A Microcalcification Detection Using Adaptive Contrast Enhancement on Wavelet Transform and Neural Network

  title={A Microcalcification Detection Using Adaptive Contrast Enhancement on Wavelet Transform and Neural Network},
  author={Ho Kyung Kang and Yong Man Ro and Sung-Min Kim},
  journal={IEICE Trans. Inf. Syst.},
Microcalcification detection is an important part of early breast cancer detection. In this paper, we propose a microcalcification detection algorithm using adaptive contrast enhancement in a mammography CAD (computer-aided diagnosis) system. The proposed microcalcification detection algorithm includes two parts. One is adaptive contrast enhancement in which the enhancement filtering parameters are determined based on noise characteristics of the mammogram. The other is a multi-stage… 

A Microcalcification Detection Using Multi-Layer Support Vector Machine in Korean Digital Mammogram

Experimental result showed that the proposed microcalcification detection method using multi-layer support vector machine (SVM) classifiers to determine whether microCalcifications are malignant or benign tumors would outperform conventional method using ANN (artificial neural networks).

Microcalcification detection system in digital mammogram using two-layer SVM

A microcalcification detection system which consists of three modules; coarse detection, clustering, and fine detection module is proposed which is compared with full-field digital mammogram (FFDM) and with an ANN-based detection system.

Comparing the Performance of Image Enhancement Methods to Detect Microcalcification Clusters in Digital Mammography

Experimental results strongly suggest that the wavelet transformation can be more effective and improve significantly overall detection of the Computer-Aided Diagnosis (CAD) system especially for dense breast.

Self organizing map neural network with fuzzy screening for micro-calcifications detection on mammograms

  • C. TiuT. JongC. Hsieh
  • Computer Science
    2008 IEEE Conference on Soft Computing in Industrial Applications
  • 2008
The survey revealed the micro-calcification regions had a good clustering property in self-organizing map neural network index, which was adopted to classify the regions with similar characteristics.

Detection of masses and microcalcifications in digital mammogram images using fuzzy logic

The fuzzy system is a promising technique for early detection of breast cancer and has an acceptable sensitivity of 85.6% and specificity of 90.7%.

Detecting microcalcification clusters in digital mammograms: Study for inclusion into computer aided diagnostic prompting system

This work presents two concurrent methods for MCC detection, and studies their possible inclusion to a computer aided diagnostic prompting system.

Contourlet based mammographic image enhancement

A method aimed at minimizing image noise while optimizing contrast of mammographic image features is presented in this paper, for more accurate detection of microcalcification clusters.

An Efficient Way to Enhance Mammogram Image in Transformation Domain

An efficient technique to enhance the mammogram image using various transforms, which cannot capture the geometric information of images and tend to amplify noises when they are applied to noisy images since they cannot distinguish noises from weak edges is proposed.

Improvement of digital mammogram images using histogram equalization, histogram stretching and median filter

Enhancing techniques, i.e. histogram equalization, histogram stretching and median filters, were used to provide better visualization for radiologists in order to help early detection of breast abnormalities.

Mammograms Enhancement and Denoising Using Generalized Gaussian Mixture Model in Nonsubsampled Contourlet Transform

A novel algorithm for mammographic image enhancement and denoising based on Multiscale Geometric Analysis (MGA) is proposed, which outperforms the spatial filters and other methods based on wavelets in terms of mass and microcalcification denoised and enhancement.



Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcifications

A computationally efficient mixed feature based neural network (MFNN) is proposed for the detection of microcalcification clusters (MCCs) in digitized mammograms. The MFNN employs features computed

A CAD System for the Automatic Detection of Clustered Microcalcification in Digitized Mammogram Films

  • S. YuL. Guan
  • Computer Science
    IEEE Trans. Medical Imaging
  • 2000
A computer-aided diagnosis (CAD) system for the automatic detection of clustered microcalcifications in digitized mammograms gives quite satisfactory detection performance.

Optimizing wavelet transform based on supervised learning for detection of microcalcifications in digital mammograms

A novel technique for optimizing the wavelet transform to enhance and detect microcalcifications in mammograms was developed based on the supervised learning method, which outperforms the authors' current scheme based on a conventional wavelets transform.

Wavelet transforms for detecting microcalcifications in mammograms

A 2-stage method based on wavelet transforms for detecting and segmenting calcifications designed to overcome the limitations of the simplistic Gaussian assumption and provides an accurate segmentation of calcification boundaries is developed.

Wavelets for contrast enhancement of digital mammography

Improvements in image contrast for multiscale imageprocessing algorithms were superior to those obtained using existing competitive algorithms and suggest that wavelet based image processing algorithms could play an important role in improving the imaging performance of digital mammography.

Detection of spicules on mammogram based on skeleton analysis

A new image processing method for the detection of spicules on mammogram using line skeletons and a modified Hough transform is proposed and results shows the effectiveness of the proposed method.

Enhancement of the Contrast in Mammographic Images Using the Homomorphic Filter Method

Experimental results show that the homomorphic filtering method improves the contrast in breast tumor images such that the contrast improvement index is increased by two fold compared to the conventional wavelet-based enhancement technique.

A wavelet-based spatially adaptive method for mammographic contrast enhancement.

Results suggest that the proposed method offers significantly improved performance over conventional and previously reported global wavelet contrast enhancement methods.