Breast cancer is the most common life-threatening type of cancer affecting women in The Netherlands. The success of the treatment of breast cancer largely depends on the stage of a tumor at the time of detection. Therefore, early detection of breast cancer is essential. Although mammography screening is currently the most effective tool for early detection of breast cancer, up to one-fifth of women with invasive breast cancer have a mammogram that is interpreted as normal, i.e., a false-negative mammogram result. To overcome such limitations, Computer-Aided Diagnosis (CAD) systems for automatic classification of breast lesions as either benign or malignant are being developed. CAD systems help radiologists with the interpretation of lesions, such that they refer less women for further examination when they actually have benign lesions. In this thesis we constructed several types of classifiers, i.e., Bayesian networks and support vector machines, for the task of computer-aided diagnosis of breast lesions.

Classification of Breast Lesions in Digital Mammograms, Master Thesis (1.3 MB PDF)

Digital images have become an important source of information in the modern world of communication systems. Thinks about FBI fingerprints, MRI scans, ECG data, digital camera photos, building plans etc. In their raw form, these images require an enormous amount of memory. With the introduction of multimedia computing, the demand for processing, storing and transmitting images has increased exponentially. In the last two decades a considerable amount of research has been devoted to tackle the problem of image compression.

During the Research Lab 4 course, I wrote a paper about the similarities and differences, advantages and disadvantages of the two well known compression techniques: Fourier and Wavelets. JPEG, JPEG2000, DiVX and MPEG, all of them are based on one of these two techniques.

Research Paper (3.8 MB PDF) (5.5 MB PS)

Presentation (1.9MB PPT) (2.2MB PDF) (HTML)

Currently, hypercomputation is still very theoretical and there is still enough to discuss. Hypercomputation concerns the study of computation beyond that defined by the Turing machine. It is a multi-disciplinary research area across a wide variety of fields, including computer science, philosophy, physics, electronics, biology and artifical intelligence.

For a seminar to be given at the university, I’ve written with two fellow students a paper about the pre-hypercomputation history, explanation of hypercomputation and the philosphy of hypercomputation and artificial intelligence.

Paper (DUTCH) (518 KB PDF) (1.1 MB PS)

Presentation (ENGLISH) (1.6 MB PPT) (744 KB PDF)

Continue reading

During a marketing high tech products class I participated in a group project and did some observational research on self promoting television sets for Philips Consumer Electronics. Our goal was to deliver ideas to Philips Consumer Electronics for the promotion of televisions in television shops by observing people who are buying a new TV set.

Paper (142 KB PDF)