Journal Article10.1142/S0218001493000704
Mammogram screening using multiresolution-based image segmentation
D. Brzakovic,M. Neskovic +1 more
57
TL;DR: In this article, a hierarchical region growing (HRG) method is proposed to detect cancerous changes in mammograms and can potentially aid medical experts in establishing the diagnosis of malignant nodules.
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Abstract: This paper describes the design, implementation, and testing of an adaptive digital image segmentation method that detects cancerous changes in mammograms and can potentially aid medical experts in establishing the diagnosis. The essence of the method is hierarchical region growing that uses pyramidal multiresolution image representation. The relationships between pixels at different resolution levels are established using a fuzzy membership function, thus enabling detection of very small and/or low contrast objects in a highly textured background. The selection of the parameters of the fuzzy membership function allows for fine-tuning the method to specific segmentation objectives. This paper discusses two versions of the method: the first is aimed at the detection of microcalcifications and the second at the detection of benign and malignant nodules. The two versions are fully automated and differ in the procedure applied to automatically select the appropriate parameters of the fuzzy membership function. Both versions were evaluated in two ways: (i) using synthetically generated objects superimposed on normal mammograms and (ii) using mammogram images for which the corresponding truth images were generated by human experts. The objective of the first evaluation was to precisely determine the method’s capabilities and its sensitivity to object size, shape, and contrast. The objective of the second evaluation was to establish the method’s usefulness in helping medical experts to establish the diagnosis.
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Citations
•Proceedings Article
Application of data mining techniques for medical image classification
Maria-Luiza Antonie,Osmar R. Zaïane,Alexandru Coman +2 more
- 26 Aug 2001
TL;DR: This paper investigates the use of different data mining techniques, neural networks and association rule mining, for anomaly detection and classification, and shows that the two approaches performed well, obtaining a classification accuracy reaching over 70% percent for both techniques.
370
Detection of breast masses in mammograms by density slicing and texture flow-field analysis
TL;DR: A method for the detection of masses in mammographic images that employs Gaussian smoothing and subsampling operations as preprocessing steps and methods for analyzing oriented flow-like textural information in mammograms are proposed.
270
A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques
Brijesh Verma,J. Zakos +1 more
- 01 Mar 2001
TL;DR: An easy-to-use intelligent system that gives the user options to diagnose, detect, enlarge, zoom and measure distances of areas in digital mammograms and finds that a combination of three features is the best combination to distinguish a benign microcalcification pattern from one that is malignant.
Computer aided diagnosis of breast cancer in digitized mammograms.
TL;DR: A computer aided neural network classification of regions of suspicion (ROS) on digitized mammograms is presented in this paper which employs features extracted by a new technique based on independent component analysis.
151
Fast detection of masses in computer-aided mammography
TL;DR: This method can distinguish between tumorous and healthy tissue among various parenchyma tissue patterns, making a decision whether a mammogram is normal or not, and then detecting the masses' position by performing sub-image windowing analysis.
151