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

Abstract

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 automated 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 (HDoG), to segment small blobs (e.g., glomeruli from kidney) from 3D 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 3D 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. HDoG is first validated in a 2D form and compared with other three blob detectors from literature, which are generally for 2D images only. To test the detectability of blobs from 3D images, 240 sets of simulated images are rendered for scenarios mimicking the renal nephron distribution observed in contrast-enhanced, 3D MRI. The results show a satisfactory performance of HDoG in detecting large numbers of small blobs. Two sets of real kidney 3D MR images (6 rats, 3 human) are then used to validate the applicability of HDoG for glomeruli detection. By comparing MRI to stereological measurements, we verify that HDoG is a robust and efficient unsupervised technique for 3D blobs segmentation.

Fig. 1.

Slice 100 (of 256) from Simulated 3D Blob Images with Different Parameter Settings ranging from least dense to the densest scenario: (A) 3D Blob Image with o=1 and N=1,000. (B) 3D Blob Image with o=6 and N=1,000. (C) 3D Blob Image with o=1 and N=96,000. (D) 3D Blob Image with o=6 and N=96,000.

Fig. 2.

Contour Plot of F-Scores on Simulated 3D Images with Different Parameter Settings. In terms of blob size and quantity, the yellow shadow area A shows the scenarios of glomerulus detection on rat kidney images, and yellow shadow area B shows scenarios for glomerulus detection in human kidney images in our cases (based on the estimation of image size 256 × 256 × 256)

Fig. 3.

Glomerular segmentation results from 3D MR images of rat kidneys (selected slices presented). (A-C) Slice 100 for rats CF1, CF2, and CF3. (D- F) Slice 150 for rats CF4, CF5, and CF6. (G-I) segmentation results for (A- C), respectively. Identified glomeruli are contoured in green. (J-L) segmentation results for (D-F), respectively, where identified glomeruli are contoured in green. (M) is the zoomed-in region from (A) while (N) is the segmentation result of (M).

Fig. 4.

Glomerular segmentation results for 3D MR images of human kidneys (selected slices): (A-C) Original slice 100 for human CF1, CF2, and CF3 kidneys. (D-F) Slice 500 for human CF1, CF2, CF3 kidneys. (G-I) Identification results for (A-C), respectively, where identified glomeruli are contoured in green. (J-L) Identification results for (D-F), respectively, where identified glomeruli are contoured in green. (M) is the zoomed-in region from (D) while (N) is the segmentation result of (M).

Fig. 5.

Intensity frequency histograms of glomeruli against whole kidney image from: (A) Human CF1 (B) Human CF2 kidney (C) Human CF3. Frequency range was [0, 0.6] and the intensity range was [0, 1] in the figure. Vertical lines indicate the modes of the intensity distribution.

Fig. 6.

Frequency histograms of average intensity, regional blobness and regional flatness for human CF1, CF2 and CF3 kidney 3D MR images. (A)-(C) True glomerular cluster frequency histograms for human CF1, CF2 and CF3 respectively. (D)- (F) Non-glomerular cluster frequency histograms for human CF1, CF2 and CF3 respectively. Frequency range was [0, 0.5] and the x-axis range was [0, 1] in the Figure.

Fig. 7.

Glomerular segmentation results for 3D MR images of Rat CF1 kidney and Human CF1 Kidney (part of the slice on Fig.5): (A) Part of Slice for Rat CF1. (B) Identification results for (A), where identified glomeruli centers are marked in red-cross. (C) Part of Slice for human CF1. (D) Identification results for (C), where identified glomeruli centers are marked in red-cross. Circles show the error of missed detection while the rectangles show the error of false positive detection