Jakob Raundahl: Mammographic Pattern Recognition

I have been reading Jakob Raundahl's PhD dissertation which is titled as Mammographic Pattern Recognition. The focus of his thesis is very much like what I want to do except that his image acquisition technique is different.

In his thesis, Raundahl has two major parts one is the methodological aspects of the research and the other is on application to clinical data and discussions derived therefrom. Furthermore, he claims that the common ways to evaluate new automated density measures are either through visual assessment or correlation with radiologist readings. Consequently, even though advanced and powerful image analysis methods are applied the endpoint is still an approximation of a radiologist giving a score of 1-4 based on visual assessment.

Since some types of hormone replacement therapy (HRT) has been proven to increase mammographic density, Raundahl uses images from HRT studies to evaluate density measures by radiologists ability to separate the HRT and placebo populations.

The automated approaches at measuring mammographic density and mammographic patterns mentioned in this paper are:

1. Automated Thresholding Method

He discusses three algorithms: 

• Kittler and Illingworth’s optimal threshold (KI)
• KI applied to the variance normalized image (KIVA) (suggested by Sivaramakrishna et al. ) 
• Adaptive threshold based on the mean breast intensity (1.3*Avg). 

Conclusion:
These types of approaches are very developed and it is better to use more complex methodologies that includes structural and textural information. Similarly, for the purpose of my own study, I need to develop indicative measures able to capture structure.

1. Unsupervised Method
2. Supervised Method
3. Supervised framework extended using SFS feature selection