Automated Seed-Based Region Growing Using The Moving K-Means Clustering For The Detection Of Mammographic Microcalcifications.

QR Code

Automated Seed-Based Region Growing Using The Moving K-Means Clustering For The Detection Of Mammographic Microcalcifications.

Mammography is by far the proven method of early detection of breast cancer. However, mammography is not without its problems. It is amongst the most difficult of radiological images to interpret as the images are of low contrast and features indicative of abnormalities are very subtle and minute....

Full description

Bibliographic Details
Main Authors:	Ngah, Umi Kalthum, Mat Isa, N A, Mohd Noor, Masriah
Format:	Conference or Workshop Item
Language:	English
Published:	2003
Subjects:	TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Online Access:	http://eprints.usm.my/14166/ http://eprints.usm.my/14166/1/automated.pdf

Similar Items

Mammographic Image Contrast Enhancement Through The Use Of Moving Contrast Sweep.
by: Mohd Nordin, Zailani, et al.
Published: (2008)

Region and boundary segmentation of microcalcifications using seed-based region growing and mathematical morphology
by: Abdul Malek, Aminah, et al.
Published: (2010)

Methods For Clustered Microcalcifications Detection In Digital Mammograms
by: Wan Zaki, Wan Mimi Diyana
Published: (2005)

Methods for Clustered Microcalcifications Detection in Digital Mammograms
by: Wan Mimi Diyana Wan Zaki,
Published: (2005)

Investigation on The Usefulness of Bioinformatics Application Techniques For Arabic Character Recognition
by: Ngah, Umi Kalthum
Published: (2013)

Enhanced Block-Based Copy-Move Image Forgery Detection Using K-Means Clustering Technique
by: Mohammed Abdo Al-Qershi, Osamah
Published: (2018)

Automated segmentation of brain MR images by combining Contourlet Transform and K-means Clustering techniques
by: Arshad, Javed, et al.
Published: (2013)

Cluster optimization in VANET using MFO algorithm and K-Means clustering
by: Ramlee, Sham Rizal, et al.
Published: (2023)

CAD Automation Module Based On Cell Moving Algorithm For Incremental Placement Timing Optimization
by: Kan, Mei War
Published: (2012)

Clustering of rainfall data using k-means algorithm
by: Mohd Sham, Mohamad, et al.
Published: (2019)

Breast Ultrasound Automated ROI Segmentation with Region Growing
by: Lee, Lay-Khoon, et al.
Published: (2015)

Detection of Masses and Microcalcifications in Digital Mammogram Images
by: Langarizadesh, Mostafa
Published: (2011)

Intrusion detection systems using K-means clustering system
by: Nor Dzuhairah Hani, Jamaludin
Published: (2019)

Analysis of K-Mean and X-Mean Clustering Algorithms Using Ontology-Based Dataset Filtering
by: Rahmah, Mokhtar, et al.
Published: (2021)

Max-D clustering K-means algorithm for Autogeneration of Centroids and Distance of Data Points Cluster
by: Wan Maseri, Wan Mohd, et al.

Microcalcifications segmentation using three edge detection techniques
by: Yasiran, Siti Salmah, et al.
Published: (2012)

Segmentation of CT Brain Images Using K-Means and EM Clustering
by: Lee, Tong Hau, et al.
Published: (2008)

Identification of typical load profiles using K-means clustering algorithm
by: Azad, S., et al.
Published: (2014)

Clustering techniques for human posture recognition: K-Means, FCM and SOM
by: Kiran, Maleeha, et al.
Published: (2009)

K-means clustering to improve the accuracy of decision tree response classification.
by: Ali, S. A., et al.
Published: (2009)

Intrusion detection based on k-means clustering and OneR classification
by: Muda, Zaiton, et al.
Published: (2011)

The performance of k-means clustering method based on robust principal components
by: Kadom, Ahmed, et al.
Published: (2018)

New approaches to normalization techniques to enhance k-means clustering algorithm
by: Dalatu, Paul Inuwa, et al.
Published: (2020)

Improved normalization and standardization techniques for higher purity in K-means clustering
by: Dalatu, Paul Inuwa, et al.
Published: (2016)

Intrusion detection based on K-means clustering and Naïve Bayes classification
by: Muda, Zaiton, et al.
Published: (2011)

Pseudocolouring Enhancement Processing Of Ovarian Ultrasound Images.
by: Saidin, Nafiza, et al.
Published: (2003)

Ovaidan Ultrasound Image Enhancement By Pseudocolouring.
by: Saidin, Nafiza, et al.
Published: (2004)

An evolutionary harmony search algorithm with dominant point detection for recognition-based segmentation of online Arabic text recognition
by: Moayad, Yousif Potrus, et al.
Published: (2014)

The relationship of mammographic density and age : implication for screening mammogram
by: Muhammed Nordin, Ellyda, et al.
Published: (2012)

Mammographic density and breast cancer risk in Asian women
by: Mariapun, Shivaani
Published: (2024)

Characterizing protection ability of blue blocking lenses using k-means clustering
by: Che Azemin, Mohd Zulfaezal, et al.
Published: (2020)

Characterizing protection ability of blue blocking lenses using K-means clustering
by: Che Azemin, Mohd Zulfaezal, et al.
Published: (2020)

Forecasting Using K-means Clustering and RNN Methods with PCA Feature Selection
by: Ferna, Marestiani, et al.
Published: (2022)

Comparison of expectation maximization and K-means clustering algorithms with ensemble classifier model
by: Sulaiman, Md. Nasir, et al.
Published: (2018)

Clustering for binary data sets by using genetic algorithm-incremental K-means
by: Saharan, S., et al.
Published: (2018)

Comparison between GVF snake and ED snake in segmenting microcalcifications
by: Yasiran, Siti Salmah, et al.
Published: (2011)

Fabrication And Characterization Of Microcalcification Breast Phantom For Image Quality Analysis
by: Oyiwoja, Okoh Franca
Published: (2022)

MaxD K-Means: A clustering algorithm for auto-generation of centroids and distance of data points in clusters
by: Wan Maseri, Wan Mohd, et al.
Published: (2012)

Computer Aided Detection of Breast Density and Mass, and Visualization of Other Breast Anatomical Regions on Mammograms Using Graph Cuts
by: Saidin, Nafiza, et al.
Published: (2013)

Segmentation Of Stretched Pap Smear Cytology Images Using Clustering Algorithm.
by: Ghafar, R, et al.
Published: (2003)