Over the past decade, there has been increased interest in quantifying cell populations in tissue sections. Image analysis is now being used for analysis in limited pathological applications, such as PAP smear evaluation, with the dual aim of increasing for accuracy of diagnosis and reducing the review time. These applications primarily used gray scale images and dealt with cytological smears in which cells were well separated. Quantification of routinely stained tissue represented a more difficult problem in that objects could not be separated in gray scale as part of the background could also have the same intensity as the objects of interest. Many of the existing semiautomatic algorithms were specific to a particular application and were computationally expensive. Hence, this paper investigates the general adaptive automated color segmentation approaches, which alleviate these problems. In particular, competitive learning and the fuzzy-kohonen networks are studied. Four adaptive segmentation algorithms are compared using synthetic images and clinical microscopy slide images. Both qualitative and quantitative performance comparisons are performed with the clinical images. A method for finding the optimal number of clusters in the image is also validated. Finally the merits and feasibility of including contextual information in the segmentation are discussed along with future directions.
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