Comparison of algorithms for ultrasound image segmentation without ground truth

Karan Sikka; Thomas M. Deserno

doi:10.1117/12.844504

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23 February 2010 Comparison of algorithms for ultrasound image segmentation without ground truth

Karan Sikka; Thomas M. Deserno

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Proceedings Volume 7627, Medical Imaging 2010: Image Perception, Observer Performance, and Technology Assessment; 76271C (2010) https://doi.org/10.1117/12.844504
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

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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Karan Sikka and Thomas M. Deserno "Comparison of algorithms for ultrasound image segmentation without ground truth", Proc. SPIE 7627, Medical Imaging 2010: Image Perception, Observer Performance, and Technology Assessment, 76271C (23 February 2010); https://doi.org/10.1117/12.844504

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