Segmentation of blurry object by learning from examples

Xiaohui Yuan

doi:10.1117/12.843839

12 March 2010 Segmentation of blurry object by learning from examples

Xiaohui Yuan

Proceedings Volume 7623, Medical Imaging 2010: Image Processing; 76234G (2010) https://doi.org/10.1117/12.843839
Event: SPIE Medical Imaging, 2010, San Diego, California, United States

Abstract

Object with blurry boundary is a very common problem across image modalities and applications in medical field. Examples include skin lesion segmentation, tumor delineation in mammogram, tongue tracing in MR images, etc. To address blurry boundary problem, region-based active contour methods have been developed which utilize global image feature to address the problem of fuzzy edge. Image feature, such as texture, intensity histograms, or structure tensors, have also been studied for region-based models. On the other hand, trained domain experts have been much more effective in performing such tasks than computer algorithms that are based on a set of carefully selected, sophisticated image features. In this paper, we present a novel method that employs a learning strategy to guide active contour algorithm for delineating blurry objects in the imagery. Our method consists of two steps. First, using gold-standard examples, we derive statistical descriptions of the object boundary. Second, in the segmentation process, the statistical description is reinforced to achieve desired delineation. Experiments were conducted using both synthetic images and the skin lesion images. Our synthetic images were created with 2D Gaussian function, which closely resembles objects with blurry boundary. The robustness of our method with respect to the initialization is evaluated. Using different initial curves, similar results were achieved consistently. In experiments with skin lesion images, the outcome matches the contour in reference image, which are prepared by human experts. In summary, our experiments using both synthetic images and skin lesion images demonstrated great segmentation accuracy and robustness.

Citation Download Citation

Xiaohui Yuan "Segmentation of blurry object by learning from examples", Proc. SPIE 7623, Medical Imaging 2010: Image Processing, 76234G (12 March 2010); https://doi.org/10.1117/12.843839

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