Mammogram Breast Cancer Image Detection Using Image Processing Functions
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This article is published in Information Technology Journal.The article was published on 2007-02-01 and is currently open access. It has received 50 citations till now. The article focuses on the topics: Image processing & Breast cancer.read more
Citations
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Journal ArticleDOI
Brain MRI Image Classification for Cancer Detection Using Deep Wavelet Autoencoder-Based Deep Neural Network
Pradeep Kumar Mallick,Seuc-Ho Ryu,Sandeep Kumar Satapathy,Shruti Mishra,Gia Nhu Nguyen,Prayag Tiwari +5 more
TL;DR: A technique for image compression using a deep wavelet autoencoder (DWA), which blends the basic feature reduction property of autoen coder along with the image decomposition property of wavelet transform is proposed and it is noted that the proposed method outshines the existing methods.
Image Segmentation using Extended Edge Operator for Mammographic Images
H. B. Kekre,Saylee Gharge +1 more
TL;DR: Extended Sobel, Prewitt and Kirsch edge operators are proposed for image segmentation of mammographic images and their results are displayed.
Posted Content
Detection and Demarcation of Tumor using Vector Quantization in MRI images
TL;DR: This paper proposed segmentation using vector quantization technique using Linde Buzo-Gray algorithm (LBG) for segmentation of MRI images and displayed results of watershed segmentation and Entropy using Gray Level Co-occurrence Matrix along with this method.
Journal ArticleDOI
Medical imaging technique using curvelet transform and machine learning for the automated diagnosis of breast cancer from thermal image
R. Karthiga,K. Narasimhan +1 more
TL;DR: In the proposed method, 16 features are used for the automated classification of input thermal images, and the cubic SVM renders the highest accuracy of 93.3%.
Proceedings ArticleDOI
A wavelet based morphological mass detection and classification in mammograms
J. Anitha,J. Dinesh Peter +1 more
TL;DR: In this article, wavelet features are extracted from the detected mass regions and compared with feature extracted using Gray Level Co-occurrence Matrix (GLCM) to differentiate the TP and FP regions.
References
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Minimum spatial entropy threshold selection
TL;DR: A recent measure of spatial entropy, which attempts to include the spatial information inherent in the image, has been used here as a criterion function for threshold selection and indicates an improvement over methods that ignore this information.
Journal ArticleDOI
Entropic thresholding using a block source model
TL;DR: This new approach is based on a distribution-free local analysis of the image and does not use higher order entropy, and is compared to the existing entropic thresholding methods.