@inbook {897,
	title = {Virtual dissection of the colon: technique and first experiments with artificial and cadaveric phantoms},
	booktitle = {Medical Imaging 2002: Visualization, Image-Guided Procedures, and Display},
	year = {2002},
	note = {ScopusID: 0036061143doi: 10.1117/12.466982},
	month = {2002///},
	pages = {713 - 721},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Bellingham; Washington},
	abstract = {Virtual dissection refers to a display technique for polypdetection, where the colon is digitally straightened and then 
flattened using multirow detector Computed Tomograph (CT) 
images. As compared to virtual colonoscopy where polyps may be 
hidden from view behind the folds, the unravelled colon is more 
suitable for polyp detection, because the entire inner surface 
of the colon is displayed in a single view. The method was 
tested both on artificial and cadaveric phantoms. All polyps 
could be recognized on both phantoms. This technique for virtual 
dissection requires only a minimum of operator interaction.
}
}
@inbook {1012,
	title = {Standardizing the MR image intensity scales: making MR intensities have tissue-specific meaning},
	booktitle = {Medical Imaging 2000: Image Display and Visualization},
	year = {2000},
	note = {ScopusID: 0033745402doi: 10.1117/12.383076},
	month = {2000///},
	pages = {496 - 504},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Bellingham; Washington},
	abstract = {One of the major drawbacks of Magnetic Resonance Imaging (MRI)has been the lack of a standard and quantifiable interpretation 
of image intensities. Unlike in other modalities such as x-ray 
computerized tomography, MR images taken for the same patient on 
the same scanner at different times may appear different from 
each other due to a variety of scanner-dependent variations, and 
therefore, the absolute intensity values do not have a fixed 
meaning. We have devised a two-step method wherein all images 
can be transformed in such a way that for the same protocol and 
body region, in the transformed images similar intensities will 
have similar tissue meaning. Standardized images can be 
displayed with fixed windows without the need of per case 
adjustment. More importantly, extraction of quantitative 
information with fixed windows without the need of per case 
adjustment. More importantly, extraction of quantitative 
information about healthy organs or about abnormalities can be 
considerably simplified. This paper introduces and compares new 
variants of this standardizing method that can help to overcome 
some of the problems with the original method.
}
}
@inbook {1011,
	title = {Approach to standardizing MR image intensity scale},
	booktitle = {Medical Imaging 1999: Image Display},
	year = {1999},
	note = {ScopusID: 0032677406doi: 10.1117/12.349472},
	month = {1999///},
	pages = {595 - 603},
	publisher = {SPIE},
	organization = {SPIE},
	address = {Bellingham; Washington},
	abstract = {Despite the many advantages of MR images, they lack a standardimage intensity scale. MR image intensity ranges and the meaning 
of intensity values vary even for the same protocol (P) and the 
same body region (D). This causes many difficulties in image 
display and analysis. We propose a two-step method for 
standardizing the intensity scale in such a way that for the 
same P and D, similar intensities will have similar meanings. In 
the first step, the parameters of the standardizing 
transformation are {\textquoteright}learned{\textquoteright} from an image set. In the second 
step, for each MR study, these parameters are used to map their 
histogram into the standardized histogram. The method was tested 
quantitatively on 90 whole brain FSE T2, PD and T1 studies of MS 
patients and qualitatively on several other SE PD, T2 and SPGR 
studies of the grain and foot. Measurements using mean squared 
difference showed that the standardized image intensities have 
statistically significantly more consistent range and meaning 
than the originals. Fixed windows can be established for 
standardized imags and used for display without the need of per 
case adjustment. Preliminary results also indicate that the 
method facilitates improving the degree of automation of image 
segmentation.
}
}
