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. 2018 May 2:2018:4015613.
doi: 10.1155/2018/4015613. eCollection 2018.

Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology

Hongyu Wang  1 , Jun Feng  1 , Qirong Bu  1 , Feihong Liu  1 , Min Zhang  2 , Yu Ren  3 , Yi Lv  4
Affiliations

Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology

Hongyu Wang et al. J Healthc Eng. .

Abstract

Inspired by gestalt psychology, we combine human cognitive characteristics with knowledge of radiologists in medical image analysis. In this paper, a novel framework is proposed to detect breast masses in digitized mammograms. It can be divided into three modules: sensation integration, semantic integration, and verification. After analyzing the progress of radiologist's mammography screening, a series of visual rules based on the morphological characteristics of breast masses are presented and quantified by mathematical methods. The framework can be seen as an effective trade-off between bottom-up sensation and top-down recognition methods. This is a new exploratory method for the automatic detection of lesions. The experiments are performed on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) data sets. The sensitivity reached to 92% at 1.94 false positive per image (FPI) on MIAS and 93.84% at 2.21 FPI on DDSM. Our framework has achieved a better performance compared with other algorithms.

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Figures

Figure 1
Figure 1
Sample images from MIAS data set [17].
Figure 2
Figure 2
The clinical diagnosis of breast mass by the radiologist.
Figure 3
Figure 3
The framework of the proposed approach. It can be divided into three stages including sensation integration, semantic integration, and verification. Visual rules used in the framework are modeled and indicated with the labels A, B, C, D, and E.
Figure 4
Figure 4
Visual patches based on Gestalt psychology.
Figure 5
Figure 5
The densification of different patches. Visual patches of (a) mass and normal tissue.
Figure 6
Figure 6
Visual patches with the glandular.
Figure 7
Figure 7
The statistical histogram of homogeneity of negative and positive visual patches.
Figure 8
Figure 8
Sample results of the saliency algorithms. Green denotes the saliency region detected by these algorithms, pink represents the ground truth region containing mass, and white denotes the crossing region between green and pink. (a) Agrawal et al. [16], (b) Achanta and Süsstrunk [55], (c) Murray et al. [56], and (d) the three stages of our method. Stage 1: the fifth column is the detection result of sensation integration. Stage 2: the sixth column is the detection result of semantic integration. Stage 3: the last column is the final detection result (verification) of our method.
Figure 9
Figure 9
The number and percentage of patches/ROIs are counted for each step of our method: (a) plotted on the MIAS data set and (b) plotted on the DDSM data set.
Figure 10
Figure 10
FROC curves of the proposed method on MIAS and DDSM data sets.
See this image and copyright information in PMC

References

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