Refereed full papers (journals, book chapters, international conferences)

2001

@article{SaU2001,
	vgclass =	{refpap},
	author =	{Punam K. Saha and Jayaram K. Udupa},
	title =	{Optimum image thresholding via class uncertainty and
	region homogeneity},
	journal =	{IEEE Transactions on Pattern Analysis and Machine Intelligence},
	volume =	{23},
	number =	{7},
	pages =	{689--706},
	month =	{July},
	year =	{2001},
	url =	{http://www.computer.org/tpami/tp2001/i0689abs.htm},
	url1 =	{http://ieeexplore.ieee.org/iel5/34/20256/00935844.pdf},
	abstract =	{Thresholding is a popular image segmentation method that
	converts a gray-level image into a binary image. The selection of
	optimum thresholds has remained a challenge over decades. Besides being
	a segmentation tool on its own, often it is also a step in many
	advanced image segmentation techniques in spaces other than the image
	space. Most of the thresholding methods reported to date are based on
	histogram analysis using information-theoretic approaches. These
	methods have not harnessed the information captured in image
	morphology. Here, we introduce a novel thresholding method that
	accounts for both intensity-based class uncertainty a histogram-based
	property and region homogeneity an image morphology-based property. A
	scale-based formulation is used for region homogeneity computation. At
	any threshold, intensity-based class uncertainty is computed by fitting
	a Gaussian to the intensity distribution of each of the two regions
	segmented at that threshold. The theory of the optimum thresholding
	method is based on the postulate that objects manifest themselves with
	fuzzy boundaries in any digital image acquired by an imaging device.
	The main idea here is to select that threshold at which pixels with
	high class uncertainty accumulate mostly around object boundaries. To
	achieve this, a new threshold energy criterion is formulated using
	class-uncertainty and region homogeneity such that, at any image
	location, a high energy is created when both class uncertainty and
	region homogeneity are high or both are low. Finally, the method
	selects that threshold which corresponds to the minimum overall energy.
	The method has been compared to a recently published maximum segmented
	image information (MSII) method. Superiority of the proposed method was
	observed both qualitatively on clinical medical images as well as
	quantitatively on 250 realistic phantom images generated by adding
	different degrees of blurring, noise, and background variation to real
	objects segmented from clinical images.},
}