Image segmentation is a pre-requisite to medical image analysis. A variety of segmentation algorithms have been
proposed, and most are evaluated on a small dataset or based on classification of a single feature. The lack of a
gold standard (ground truth) further adds to the discrepancy in these comparisons. This work proposes a new
methodology for comparing image segmentation algorithms without ground truth by building a matrix called
region-correlation matrix. Subsequently, suitable distance measures are proposed for quantitative assessment of
similarity. The first measure takes into account the degree of region overlap or identical match. The second
considers the degree of splitting or misclassification by using an appropriate penalty term. These measures are
shown to satisfy the axioms of a quasi-metric. They are applied for a comparative analysis of synthetic segmentation
maps to show their direct correlation with human intuition of similar segmentation. Since ultrasound
images are difficult to segment and usually lack a ground truth, the measures are further used to compare the
recently proposed spectral clustering algorithm (encoding spatial and edge information) with standard k-means
over abdominal ultrasound images. Improving the parameterization and enlarging the feature space for k-means
steadily increased segmentation quality to that of spectral clustering.
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