» Articles » PMID: 20071209

A Review of Automatic Mass Detection and Segmentation in Mammographic Images

Overview
Journal Med Image Anal
Publisher Elsevier
Specialty Radiology
Date 2010 Jan 15
PMID 20071209
Citations 57
Authors
Affiliations
Soon will be listed here.
Abstract

The aim of this paper is to review existing approaches to the automatic detection and segmentation of masses in mammographic images, highlighting the key-points and main differences between the used strategies. The key objective is to point out the advantages and disadvantages of the various approaches. In contrast with other reviews which only describe and compare different approaches qualitatively, this review also provides a quantitative comparison. The performance of seven mass detection methods is compared using two different mammographic databases: a public digitised database and a local full-field digital database. The results are given in terms of Receiver Operating Characteristic (ROC) and Free-response Receiver Operating Characteristic (FROC) analysis.

Citing Articles

Development of an Automated CAD System for Lesion Detection in DCE-MRI.

Andreadis T, Chouchos K, Courcoutsakis N, Seimenis I, Koulouriotis D J Imaging Inform Med. 2025; .

PMID: 39979761 DOI: 10.1007/s10278-025-01445-2.


CT radiomics to predict pathologic complete response after neoadjuvant immunotherapy plus chemoradiotherapy in locally advanced esophageal squamous cell carcinoma.

Shi L, Li C, Bai Y, Cao Y, Zhao S, Chen X Eur Radiol. 2024; 35(3):1594-1604.

PMID: 39470794 DOI: 10.1007/s00330-024-11141-4.


An Artificial Intelligence-Based Tool for Enhancing Pectoral Muscle Segmentation in Mammograms: Addressing Class Imbalance and Validation Challenges in Automated Breast Cancer Diagnosis.

Cortes-Rojas F, Hernandez-Rodriguez Y, Bayareh-Mancilla R, Cigarroa-Mayorga O Diagnostics (Basel). 2024; 14(19).

PMID: 39410548 PMC: 11475286. DOI: 10.3390/diagnostics14192144.


Comparative analysis of features and classification techniques in breast cancer detection for Biglycan biomarker images.

Matouq J, Alnuman N Cancer Biomark. 2024; 40(3-4):263-273.

PMID: 39177590 PMC: 11380270. DOI: 10.3233/CBM-230544.


Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy.

Abdoli N, Zhang K, Gilley P, Chen X, Sadri Y, Thai T Bioengineering (Basel). 2023; 10(11).

PMID: 38002458 PMC: 10669238. DOI: 10.3390/bioengineering10111334.