Literature DB >> 21669471

Technique for preprocessing of digital mammogram.

Indra Kanta Maitra1, Sanjay Nag, Samir Kumar Bandyopadhyay.   

Abstract

Digital mammogram has emerged as the most popular screening technique for early detection of breast cancer and other abnormalities in human breast tissue. It provides us opportunities to develop algorithms for computer aided detection (CAD). In this paper we have proposed three distinct steps. The initial step involves contrast enhancement by using the contrast limited adaptive histogram equalization (CLAHE) technique. Then define the rectangle to isolate the pectoral muscle from the region of interest (ROI) and finally suppress the pectoral muscle using our proposed modified seeded region growing (SRG) algorithm. The proposed algorithms were extensively applied on all the 322 mammogram images in MIAS database resulting in complete pectoral muscle suppression in most of the images. Our proposed algorithm is compared with other segmentation methods showing superior results in comparison.
Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

Entities:  

Mesh:

Year:  2011        PMID: 21669471     DOI: 10.1016/j.cmpb.2011.05.007

Source DB:  PubMed          Journal:  Comput Methods Programs Biomed        ISSN: 0169-2607            Impact factor:   5.428


  11 in total

Review 1.  Review of recent advances in segmentation of the breast boundary and the pectoral muscle in mammograms.

Authors:  Mario Mustra; Mislav Grgic; Rangaraj M Rangayyan
Journal:  Med Biol Eng Comput       Date:  2015-11-06       Impact factor: 2.602

2.  Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM.

Authors:  Shubhi Sharma; Pritee Khanna
Journal:  J Digit Imaging       Date:  2014-07-09       Impact factor: 4.056

3.  Automatic Pectoral Muscle Region Segmentation in Mammograms Using Genetic Algorithm and Morphological Selection.

Authors:  Rongbo Shen; Kezhou Yan; Fen Xiao; Jia Chang; Cheng Jiang; Ke Zhou
Journal:  J Digit Imaging       Date:  2018-10       Impact factor: 4.056

4.  Computerized Analysis of Mammogram Images for Early Detection of Breast Cancer.

Authors:  Yassir Edrees Almalki; Toufique Ahmed Soomro; Muhammad Irfan; Sharifa Khalid Alduraibi; Ahmed Ali
Journal:  Healthcare (Basel)       Date:  2022-04-25

5.  Three-Class Mammogram Classification Based on Descriptive CNN Features.

Authors:  M Mohsin Jadoon; Qianni Zhang; Ihsan Ul Haq; Sharjeel Butt; Adeel Jadoon
Journal:  Biomed Res Int       Date:  2017-01-15       Impact factor: 3.411

6.  A New Breast Border Extraction and Contrast Enhancement Technique with Digital Mammogram Images for Improved Detection of Breast Cancer

Authors:  Manasi Hazarika; Lipi B Mahanta
Journal:  Asian Pac J Cancer Prev       Date:  2018-08-24

7.  An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the Radiomics Analysis Approach.

Authors:  Tariq Mahmood; Jianqiang Li; Yan Pei; Faheem Akhtar; Azhar Imran; Muhammad Yaqub
Journal:  Cancers (Basel)       Date:  2021-11-24       Impact factor: 6.639

8.  Deep learning-based breast region extraction of mammographic images combining pre-processing methods and semantic segmentation supported by Deeplab v3.

Authors:  Kuochen Zhou; Wei Li; Dazhe Zhao
Journal:  Technol Health Care       Date:  2022       Impact factor: 1.205

9.  Local Binary Patterns Descriptor Based on Sparse Curvelet Coefficients for False-Positive Reduction in Mammograms.

Authors:  Meenakshi M Pawar; Sanjay N Talbar; Akshay Dudhane
Journal:  J Healthc Eng       Date:  2018-09-25       Impact factor: 2.682

10.  Impact of Image Enhancement Module for Analysis of Mammogram Images for Diagnostics of Breast Cancer.

Authors:  Yassir Edrees Almalki; Toufique Ahmed Soomro; Muhammad Irfan; Sharifa Khalid Alduraibi; Ahmed Ali
Journal:  Sensors (Basel)       Date:  2022-02-26       Impact factor: 3.576

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.