ML based CAD algorithm for the automatic detection of lesions and malignancies in Breast imaging 
  2. Development of an X-ray Medical Image Enhancement Library that will comprise the following algorithms (utilizing the SRT) for:
    • Image denoising (filtering) in 2D and 3D imaging
    • Super resolution for improving the resolution of medical images
    • Image contrast and edge enhancement 
  3. Design and development of software phantoms for the production of synthetic data using X-Ray Imaging Simulator and physical phantoms using conventional and 3D printing techniques.
  4. Validation of the developed algorithms using real data.
  5. Conduction of an evaluation study for the performance evaluation of the derived ML based CAD algorithms versus the diagnosis provided by medical experts

In order to achieve these goals, the duration of the project is set to 36 months and a multidisciplinary research team of highly experienced researchers, has been assembled. The members of the team are affiliated with research groups that are highly experienced in these fields and have long track record in the areas covered by the proposed project. 

A successful outcome of the proposed project will lead to better tools for breast lesion and malignancies diagnosis, creation of powerful SRT and ML tools for future research and will allow involved groups and investigators to expand the application of this approach to other fields of X-ray Medical Imaging and to remain in the front end of these research fields.

1η Προκήρυξη ερευνητικών έργων ΕΛ.ΙΔ.Ε.Κ. για την ενίσχυση των μελών ΔΕΠ και Ερευνητών/τριών και την προμήθεια ερευνητικού εξοπλισμού μεγάλης αξίας

Τμήμα Ερευνητικών Έργων 

Αθήνα, 07.12.2018 | Α.Π. 7518

Πρότυπα Έγγραφα Προτάσεις Κατηγορίας Ι ή ΙΙ (Β΄ Φάση Υποβολής)