Efficient Small Blob Detection Based on Local Convexity, Intensity and Shape Information

PROJECT TITLE :

Efficient Small Blob Detection Based on Local Convexity, Intensity and Shape Information

ABSTRACT:

The identification of little structures (blobs) from medical pictures to quantify clinically relevant features, like size and form, is very important in several medical applications. One specific application explored here is the automated detection of kidney glomeruli when targeted distinction enhancement and magnetic resonance imaging. We have a tendency to propose a computationally economical algorithm, termed the Hessian-based Difference of Gaussians (HDoG), to section little blobs (e.g. glomeruli from kidney) from 3D medical pictures primarily based on local convexity, intensity and form data. The image is initial smoothed and pre-segmented into tiny blob candidate regions based mostly on local convexity. 2 novel 3D regional features (regional blobness and regional flatness) are then extracted from the candidate regions. At the side of regional intensity, the 3 features are utilized in an unsupervised learning algorithm for auto post-pruning. HDoG is 1st validated during a 2D type and compared with alternative three blob detectors from literature, that are generally for 2D images only. To check the detectability of blobs from 3D pictures, 240 sets of simulated images are rendered for eventualities mimicking the renal nephron distribution observed in contrast-enhanced, 3D MRI. The results show a satisfactory performance of HDoG in detecting massive numbers of small blobs. Two sets of real kidney 3D MR pictures (half dozen rats, 3 human) are then used to validate the applicability of HDoG for glomeruli detection. By comparing MRI to stereological measurements, we tend to verify that HDoG could be a strong and efficient unsupervised technique for 3D blobs segmentation.

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