Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology

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

doi:10.1155/2018/4015613

Cited by 32 publications

(15 citation statements)

References 58 publications

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“…In addition, deep learning, which is based on learning data representations and convolutional neural network, has shown its higher performance and has been effectively applied to breast mass detection and classification [26] [27]. However, [28] mentioned that these methods work well on large data sets but exhibit certain limitations on small data sets. They propose a new exploratory method for the automatic detection of lesion based on gestalt psychology, which combines human cognitive characteristics and radiologist's knowledge.…”

Section: Related Workmentioning

confidence: 99%

IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

Belkhodja¹,

Hamdadou²

2019

IJIMAI

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Computer aided systems for detection and diagnosis on mammograms are one of the automatic solutions that help the radiologist in detecting abnormalities in an efficient way as a second reader of digital mammograms. To this end, we propose, in this paper, a methodology for computeraided detection of breast masses on screening mammograms, which joins multidisciplinary axes such as medical domain, image processing and biological pattern recognition. For this, we focus on minimizing false positive findings and increasing true positive cases using all benefits of fuzzy processing and artificial immune recognition system.

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Section: Related Workmentioning

confidence: 99%

IMCAD: Computer Aided System for Breast Masses Detection based on Immune Recognition

Belkhodja¹,

Hamdadou²

2019

IJIMAI

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“…Wang H. et al [10] proposed a framework to detect breast masses in digitized mammograms. In their research, the authors analyzed the progress of radiologist's mammography screening, then a series of visual rules based on the morphological characteristics of breast masses are presented and quantified by mathematical methods.…”

Section: Introductionmentioning

confidence: 99%

Detection of partially overlapped masses in mammograms

Alhabib

2020

IJEECS

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<span>Breast cancer remains one of the major causes of cancer deaths among women. For decades, screening mammography has been one of the most common methods for early cancer detection and diagnosis. Digital mammography images are created by applying a small burst of x-rays that pass through the breast to a solid-state detector, which transmits the electronic signals to a computer to form a digital image. However, due to projection, some mass areas may be partially covered, which makes them difficult to be interprated. This paper addresses the issue of potential mass regions being distorted by other normal breast tissues, which will negatively affect some of the features being extracted from the mass and in turn deteriorate the classification accuracy. The goal was to estimate the overlapped parts of the mass border using Euclidean distance in order to give more accurate results in next stages. The presented method achieved 95.744% region sensitivity at 0.333 False Positive per Image (FPI), outperforming other researches in this branch of mammography analysis.</span>

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“…In the work of Wang et al [11], a framework for automatic detection of breast masses was presented, based on gestalt psychology. It combines human cognitive characteristics and knowledge of radiologists in medical image analysis.…”

Section: Introductionmentioning

confidence: 99%