3D co-occurrence matrix based texture analysis applied to cervical cancer screening
Independent thesis Advanced level (degree of Master (Two Years)), 30 credits / 45 HE creditsStudent thesis
Cervical cancer is the second most common cancer in women worldwide, approximately 471,000 new cases are diagnosed each year. In 2005, there were about 500,000 cases of cervical cancer and 260,000 cases caused death in worldwide . Cervical cancer starts as a precancerous condition, however the changes of precancerous are hardly detected by the naked eyes, special test such as Papanicolaou test are used to spot the conditions. These are time consuming to inspect visually. In the last 50 years there have been many projects to develop automated computer image analysis system for screening.
One of the most important changes in a cell when it becomes precancerous is a change in chromatin texture. The field of nuclear texture analysis gives information about the spatial arrangement of pixel gray levels in a digitized microscopic nuclei image. A well known method for quantifying textures in digital images is the gray level co-occurrence matrix(GLCM). This method tries to quantify specific pairwise gray level occurrence at specific relative positions. In this project, firstly we have developed and tested three image normalization methods : gradient based intensity normalization, histogram equalization and standardizing normal random variables; secondly we have developed 2D gray level co-occurrence matrix calculation and 3D gray level co-occurrence matrix calculation, thirdly compared Haralick features with adaptive feature vectors from class distance and class difference matrices (adaptive texture feature) based on the 2D gray level co-occurrence matrix; compared the Haralick features with adaptive feature based on the 3D gray level co-occurrence matrix. Our result shows that neither of the 3D results yields a significant improvement from 2D results.
Place, publisher, year, edition, pages
IT, 12 041
Engineering and Technology
IdentifiersURN: urn:nbn:se:uu:diva-180850OAI: oai:DiVA.org:uu-180850DiVA: diva2:551578
Master Programme in Computer Science
Bengtsson, EwertKaati, Lisa