Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology.

Hongyu Wang,Qirong Bu,Jun Feng,Min Zhang,Feihong Liu,Yu Ren,Yi Lv

doi:10.1155/2018/4015613

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.

Highlights

Breast cancer is responsible for 23% of all cancer cases and 14% of cancer-related deaths amongst women worldwide [1]
207 images do not contain any lesions while other 50 images have masses. e spatial resolution of image in Mammographic Image Analysis Society (MIAS) is 50 μm × 50 μm, and grayscale intensity is quantized to 8 bits. e Digital Database for Screening Mammography (DDSM) data set contains 210 images, in which 130 images contain masses and the other ones are normal mammograms. e images of DDSM have been resized to 1024 × 1024 pixels, and grayscale intensity is quantized to 8 bits in accordance with images in MIAS
In both MIAS and DDSM data sets, the mammograms containing masses have been annotated by expert radiologists, which are used for reporting the detection performance in our experiments. e extreme learning machine (ELM) classification method divides visual patches into mass and nonmass candidates using 10-fold cross validation

Summary

Introduction

Breast cancer is responsible for 23% of all cancer cases and 14% of cancer-related deaths amongst women worldwide [1]. Proposed an automatic mass detection algorithm using the graph-based vision saliency (GBVS) map Most of these methods are derived from natural scene statistics while the characteristics of medical images are different. Inspired by the framework of Gestalt theory, we propose to apply visual rules to medical image analysis. We present an automatic mass detection framework based on Gestalt psychology It contains three modules: sensation integration, semantic integration, and validation. E proposed automatic mass detection method integrates human cognition properties and the visual characteristics of breast masses. Experimental results demonstrated that the proposed method has yielded better performance than other algorithms

Mass Detection Framework Inspired by Gestalt Psychology

Stage 1

Stage 2

Stage 3: Verification

Conclusion