Efficient Small Blob Detection Based on Local Convexity, Intensity and Shape Information.
Efficient Small Blob Detection Based on Local Convexity, Intensity and Shape Information.
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DOI：
10.1109/tmi.2015.2509463
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发表时间：
2016-04
影响因子：
10.6
通讯作者：
Bennett KM
中科院分区：
文献类型：
--
作者：
Zhang M;Wu T;Beeman SC;Cullen-McEwen L;Bertram JF;Charlton JR;Baldelomar E;Bennett KM
The identification of small structures (blobs) from medical images to quantify clinically relevant features, such as size and shape, is important in many medical applications. One particular application explored here is the auto mated detection of kidney glomeruli after targeted contrast enhancement and magnetic resonance imaging. We propose a computationally efficient algorithm, termed the Hessian-based difference of Gaussians (H0oG), to segment small blobs (e.g. glomeruli from kidney) from 30 medical images based on local convexity, intensity and shape information. The image is first smoothed and pre-segmented into small blob candidate regions based on local convexity. Two novel 30 regional features (regional blobness and regional flatness) are then extracted from the candidate regions. Together with regional intensity, the three features are used in an unsupervised learning algorithm for auto post-pruning. H0oG is first validated in a 20 form and compared with other three blob detectors from literature, which are generally for 20 images only. To test the detectability of blobs from 30 images, 240 sets of simulated images are rendered for scenarios mimicking the renal nephron distribution observed in contrast-enhanced, 30 MRI. The results show a satisfactory performance of H0oG in detecting large numbers of small blobs. Two sets of real kidney 30 MR images (6 rats, 3 human) are then used to validate the applicability of H0oG for glomeruli detection. By comparing MRI to stereological measurements, we verify that H0oG is a robust and efficient unsupervised technique for 30 blobs segmentation.
