New optimum thresholding method using region homogeneity and class uncertainty
Punam Kumar Saha, Jayaram K. Udupa · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2000
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 buried 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. The idea here is to select the optimum 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. Qualitative experiments based on both phantoms and clinical images show significant improvements using the proposed method over a recently-published maximum segmented image information (MSII) method. Quantitative analysis on phantoms generated under a range of conditions of blurring, noise, and background variation confirm the superiority of the new method.