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Utilizing digital breast tomosynthesis projection views correlation for microcalcification enhancement for detection purposes

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2012 . 11 ~ current

 

 

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1.  Introduction

 

         In this work, we devise a novel method to enhance the microcalcifications (MCs) in the digital breast tomosynthesis (DBT) projection views (PVs) for MC cluster detection purposes.

         We propose utilizing the correlation between the PVs such that: 

1.    The contrast of MCs is improved.

2.    Image noise due to the low X-ray exposure in DBT is minimized.

3.    The false positive (FP) MC cluster detection is reduced.

 

2.  Proposed microcalcification multi-shift enhancement

 

2.1. Overview

 

Figure 1: The correlation between stacked PVs (enhanced PVs).

(a) The observed shift in MCs across the PVs. (b) Aligned PVs according to the shift

 

         Figure 1 shows a stack of enhanced PVs, in which the correlation between the PVs is apparent. From the PVs, we can have two main observations:

1.    The breast region shifts along the tube motion direction in each PV.

2.    The small bright shape of MCs appears shifted in each PV with a constant shift based on their depth in the breast.

 

         A multi-shift enhancement is devised to utilize those shifts in order to improve the MC contrast and reduce the PVs noise. The enhancement is performed by two enhancement steps described below:

 

2.2. Coarse Shift Enhancement

         The coarse shift is the shit in the breast region between the PVs

         Estimated by block-matching at N critical points.

         The coarse shift (dcoarse) is the average of all critical points shifts.

         The standard deviation (coarse) of all critical points shift is calculated.

 

2.3. Fine Multi-shift Enhancement

         To enhance the MCs at the i-th PV:

         All PVs are shifted with respect to the i-th PV within [ -coarse ,+ coarse]

         Average pooling of the PVs at each shift to suppress random noise.

         2D Laplacian of Gaussian filtering to improve MC like particles.

         Obtain the enhanced i-th PV by maximum pooling across the PVs.

 

 

3.   Microcalcification cluster detection and false positive reduction

 

         MC clusters are detected via iterative thresholding on the enhanced PVs followed by clustering.

         FPs are rejected and missing ROIs are compensated as shown in figure 2.

         Finally, to determine the malignancy of the MC cluster, features are extracted from each ROI and in the PVs, and classification fusion is utilized.

 

Figure 2: FP reduction and missing ROI compensation

 

 

4.  Experiments

 

4.1 Dataset

 

To evaluate this work, a dataset consisting of 46 PV sets of both MLO and CC views collected form 23 patients. Each PV set consists of 15 PV images and has the fixed pixel size of 140 µm 140 µm. From the provided PV sets, 20 had a biopsy proven malignant MC cluster.

 

4.2 Experimental results

 

 

Box-Rim Filter

Difference of Gaussian (DoG)

Proposed Multi-shift enhancement

Figure 3: Example of different enhancements results, and the average contrast to noise ratio around the true MCs (Average MC CNR)

 

 

To demonstrate the effectiveness of the proposed PV enhancement technique, we show a comparison with the recently proposed methods in recent literature. As clearly shown in figure 3, The proposed PV enhancement technique improves the contrast of the MCs while it suppresses the noise and the breast tissue. This results in an improvement in the detectability of the MCs. Moreover, to quantitatively evaluate the MC contrast enhancement achieved by the proposed PV enhancement method, CNR was calculated around the MCs and the average of the MC CNR values is shown in table 1.

 

 

Table 1:  Comparison of average MC Cluster CNR values

Approach

Box-Rim Filter

Difference of

Gaussian (DoG)

Proposed Multi-shift

enhancement

Average MC CNR

1.648

1.195

2.027

 

 

Finally, we show, in figure 4, an FROC curve representing the detection sensitivity of the proposed method. The results demonstrated that the proposed approach increases contrast of MCs and improves the detection sensitivity with a low FP rate.

 

MATLAB Handle Graphics

Figure 4: Detection performance in terms of the FROC curve

 

 

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Contact Person: Prof. Yong Man Ro (ymro@kaist.ac.kr)

 

 

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1.     Baddar, Wissam J., et al. "Utilizing digital breast tomosynthesis projection views correlation for microcalcification enhancement for detection purposes." SPIE Medical Imaging. International Society for Optics and Photonics, 2015.