Abstract

In recent years, computer vision technology has been widely used in the field of medical image processing. However, there is still a big gap between the existing breast mass detection methods and the real-world application due to the limited detection accuracy. It is known that humans locate the regions of interest quickly and further identify whether these regions are the targets we found. In breast cancer diagnosis, we locate all the potential regions of breast mass by glancing at the mammographic image from top to bottom and from left to right, then further identify whether these regions are a breast mass. Inspired by the process of human detection of breast mass, we proposed a novel breast mass detection method to detect breast mass on a mammographic image by stimulating the process of human detection. The proposed method preprocesses the mammographic image via the mathematical morphology method and locates the suspected regions of breast mass by the image template matching method. Then, it obtains the regions of breast mass by classifying these suspected regions into breast mass and background categories using a convolutional neural network (CNN). The bounding box of breast mass obtained by the mathematical morphology method and image template matching method are roughly due to the mathematical morphology method, which transforms all of the brighter regions into approximate circular areas. For regression of a breast mass bounding box, the optimal solution should be searched in the feasible region and the Particle Swarm Optimization (PSO) is suitable for solving the problem of searching the optimal solution within a certain range. Therefore, we refine the bounding box of breast mass by the PSO algorithm. The proposed breast mass detection method and the compared detection methods were evaluated on the open database Digital Database for Screening Mammography (DDSM). The experimental results demonstrate that the proposed method is superior to all of the compared detection methods in detection performance.

Highlights

  • Licensee MDPI, Basel, Switzerland.Breast cancer has the highest mortality rate among all cancers, it greatly threatens the lives of women globally [1]

  • The mathematical morphology method can highlight the region of interest in the image and guides people to focus on these regions, which are the objects we want to find with a high probability in the object detection task

  • The method proposed in this paper contains four phases: the mammographic image processing based on the mathematical morphology method, the suspected regions of breast mass generation, the suspected region of breast mass identification and the bounding box of breast mass regression

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Summary

Introduction

The mathematical morphology method can highlight the region of interest in the image and guides people to focus on these regions, which are the objects we want to find with a high probability in the object detection task Matching these regions using the image template matching method and identifying whether they are breast mass by a classifier. We locate the suspected regions of breast mass in the processed mammographic image using the image template matching method. These matched regions will be further identified as breast mass or background by the following works. We realized the detection task for breast mass and obtain a better detection performance in the mammographic image via the mathematical morphology method, image template matching method and a classification network.

Related Work
The Proposed Method
Processing of Mammographic Images by the Mathematical Morphology Method
The Generation Model for Candidate Region of Breast Mass
Candidate Regions Identification Model
Regression Model for the Location and Bounding Box of Breast Mass
Calculate the fitness by DB-CNN
Experiments
Dataset and Experimental Setting
Accuracy Comparison and Analysis
Method
Findings
Conclusions
Full Text
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