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Proceedings Paper

Efficient adaptive thresholding with image masks
Author(s): Young-Taek Oh; Youngkyoo Hwang; Jung-Bae Kim; Won-Chul Bang
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Paper Abstract

Adaptive thresholding is a useful technique for document analysis. In medical image processing, it is also helpful for segmenting structures, such as diaphragms or blood vessels. This technique sets a threshold using local information around a pixel, then binarizes the pixel according to the value. Although this technique is robust to changes in illumination, it takes a significant amount of time to compute thresholds because it requires adding all of the neighboring pixels. Integral images can alleviate this overhead; however, medical images, such as ultrasound, often come with image masks, and ordinary algorithms often cause artifacts. The main problem is that the shape of the summing area is not rectangular near the boundaries of the image mask. For example, the threshold at the boundary of the mask is incorrect because pixels on the mask image are also counted. Our key idea to cope with this problem is computing the integral image for the image mask to count the valid number of pixels. Our method is implemented on a GPU using CUDA, and experimental results show that our algorithm is 164 times faster than a naïve CPU algorithm for averaging.

Paper Details

Date Published: 7 March 2014
PDF: 6 pages
Proc. SPIE 9024, Image Processing: Machine Vision Applications VII, 90240A (7 March 2014); doi: 10.1117/12.2036178
Show Author Affiliations
Young-Taek Oh, Samsung Advanced Institute of Technology (Korea, Republic of)
Youngkyoo Hwang, Samsung Advanced Institute of Technology (Korea, Republic of)
Jung-Bae Kim, Samsung Advanced Institute of Technology (Korea, Republic of)
Won-Chul Bang, Samsung Advanced Institute of Technology (Korea, Republic of)

Published in SPIE Proceedings Vol. 9024:
Image Processing: Machine Vision Applications VII
Kurt S. Niel; Philip R. Bingham, Editor(s)

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